Add files using upload-large-folder tool

.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/prepared_sources/allen_mouse_reference_bf8cb800/obs.csv filter=lfs diff=lfs merge=lfs -text
+data/prepared_sources/human_whole_brain_whb_10xv3_nonneurons_log2/obs.csv filter=lfs diff=lfs merge=lfs -text

data/prepared_sources/allen_mouse_reference_03390dd0/source_manifest.json

@@ -0,0 +1,15 @@
+{
+  "counts_path": "counts.mtx",
+  "obs_path": "obs.csv",
+  "var_path": "var.csv",
+  "count_orientation": "cell_by_gene",
+  "cell_id_col": "cell_id",
+  "feature_id_col": "feature_id",
+  "n_obs": 50000,
+  "n_vars": 542,
+  "source_id": "allen_mouse_reference_03390dd0",
+  "species": "mouse",
+  "family_use": "gaussian",
+  "label_column": "native_label",
+  "sample_column": "sample_id"
+}

data/prepared_sources/allen_mouse_reference_03390dd0/var.csv

@@ -0,0 +1,543 @@
+n_cells,highly_variable,means,dispersions,dispersions_norm,feature_is_filtered,mean,std,feature_name,feature_reference,feature_biotype,feature_length,feature_type,feature_id
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+60994,True,0.7694460382625925,1.285806543269684,2.1167417,False,0.4901440359612483,0.7180644201047287,Cntn2,NCBITaxon:10090,gene,3561,protein_coding,CNTN2
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+2094,True,0.04701154036800263,1.4767414441284281,2.0699677,False,0.01744917496912213,0.17079094250208857,Fcgr2b,NCBITaxon:10090,gene,1462,protein_coding,FCGR2B
+4562,True,0.08710731466663642,1.4533887474347507,1.9952528,False,0.04032785115554501,0.2504128186120232,Fcer1g,NCBITaxon:10090,gene,674,protein_coding,FCER1G
+1785,True,0.042842734198565914,1.516611647944519,2.1975288,False,0.015374019486324131,0.16093028643065682,Cd48,NCBITaxon:10090,gene,1054,protein_coding,CD48
+41602,False,0.375837246861438,0.7959762896484774,-0.08761236,False,0.2880536277798098,0.5221530631928448,Ncstn,NCBITaxon:10090,gene,1804,protein_coding,NCSTN
+41454,True,0.5770571227738078,1.2203353236183316,1.960205,False,0.2296780333780902,0.5259239405572996,Pld5,NCBITaxon:10090,gene,3278,protein_coding,PLD5
+21399,False,0.19078000659275107,0.8090794169263075,-0.20150836,False,0.12439971944154471,0.366234632744579,Psen2,NCBITaxon:10090,gene,1462,protein_coding,PSEN2
+18703,True,0.22375696026372863,1.1103448130810984,1.0940226,False,0.11508391895534442,0.37687109800446067,Tgfb2,NCBITaxon:10090,gene,821,protein_coding,TGFB2
+1503,True,0.016419547737395984,1.1731824812592875,1.0987585,False,0.006640275653351567,0.09486459669025542,Cenpf,NCBITaxon:10090,gene,2014,protein_coding,CENPF
+39134,True,0.6661490461042323,1.4219058964205487,2.8448157,False,0.3083284464932521,0.6586831214733923,Prox1,NCBITaxon:10090,gene,3285,protein_coding,PROX1
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+15655,True,0.2506865513552018,1.3265691950781238,2.0238519,False,0.11902600442565488,0.4149193304490282,Fam107b,NCBITaxon:10090,gene,688,protein_coding,FAM107B
+153567,False,1.9973119459670476,0.4641528528664849,-0.30191222,False,1.538905693191809,0.5794851542298246,Frmd4a,NCBITaxon:10090,gene,2025,protein_coding,FRMD4A
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+52813,True,0.9280833896816039,1.840282792329922,4.018429,False,0.3637738057866967,0.7142771527444024,Ptgds,NCBITaxon:10090,gene,830,protein_coding,PTGDS
+15332,True,0.25537810664338956,1.2490813495963595,1.690631,False,0.12706521181394248,0.4262944623174911,Notch1,NCBITaxon:10090,gene,406,protein_coding,NOTCH1
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+168494,False,2.360592264008168,0.1888578610224333,0.033956844,False,1.83416952883814,0.32103799923655857,Lrp1b,NCBITaxon:10090,gene,2571,protein_coding,LRP1B
+26342,True,0.32618306610311804,1.1146867250854182,1.3995599,False,0.16384035161100957,0.44965997967802224,Lypd6b,NCBITaxon:10090,gene,565,protein_coding,LYPD6B
+28818,True,0.44755721873936405,1.315177969215696,2.469792,False,0.2543139655871041,0.5820597275405662,Lypd6,NCBITaxon:10090,gene,3495,protein_coding,LYPD6
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+79397,True,0.9682865438522616,1.053714496458105,1.2296146,False,0.5689030236986113,0.7012957781933647,Cdh4,NCBITaxon:10090,gene,587,protein_coding,CDH4
+11424,False,0.10195050099374968,0.8166113848462615,-0.04205822,False,0.08685407726295455,0.3015878944436523,Col20a1,NCBITaxon:10090,gene,3867,protein_coding,COL20A1
+36227,False,0.317360353459326,0.7217393224621989,-0.4340182,False,0.23002136809302745,0.46565266745657224,Zbtb46,NCBITaxon:10090,gene,2577,protein_coding,ZBTB46
+86824,False,0.853145688333413,0.6341378265332954,-0.42908996,False,0.5925279297509295,0.6549324857765623,Stmn2,NCBITaxon:10090,gene,1357,protein_coding,STMN2
+50203,True,0.9478019411475893,1.6889666177545193,3.4819305,False,0.48951941398348653,0.7966175596702836,Car2,NCBITaxon:10090,gene,881,protein_coding,CAR2
+167471,False,2.290082467628336,-0.10989157687940655,-0.82835567,False,1.823020557815143,0.2933719184087683,Nlgn1,NCBITaxon:10090,gene,4272,protein_coding,NLGN1
+45365,True,0.9079246342267117,1.6616833095350507,3.3851962,False,0.442962807587574,0.7772843516641843,Cldn11,NCBITaxon:10090,gene,1870,protein_coding,CLDN11
+28538,True,0.48090450052839684,1.532364893789437,3.48702,False,0.16639470226823005,0.47692401185723204,Kcnmb2,NCBITaxon:10090,gene,2357,protein_coding,KCNMB2
+129580,True,1.7225812886315888,1.3292517604624239,1.6084052,False,1.347969843597535,0.7082592844108689,Pex5l,NCBITaxon:10090,gene,3136,protein_coding,PEX5L
+40701,True,0.571587573294494,1.1864194220017676,1.8667316,False,0.3051620448412403,0.6304565812098286,Sox2,NCBITaxon:10090,gene,2057,protein_coding,SOX2
+16388,False,0.16486993530266159,0.9364898538863623,0.34639448,False,0.10471198158595033,0.3366261726533109,Anxa5,NCBITaxon:10090,gene,567,protein_coding,ANXA5
+2393,False,0.02593852201265367,1.0329349726767263,0.6500494,False,0.017232318207727627,0.14226573747519802,Postn,NCBITaxon:10090,gene,2670,protein_coding,POSTN
+785,False,0.0052265056962404795,0.43813568914967876,-1.2529567,False,0.0038898307043812937,0.061776269242893984,Ccna1,NCBITaxon:10090,gene,1218,protein_coding,CCNA1
+141096,False,1.7516538245140696,0.4994505276589723,0.14541732,False,1.4574168247969899,0.6196009915683897,Dclk1,NCBITaxon:10090,gene,2922,protein_coding,DCLK1
+1799,True,0.03684260505800967,1.430717833253229,1.9227194,False,0.01776280763322329,0.170191185767199,P2ry13,NCBITaxon:10090,gene,2528,protein_coding,P2RY13
+29205,True,0.37491736106796064,1.5503003661749952,3.432228,False,0.19123088949820513,0.49217073997626215,P2ry12,NCBITaxon:10090,gene,2157,protein_coding,P2RY12
+24499,False,0.2801290353648528,1.0171369969176394,0.69320124,False,0.10905523880573063,0.3599192841273292,Plch1,NCBITaxon:10090,gene,5554,protein_coding,PLCH1
+15166,False,0.14508652473522146,0.7825387416048388,-0.31564116,False,0.058641383073531846,0.24571085402404436,Zbbx,NCBITaxon:10090,gene,1978,protein_coding,ZBBX
+10538,False,0.1362022219808098,1.1422465835551598,0.9997819,False,0.1158592319068651,0.36783843970416086,Rxfp1,NCBITaxon:10090,gene,3433,protein_coding,RXFP1
+1755,False,0.014087631623175145,0.6406233433502703,-0.6051159,False,0.002535920744987188,0.05360586576808846,Sfrp2,NCBITaxon:10090,gene,600,protein_coding,SFRP2
+1133,True,0.019658897194357504,1.3881427674758349,1.7865043,False,0.010376623662731004,0.12839935099275396,Nes,NCBITaxon:10090,gene,5958,protein_coding,NES
+36321,True,0.5589118924165256,1.3690805440177836,2.7222528,False,0.2621013226021525,0.5825313806652038,Bcan,NCBITaxon:10090,gene,1020,protein_coding,BCAN
+22649,True,0.4670396901577408,1.5113819327545477,3.3887432,False,0.22872744833779488,0.58729526200579,Hapln2,NCBITaxon:10090,gene,1184,protein_coding,HAPLN2
+24874,False,0.23609239120451272,0.8339140175338671,-0.094712175,False,0.1497957233613987,0.40176641073846064,Thbs3,NCBITaxon:10090,gene,745,protein_coding,THBS3
+580,True,0.00877057505345782,1.179432608298163,1.1187552,False,0.0035075283503031734,0.07129491890112435,S100a4,NCBITaxon:10090,gene,417,protein_coding,S100A4
+7615,False,0.068249419782229,0.8872672978367182,0.18399893,False,0.04178524125444886,0.21375853987290963,S100a10,NCBITaxon:10090,gene,611,protein_coding,S100A10
+7305,True,0.20257925901835597,1.811537389889878,4.1093597,False,0.08038310852639982,0.37065298272480673,Ctss,NCBITaxon:10090,gene,1355,protein_coding,CTSS
+152,False,0.0017029733003513406,1.0367898903477861,0.66238284,False,0.0013789797903000557,0.040895220560368116,Cd2,NCBITaxon:10090,gene,968,protein_coding,CD2
+4086,True,0.06862861871769081,1.3599060658091489,1.6961635,False,0.03346966631596526,0.22523509652153156,Olfml3,NCBITaxon:10090,gene,3981,protein_coding,OLFML3
+528,True,0.010671339609460864,1.3835650483512962,1.7718583,False,0.005661025775962239,0.09604690105321705,Tmigd3,NCBITaxon:10090,gene,792,protein_coding,TMIGD3
+61708,True,0.8487238096437806,1.303327569151219,2.18519,False,0.3939748836938501,0.6286725906948509,Ntng1,NCBITaxon:10090,gene,1620,protein_coding,NTNG1
+27004,True,0.3255126770544073,1.0913416529261215,1.2906268,False,0.13836679086237832,0.4042322340865178,Col11a1,NCBITaxon:10090,gene,2112,protein_coding,COL11A1
+26213,False,0.26282379137643547,0.9258502801234717,0.30064115,False,0.21452049701141143,0.46351198871008387,Palmd,NCBITaxon:10090,gene,2869,protein_coding,PALMD
+31295,True,0.46525790097593145,1.374763569636848,2.7488701,False,0.14523590567891057,0.4405423533312401,Ndst4,NCBITaxon:10090,gene,3019,protein_coding,NDST4
+64525,True,0.8385786723542965,1.2628486916054824,2.0270536,False,0.4663581345851558,0.653795542926733,Col25a1,NCBITaxon:10090,gene,2723,protein_coding,COL25A1
+10534,True,0.24941824300172077,1.6310237234335219,3.3330972,False,0.055658162267135644,0.2931534240338011,Etnppl,NCBITaxon:10090,gene,1597,protein_coding,ETNPPL
+9172,False,0.10033613923357038,1.1090562387906413,0.8935924,False,0.06738191100771071,0.2842673912602126,Npnt,NCBITaxon:10090,gene,3383,protein_coding,NPNT
+3692,True,0.062157150828633,1.3155634581090736,1.5542934,False,0.02672053721080155,0.20316558999573647,Gbp7,NCBITaxon:10090,gene,4055,protein_coding,GBP7
+94865,True,1.2597262651197019,1.1115934487534898,1.4669844,False,0.9099486273385816,0.7889708084920006,Tox,NCBITaxon:10090,gene,2286,protein_coding,TOX
+36012,False,0.38896604087938097,0.9065177666210226,0.42819825,False,0.21206042893745378,0.4737095211921499,Calb1,NCBITaxon:10090,gene,736,protein_coding,CALB1
+65481,True,0.8800948102609442,1.203534781438993,1.7608094,False,0.3337041658355426,0.6209198681868481,Grin3a,NCBITaxon:10090,gene,5678,protein_coding,GRIN3A
+2176,True,0.026676458635715248,1.247302039098758,1.3358972,False,0.010595011792222485,0.11825405202328351,Klf4,NCBITaxon:10090,gene,859,protein_coding,KLF4
+123968,False,1.5341692697742482,0.821396365715892,0.77438456,False,1.06702075285651,0.7482370856907542,Astn2,NCBITaxon:10090,gene,4817,protein_coding,ASTN2
+57411,True,0.9396610175476188,1.5337154399284791,2.9314802,False,0.551128749175284,0.7852060032472348,Adamtsl1,NCBITaxon:10090,gene,1359,protein_coding,ADAMTSL1
+142251,True,1.980716344541234,1.42519499594851,1.9373084,False,1.540147506457645,0.644069192430177,Slc24a2,NCBITaxon:10090,gene,2171,protein_coding,SLC24A2
+113183,True,1.604361235411361,1.305260229734036,1.5558454,False,1.2114765120068551,0.7934704318614616,Nfia,NCBITaxon:10090,gene,9076,protein_coding,NFIA
+50747,True,0.6362128596683894,1.1320792884450703,1.5728853,False,0.2675234918937502,0.571239546563305,Cachd1,NCBITaxon:10090,gene,2387,protein_coding,CACHD1
+159359,True,2.1829999246136134,1.2125243134692343,2.142053,False,1.6802137702505584,0.5177102050763991,Pde4b,NCBITaxon:10090,gene,2129,protein_coding,PDE4B
+3528,False,0.032311251871464344,0.8145003461216053,-0.048812296,False,0.017392442875883346,0.14099238144304854,Mycl,NCBITaxon:10090,gene,728,protein_coding,MYCL
+72400,True,0.9361959662859972,1.0758194159581427,1.3079886,False,0.6552798841552862,0.7491765737476628,Grik3,NCBITaxon:10090,gene,8973,protein_coding,GRIK3
+1899,True,0.028139262584531134,1.2906192624697719,1.4744867,False,0.01369829562536729,0.13694367390003825,Cd52,NCBITaxon:10090,gene,506,protein_coding,CD52
+43105,True,0.7539841497870186,1.4542939851186771,2.7749605,False,0.3764185146166375,0.7110095099735857,Clic4,NCBITaxon:10090,gene,3606,protein_coding,CLIC4
+6777,True,0.12580479149194077,1.3948041956984556,1.807817,False,0.04874166878259697,0.2711481115730146,Id3,NCBITaxon:10090,gene,738,protein_coding,ID3
+88673,False,0.9563648196536914,0.9632372958826093,0.9088235,False,0.693714199327783,0.7367498051378435,Kazn,NCBITaxon:10090,gene,2261,protein_coding,KAZN
+126304,True,1.7238626184846573,1.255256854329222,1.4462998,False,1.4546351334632601,0.6641679268053142,Grm3,NCBITaxon:10090,gene,3049,protein_coding,GRM3
+37735,True,0.5256089868469893,1.2227001796248371,2.036658,False,0.4071730531186585,0.6460199856826915,Sema3e,NCBITaxon:10090,gene,5206,protein_coding,SEMA3E
+1534,False,0.01718867028843095,1.0987547591382536,0.8606338,False,0.002885231427280757,0.06260794982283838,Cd36,NCBITaxon:10090,gene,2692,protein_coding,CD36
+37638,True,0.5937087460477176,1.444768416922746,2.94515,False,0.33846484543543476,0.6235710312811602,Reln,NCBITaxon:10090,gene,2555,protein_coding,RELN
+14416,True,0.28101474168865737,1.4115190993587725,2.3891618,False,0.10444600697835135,0.3929704013192317,Fgfr3,NCBITaxon:10090,gene,968,protein_coding,FGFR3
+21617,False,0.2560973115237113,1.074587864430505,0.9402571,False,0.15056264082508938,0.4280926617677473,Rgs12,NCBITaxon:10090,gene,795,protein_coding,RGS12
+11662,False,0.11478880684988195,0.8511129219074095,0.0683263,False,0.017672818756328566,0.14646395243231045,Stk32b,NCBITaxon:10090,gene,3458,protein_coding,STK32B
+36503,True,0.5511490885274382,1.3721916426202085,2.7368243,False,0.29369410794612083,0.6224562341258023,Qdpr,NCBITaxon:10090,gene,1189,protein_coding,QDPR
+165721,False,2.250631542311239,0.5580858430098871,0.6720542,False,1.859690178059681,0.21517440405847754,Kcnip4,NCBITaxon:10090,gene,2164,protein_coding,KCNIP4
+54206,True,0.6773933462292383,1.1246485563315778,1.5402749,False,0.5479451600723918,0.6871863740819406,Nwd2,NCBITaxon:10090,gene,2825,protein_coding,NWD2
+9897,True,0.2649433015678946,1.7822490543377483,3.983411,False,0.09104762228222937,0.38200349066601264,Pdgfra,NCBITaxon:10090,gene,1561,protein_coding,PDGFRA
+14450,True,0.20104744498584784,1.2529774774982716,1.7073855,False,0.09923719587089867,0.3610809736404092,Kit,NCBITaxon:10090,gene,1973,protein_coding,KIT
+6140,True,0.09289991783729007,1.2462796457809877,1.3326262,False,0.0555125401055274,0.2857311060890778,Hopx,NCBITaxon:10090,gene,1217,protein_coding,HOPX
+3433,False,0.03717562729161984,0.977152686546119,0.47157905,False,0.010666832198970029,0.11834780147503793,Npffr2,NCBITaxon:10090,gene,1643,protein_coding,NPFFR2
+50688,True,0.5989730326359455,1.0673211118352697,1.2886881,False,0.4594284258499961,0.6579129132507984,Adamts3,NCBITaxon:10090,gene,2883,protein_coding,ADAMTS3
+304,True,0.00709089899884814,1.614076988394031,2.5093603,False,0.0016246641334776363,0.051767282358858184,Pf4,NCBITaxon:10090,gene,411,protein_coding,PF4
+157,True,0.0026994453551753587,1.4158980571814686,1.8753048,False,0.0009514235297411392,0.03843727930558855,Plac8,NCBITaxon:10090,gene,741,protein_coding,PLAC8
+55945,True,0.8091977947294936,1.3313585389074656,2.2946966,False,0.2690667864666337,0.6027450908215211,Arhgap24,NCBITaxon:10090,gene,3071,protein_coding,ARHGAP24
+14,False,0.00015699348550935206,0.8645365594276475,0.111274004,False,0.00010306088693103002,0.01118549410734005,Mepe,NCBITaxon:10090,gene,1682,protein_coding,MEPE
+3291,True,0.07950874012370006,1.5713537877819903,2.3726714,False,0.03958104814924598,0.2583946004711633,Tmem119,NCBITaxon:10090,gene,1289,protein_coding,TMEM119
+7731,True,0.207638548094009,1.8536096640746775,4.290283,False,0.08510731617904939,0.3847801296171233,Selplg,NCBITaxon:10090,gene,553,protein_coding,SELPLG
+19929,False,0.194394875221776,0.8749753055896518,0.0818636,False,0.07141281005549643,0.27436287147084504,Nos1,NCBITaxon:10090,gene,4388,protein_coding,NOS1
+70528,False,0.7027456861719339,0.7237010626851896,-0.21931978,False,0.5754582975119835,0.6617513933673841,Rph3a,NCBITaxon:10090,gene,3358,protein_coding,RPH3A
+72067,False,0.8295610758576825,0.9508164240647178,0.8080576,False,0.5029818762352323,0.7099452337335478,Cux2,NCBITaxon:10090,gene,740,protein_coding,CUX2
+20781,True,0.28888370935712665,1.187369354435053,1.7387129,False,0.07609416731948097,0.3209363815531889,Tmem132c,NCBITaxon:10090,gene,2754,protein_coding,TMEM132C
+102415,True,1.3534986623622371,1.0690825706905285,1.2848676,False,1.0486518701030785,0.7726472073772934,Tmem132d,NCBITaxon:10090,gene,748,protein_coding,TMEM132D
+89781,True,1.180750261180096,1.1579131439739034,1.6052138,False,0.7756252994696504,0.7686980177832716,Sdk1,NCBITaxon:10090,gene,4369,protein_coding,SDK1
+23776,False,0.1984375615186664,0.6133739167683271,-1.0431004,False,0.09279469497093068,0.305043957126803,Flt3,NCBITaxon:10090,gene,3804,protein_coding,FLT3
+3118,True,0.09383598536725322,2.14804877890293,4.2177544,False,0.024241639152177644,0.2033657118595452,Flt1,NCBITaxon:10090,gene,2039,protein_coding,FLT1
+14426,True,0.2770329220900333,1.3775504044409237,2.2430863,False,0.10965146609646222,0.414617702523813,Gng11,NCBITaxon:10090,gene,919,protein_coding,GNG11
+19903,True,0.2768685351526028,1.1875831890196504,1.4261707,False,0.15557826473469705,0.43733208104170945,Tac1,NCBITaxon:10090,gene,980,protein_coding,TAC1
+60553,True,0.942220468446842,1.4529343281823959,2.645067,False,0.6447608372075702,0.77013706153427,Foxp2,NCBITaxon:10090,gene,1575,protein_coding,FOXP2
+4190,True,0.06588725426077031,1.2991253191894485,1.5017011,False,0.031744383489392645,0.21021926432301868,Cav1,NCBITaxon:10090,gene,472,protein_coding,CAV1
+80235,True,1.1967453189153634,1.5856694260501432,2.8817432,False,0.49750061397092094,0.7612240032352346,Ptprz1,NCBITaxon:10090,gene,4089,protein_coding,PTPRZ1
+88227,True,1.2349791274946553,1.244690767162286,1.8641794,False,0.898627845317633,0.7876585480318464,Grm8,NCBITaxon:10090,gene,2830,protein_coding,GRM8
+8104,False,0.06250497845098972,0.5336723416079784,-0.9472959,False,0.024470732824131466,0.1642061005626268,Prrt4,NCBITaxon:10090,gene,3411,protein_coding,PRRT4
+2780,False,0.025599396896144988,0.833681170360912,0.012555002,False,0.013361847059349067,0.12520122853390012,Hilpda,NCBITaxon:10090,gene,820,protein_coding,HILPDA
+1709,False,0.012939488342764469,0.5563560038222491,-0.8747216,False,0.019002282552290445,0.13763867510303654,Trbc1,NCBITaxon:10090,gene,737,TR_C_gene,TRBC1
+8464,False,0.10665849526209413,1.0408368108971784,0.6753306,False,0.031528355995471845,0.21193571569535619,Tmem176a,NCBITaxon:10090,gene,1086,protein_coding,TMEM176A
+1705,True,0.024438175346528045,1.3097279682850531,1.5356233,False,0.014725884034709142,0.13691068790823402,Gpnmb,NCBITaxon:10090,gene,1832,protein_coding,GPNMB
+11161,True,0.15208954508391534,1.3209415913685143,1.9996516,False,0.10628765442732077,0.36668022827183067,Npy,NCBITaxon:10090,gene,566,protein_coding,NPY
+12541,True,0.22104745477937207,1.289295475782892,1.8635638,False,0.07090992263219134,0.3182654278356613,Tril,NCBITaxon:10090,gene,5363,protein_coding,TRIL
+163673,False,2.1783897765457296,0.11706211656819085,-0.31857267,False,1.6928884805543896,0.45107214761047054,Grid2,NCBITaxon:10090,gene,4125,protein_coding,GRID2
+5737,True,0.08872503565719905,1.4037317369027666,1.8363798,False,0.04352345621126318,0.24390239279326723,Ndnf,NCBITaxon:10090,gene,5314,protein_coding,NDNF
+4172,False,0.050228853266238785,1.062550954583261,0.744803,False,0.020716344010486214,0.1635455444451635,Smyd1,NCBITaxon:10090,gene,1505,protein_coding,SMYD1
+39,True,0.0004942662281864064,1.3177919675780287,1.5614233,False,0.0002214892648819504,0.017005670323522074,Cd8a,NCBITaxon:10090,gene,2975,protein_coding,CD8A
+775,True,0.01335161025290719,1.2814848575259579,1.4452621,False,0.004380547191711042,0.08317860100343274,Capg,NCBITaxon:10090,gene,679,protein_coding,CAPG
+13994,False,0.16062068508084448,1.0219178976111778,0.71376055,False,0.040352822984106015,0.21609792998999494,Tacr1,NCBITaxon:10090,gene,4933,protein_coding,TACR1
+15194,True,0.24676646297689217,1.2926270517541392,1.8778906,False,0.123840455089138,0.40919722106124456,Aldh1l1,NCBITaxon:10090,gene,653,protein_coding,ALDH1L1
+32030,False,0.24525586764212676,0.4939300246985455,-1.5567447,False,0.1526663476001045,0.37616454943277655,Grip2,NCBITaxon:10090,gene,3280,protein_coding,GRIP2
+75191,True,1.0729398925137792,1.4430528609648212,2.5820298,False,0.7372979576779478,0.7780458512434599,Tafa1,NCBITaxon:10090,gene,1547,protein_coding,TAFA1
+151264,False,2.013382331692105,0.6305207723646373,0.08572373,False,1.6213575668456883,0.523117213714302,Grm7,NCBITaxon:10090,gene,3127,protein_coding,GRM7
+86954,False,0.9864105466510688,0.9264606444470824,0.77843016,False,0.5768923230981444,0.7445751014780804,Slc6a1,NCBITaxon:10090,gene,2007,protein_coding,SLC6A1
+48674,False,0.427976894293231,0.7187227138269136,-0.44809437,False,0.29671119190440615,0.5230637440129953,Zfand4,NCBITaxon:10090,gene,1163,protein_coding,ZFAND4
+34352,True,0.8107943447909197,1.750683212610888,3.9328449,False,0.3768266030793623,0.7439461153905208,Ninj2,NCBITaxon:10090,gene,838,protein_coding,NINJ2
+1182,True,0.019847124887422497,1.562476282076835,2.3442686,False,0.010176569796129848,0.11210170169665538,Cd163,NCBITaxon:10090,gene,4404,protein_coding,CD163
+1876,False,0.01812280581735684,0.7381273479443544,-0.29316074,False,0.02067710293755316,0.15401994533819927,Cd4,NCBITaxon:10090,gene,946,protein_coding,CD4
+3306,True,0.05660188734823574,1.216484163091335,1.2372983,False,0.021881471562541478,0.1823154430989654,Tnfrsf1a,NCBITaxon:10090,gene,692,protein_coding,TNFRSF1A
+75,False,0.000615472974958688,0.6651432019834629,-0.5266669,False,0.00011453191293167715,0.011475491498415034,Klrb1,NCBITaxon:10090,gene,797,protein_coding,KLRB1
+24,True,0.00048307908416341606,1.5314851521241202,2.2451153,False,0.0001364120510296729,0.01515165775315932,Cd69,NCBITaxon:10090,gene,700,protein_coding,CD69
+219,False,0.0021845910731392287,1.060444989168463,0.7380652,False,0.0013665628537672396,0.039379710302694354,Clec9a,NCBITaxon:10090,gene,867,protein_coding,CLEC9A
+4094,True,0.0681878670595459,1.1998437554043329,1.1840588,False,0.017103899786207678,0.15794999865789777,Mgst1,NCBITaxon:10090,gene,827,protein_coding,MGST1
+998,True,0.023087514481312684,1.8690323455503515,3.3250666,False,0.0063168279282252195,0.10271226198738487,Abcc9,NCBITaxon:10090,gene,6759,protein_coding,ABCC9
+41890,True,0.7335158912784804,1.5123241713087343,3.0016632,False,0.3467129837385236,0.6809978606270568,Itpr2,NCBITaxon:10090,gene,1504,protein_coding,ITPR2
+94650,False,0.9295729618185762,0.8118188204139787,0.37196228,False,0.7918679102160509,0.683710152164005,Ttyh1,NCBITaxon:10090,gene,725,protein_coding,TTYH1
+10611,True,0.17151043217562928,1.2538052503636172,1.7109452,False,0.0877785918584439,0.3471801795766442,Hif3a,NCBITaxon:10090,gene,1333,protein_coding,HIF3A
+3204,False,0.0277215409068448,0.7915149102433785,-0.1223521,False,0.029098220371270182,0.1781536282654598,Fosb,NCBITaxon:10090,gene,3629,protein_coding,FOSB
+71095,True,1.0725794615354114,1.5972779256909198,3.0838063,False,0.5469765768953503,0.7815546274282074,Apoe,NCBITaxon:10090,gene,817,protein_coding,APOE
+4332,True,0.096487853032053,1.5469255009632927,2.2945151,False,0.04503532204809407,0.2746447178299934,Tgfb1,NCBITaxon:10090,gene,700,protein_coding,TGFB1
+30656,False,0.296954513284653,0.8784208207635675,0.29709178,False,0.16124375811247682,0.42313535225608423,Psenen,NCBITaxon:10090,gene,488,protein_coding,PSENEN
+362,True,0.0072232857714730455,1.3582976345329059,1.6910175,False,0.003038089099120634,0.06988191193826249,Cd22,NCBITaxon:10090,gene,1263,protein_coding,CD22
+49940,True,1.040929122320201,1.7602472770711117,3.6140327,False,0.4946092945599231,0.8214677695012818,Mag,NCBITaxon:10090,gene,1916,protein_coding,MAG
+54,True,0.0010556911448353981,1.612814103892109,2.5053198,False,0.00022307607254836012,0.01850436846070803,Nkg7,NCBITaxon:10090,gene,686,protein_coding,NKG7
+434,True,0.0069191379376582025,1.289182871025225,1.4698912,False,0.003660310337778623,0.0733650211998038,Klk8,NCBITaxon:10090,gene,745,protein_coding,KLK8
+2342,True,0.04748228750326341,1.3719320975091809,1.7346398,False,0.015736951500243596,0.15849133888723604,Klk6,NCBITaxon:10090,gene,1133,protein_coding,KLK6
+44849,True,0.586993268892454,1.056284547919981,1.2402531,False,0.6752690026356297,0.711325531439434,Slc17a7,NCBITaxon:10090,gene,2722,protein_coding,SLC17A7
+148,False,0.0014232427686925312,0.8284393741663186,-0.0042156465,False,0.0005399563709151631,0.026507824423258244,Slc6a5,NCBITaxon:10090,gene,4716,protein_coding,SLC6A5
+24548,False,0.29722111663297485,0.9911771067321621,0.8232371,False,0.043119079781597326,0.21601976167640366,Slc17a6,NCBITaxon:10090,gene,1686,protein_coding,SLC17A6
+104166,True,1.4983551726884312,1.4401624522145855,2.2017558,False,0.8397304271881654,0.8421849839535169,Luzp2,NCBITaxon:10090,gene,3408,protein_coding,LUZP2
+6196,True,0.20931698654869396,1.9548119794828276,4.725483,False,0.08365420912580615,0.3870192185692095,Siglech,NCBITaxon:10090,gene,1794,protein_coding,SIGLECH
+38205,True,0.5897243136273743,1.2717306021957766,2.1857579,False,0.3232232315515444,0.6543273305549187,Pcsk6,NCBITaxon:10090,gene,2207,protein_coding,PCSK6
+8284,True,0.19097090219951998,1.6240502676395838,3.3031092,False,0.07822252396056328,0.3589917410256419,Lrrk1,NCBITaxon:10090,gene,1744,protein_coding,LRRK1
+25081,False,0.28323410355684553,1.0010193612088736,0.6238906,False,0.033392541225266525,0.21081358322675534,Nr2f2,NCBITaxon:10090,gene,1914,protein_coding,NR2F2
+10227,False,0.10983257313654782,0.9661066593146284,0.4362383,False,0.06721574435496888,0.270116750296632,Mctp2,NCBITaxon:10090,gene,2834,protein_coding,MCTP2
+109113,False,1.155557717154751,0.7914167530275947,0.5114988,False,0.8705549484532722,0.7185390814508508,Slco3a1,NCBITaxon:10090,gene,771,protein_coding,SLCO3A1
+32143,False,0.30216917161503704,0.8448081466253603,0.14024772,False,0.16449314763753772,0.43161974973993184,Idh2,NCBITaxon:10090,gene,891,protein_coding,IDH2
+31300,True,0.34370953805579235,1.0991830665194462,1.3272165,False,0.2653176071050768,0.5178095432531656,Cemip,NCBITaxon:10090,gene,4287,protein_coding,CEMIP
+3121,True,0.05745591487252964,1.2860764026034734,1.4599522,False,0.021303952628345446,0.18310087614588216,Trim34a,NCBITaxon:10090,gene,2578,protein_coding,TRIM34A
+292,True,0.006059837323349279,1.5796874883613814,2.3993342,False,0.0019314231842871116,0.05376078916650246,Lyve1,NCBITaxon:10090,gene,1847,protein_coding,LYVE1
+65398,True,0.8699630662418355,1.238277293775956,1.8839905,False,0.5174391302970025,0.6964121219797207,Spon1,NCBITaxon:10090,gene,5681,protein_coding,SPON1
+50673,False,0.4706724316703357,0.8763131650251654,0.41430175,False,0.3998827413484825,0.5803407003574924,Rras2,NCBITaxon:10090,gene,2904,protein_coding,RRAS2
+49494,True,1.0091417087531827,1.7941674811667827,3.854925,False,0.43670173677406415,0.7465471195109373,Sox6,NCBITaxon:10090,gene,2844,protein_coding,SOX6
+22809,False,0.2391476468054109,0.9219393865318821,0.28382313,False,0.25813301879730854,0.5002795988447726,Crym,NCBITaxon:10090,gene,2032,protein_coding,CRYM
+38897,True,0.5411950139114607,1.2377436774363926,2.1071167,False,0.4995536430404842,0.6806295602566598,Hs3st2,NCBITaxon:10090,gene,1713,protein_coding,HS3ST2
+87205,True,1.2256374617406267,1.369940093262641,2.237954,False,0.9259597094480331,0.7898990304952781,Hs3st4,NCBITaxon:10090,gene,3189,protein_coding,HS3ST4
+1691,False,0.014351435455782977,0.750945228971554,-0.2521511,False,0.008491497403735868,0.09670762025629989,Lat,NCBITaxon:10090,gene,924,protein_coding,LAT
+22,True,0.000276782332792851,1.4473654952659836,1.9759821,False,0.00010405987654893822,0.011368311317959663,Cd19,NCBITaxon:10090,gene,2432,protein_coding,CD19
+2683,False,0.035562019037607286,1.1381943900032045,0.9868173,False,0.015124090400168225,0.14416294826920834,Sult1a1,NCBITaxon:10090,gene,1289,protein_coding,SULT1A1
+23768,False,0.19281901714127522,0.719263492110488,-0.5877436,False,0.18792259758461197,0.4193704036600262,Coro1a,NCBITaxon:10090,gene,781,protein_coding,CORO1A
+5336,True,0.15240506649035185,1.7529396399953738,3.8573718,False,0.06645397424207075,0.3388756533668233,Itgam,NCBITaxon:10090,gene,3513,protein_coding,ITGAM
+344,True,0.007857829505573764,1.4702521150938195,2.0492058,False,0.0020831418797258366,0.0584031502155315,Itgax,NCBITaxon:10090,gene,879,protein_coding,ITGAX
+20894,True,0.2616378029729754,1.2664430650008471,1.7652916,False,0.11566839744613551,0.3995489332955866,Rgs10,NCBITaxon:10090,gene,463,protein_coding,RGS10
+149197,False,1.5588975322231737,0.7653209227121935,0.64502954,False,1.252563148742565,0.6464241280002992,Fgfr2,NCBITaxon:10090,gene,2209,protein_coding,FGFR2
+2606,False,0.02015294414722847,0.8702175106678491,0.1294497,False,0.01782787087970185,0.1388979942924114,Mki67,NCBITaxon:10090,gene,5318,protein_coding,MKI67
+28926,True,0.7802143374452329,1.8861190205257543,4.461943,False,0.34562187940856776,0.7370718363555374,C030029H02Rik,NCBITaxon:10090,gene,1674,lncRNA,C030029H02RIK
+2710,True,0.050500916086902076,1.451461482263943,1.9890867,False,0.014755477403040318,0.15071892114291946,Ifitm3,NCBITaxon:10090,gene,678,protein_coding,IFITM3
+707,True,0.01010786463391258,1.2213973881824565,1.2530177,False,0.00392383584591097,0.07591516068407501,Irf7,NCBITaxon:10090,gene,813,protein_coding,IRF7
+1944,False,0.016832221330864705,0.7613641589557766,-0.21881668,False,0.002859198598334861,0.058099402140471594,Th,NCBITaxon:10090,gene,1146,protein_coding,TH
+19049,False,0.17799636982579567,0.8335243501807884,-0.09638786,False,0.11631509423867925,0.3556674953620753,Tssc4,NCBITaxon:10090,gene,1036,protein_coding,TSSC4
+1979,True,0.026942916183375586,1.1627170636667252,1.0652753,False,0.012300229185395188,0.1298440147510063,Cdkn1c,NCBITaxon:10090,gene,667,protein_coding,CDKN1C
+19946,False,0.1819529946755712,0.8155260529563456,-0.17378591,False,0.15239383884295477,0.3948268543572778,Stxbp2,NCBITaxon:10090,gene,605,protein_coding,STXBP2
+85,True,0.0017111316479694692,1.5993037781270811,2.4620948,False,0.0009594120597082804,0.03896627532670085,Cd209a,NCBITaxon:10090,gene,867,protein_coding,CD209A
+70881,False,0.7858729490891175,0.8983229232106262,0.6029847,False,0.5540681716452098,0.678075884170446,Myo16,NCBITaxon:10090,gene,6719,protein_coding,MYO16
+156228,False,2.1959044787562654,0.6678897501461452,0.9186955,False,1.8185902945760435,0.31979684055135643,Csmd1,NCBITaxon:10090,gene,2911,protein_coding,CSMD1
+65174,False,0.6382406163625463,0.7038198090692229,-0.30657047,False,0.32477312629494937,0.5485863494622769,Ank1,NCBITaxon:10090,gene,6257,protein_coding,ANK1
+77540,True,0.9595220938779991,1.0830860367137514,1.3337528,False,0.6420745337492899,0.7177347738926909,Zmat4,NCBITaxon:10090,gene,2272,protein_coding,ZMAT4
+775,False,0.006760230802825498,0.604638809863139,-0.7202451,False,0.0069802137197926195,0.08841199932716541,Ido1,NCBITaxon:10090,gene,1608,protein_coding,IDO1
+146940,False,1.9888763678523298,0.9769675394290278,0.89294195,False,1.6729324779761046,0.47145570017516675,Nrg1,NCBITaxon:10090,gene,186,protein_coding,NRG1
+96362,True,1.1323387381221228,1.0014567025496701,1.1452814,False,0.8252061743813,0.7647650317204903,Dlc1,NCBITaxon:10090,gene,3423,protein_coding,DLC1
+6293,True,0.11175381398597349,1.2514477138054132,1.3491609,False,0.045345847155989855,0.26000204370202545,Tlr3,NCBITaxon:10090,gene,707,protein_coding,TLR3
+130218,True,1.6963240972933424,1.1856508020657346,1.2938094,False,1.177523743878549,0.7418990956815288,Galntl6,NCBITaxon:10090,gene,899,protein_coding,GALNTL6
+118910,True,1.5860868295775428,1.1608585274711856,1.2394953,False,1.0118637360610068,0.8286708414419686,Spock3,NCBITaxon:10090,gene,2942,protein_coding,SPOCK3
+15701,True,0.18624446280514229,1.1222434056590205,1.1451901,False,0.11327322619969302,0.3637640170835419,Tll1,NCBITaxon:10090,gene,2712,protein_coding,TLL1
+17681,False,0.14427796467938084,0.6812639228537573,-0.75115305,False,0.12952266553437447,0.35243330790068217,Npy1r,NCBITaxon:10090,gene,2885,protein_coding,NPY1R
+1507,True,0.02559105308975239,1.2011684047562377,1.1882969,False,0.012481168348106553,0.13743355113378564,Il12rb1,NCBITaxon:10090,gene,1216,protein_coding,IL12RB1
+7735,True,0.11360336511088116,1.2074637957309966,1.2084384,False,0.07091795353714274,0.3136674465591938,Arrdc2,NCBITaxon:10090,gene,855,protein_coding,ARRDC2
+1587,True,0.026849309172362015,1.5165904513298456,2.197461,False,0.01123579833013355,0.12753037200013928,Klf2,NCBITaxon:10090,gene,1847,protein_coding,KLF2
+12528,False,0.10838031719952809,0.7792295006881774,-0.16165815,False,0.06293477437204309,0.26266055524357756,Dnajb1,NCBITaxon:10090,gene,2925,protein_coding,DNAJB1
+12423,True,0.17742415806329107,1.2088123288326555,1.5174623,False,0.060247598060762,0.28642455445586673,Mt2,NCBITaxon:10090,gene,449,protein_coding,MT2
+41176,True,0.5227023104522817,1.2256883928602411,2.050654,False,0.2346194493972157,0.528131808960091,Mt1,NCBITaxon:10090,gene,633,protein_coding,MT1
+3312,True,0.06089224722829108,1.3904116398367459,1.7937634,False,0.024680281617048987,0.19506606088134595,Nlrc5,NCBITaxon:10090,gene,1291,protein_coding,NLRC5
+29250,True,0.5218159247151856,1.3584038339778532,2.672247,False,0.26261423935630374,0.6085979154585477,Pllp,NCBITaxon:10090,gene,954,protein_coding,PLLP
+677,False,0.0049360879786985576,0.6278788069026976,-0.6458909,False,0.004290705221445417,0.06556966748747753,Ccl17,NCBITaxon:10090,gene,514,protein_coding,CCL17
+483,True,0.009321717847954663,1.523127961116324,2.218377,False,0.0012245818979882265,0.042534006112487845,Cdh1,NCBITaxon:10090,gene,2609,protein_coding,CDH1
+69923,False,0.7048388898990152,0.8076847293688036,0.1492502,False,0.6247783841308933,0.6795420263469851,Sntb2,NCBITaxon:10090,gene,3225,protein_coding,SNTB2
+1202,False,0.008585743603620516,0.5237654130184912,-0.9789922,False,0.004670616181840324,0.06703750797863409,Atp2c2,NCBITaxon:10090,gene,1922,protein_coding,ATP2C2
+5514,True,0.12869610179567914,1.6041033050725166,2.4774504,False,0.06410148847439348,0.3200197554813359,Irf8,NCBITaxon:10090,gene,1284,protein_coding,IRF8
+72118,False,0.609492145145539,0.59458647262093,-0.7859509,False,0.4587276631556071,0.5977763645885504,Tubb3,NCBITaxon:10090,gene,1322,protein_coding,TUBB3
+69676,True,0.8319149204465967,1.0372281955079306,1.1456368,False,0.5596257280201797,0.7032095295895241,Nrp1,NCBITaxon:10090,gene,3229,protein_coding,NRP1
+12367,True,0.2995443914161622,1.698447317474924,4.1235137,False,0.06177428704910272,0.3177543423458548,Pdgfd,NCBITaxon:10090,gene,2862,protein_coding,PDGFD
+71263,False,0.6145328776831284,0.6538952512827192,-0.5256689,False,0.44927740667302846,0.6041371529326593,Birc2,NCBITaxon:10090,gene,1316,protein_coding,BIRC2
+24389,False,0.2809443398983577,1.0941192063600755,1.0242476,False,0.22557861852366357,0.49319680874672495,Trpc6,NCBITaxon:10090,gene,3168,protein_coding,TRPC6
+123776,True,1.6928729861816407,1.1716111477620808,1.2630517,False,1.2722567356044128,0.7275491110424449,Cntn5,NCBITaxon:10090,gene,3297,protein_coding,CNTN5
+23830,False,0.18360259806627854,0.5903445638947742,-1.1421335,False,0.13093094660503288,0.35726682139181615,Panx1,NCBITaxon:10090,gene,1214,protein_coding,PANX1
+29221,True,0.6497668354181201,1.6606459368992337,3.892548,False,0.30705510939491987,0.6752277744176233,Anln,NCBITaxon:10090,gene,670,protein_coding,ANLN
+134338,True,1.8451857546896169,1.1215210064978087,1.4874954,False,1.267701507338916,0.7797111831058319,Kirrel3,NCBITaxon:10090,gene,3074,protein_coding,KIRREL3
+11928,True,0.22730375437801262,1.3597061830729356,2.1663508,False,0.10416289082398511,0.4006327631155014,Mcam,NCBITaxon:10090,gene,946,protein_coding,MCAM
+52,True,0.0007221272507559205,1.3383458009116806,1.6271834,False,0.0002734858814314056,0.019085007184789553,Cd3g,NCBITaxon:10090,gene,1023,protein_coding,CD3G
+8774,True,0.19151020265170365,1.4934357005353298,2.7414277,False,0.08211340080138985,0.35606568368495023,Cspg4,NCBITaxon:10090,gene,5067,protein_coding,CSPG4
+67189,True,0.8076927736521246,1.1566883131778642,1.6123239,False,0.4533929692627051,0.6780922853457577,Thsd4,NCBITaxon:10090,gene,4664,protein_coding,THSD4
+7286,False,0.05767067464961275,0.6684712199330448,-0.51601917,False,0.03814950482454192,0.20160178025755854,Ccnb2,NCBITaxon:10090,gene,1531,protein_coding,CCNB2
+16051,True,0.20739923917806197,1.1026598650388955,1.0609751,False,0.1726150482259662,0.4431755200970343,Col12a1,NCBITaxon:10090,gene,3265,protein_coding,COL12A1
+36330,False,0.3714113516054558,0.8866999819144549,0.33572415,False,0.24187748144591908,0.502162705892197,Filip1,NCBITaxon:10090,gene,3775,protein_coding,FILIP1
+5119,True,0.11933711782486278,1.5435209873360176,2.2836227,False,0.04730223226602905,0.28093792629040104,Ctsh,NCBITaxon:10090,gene,818,protein_coding,CTSH
+96564,True,1.247238569778955,1.089987974586542,1.4025084,False,0.9490469452724809,0.7706872705334146,Clstn2,NCBITaxon:10090,gene,4269,protein_coding,CLSTN2
+92254,True,0.9382285597938745,1.0166954497948748,1.0983618,False,0.7107335128650346,0.7070481749430432,Cspg5,NCBITaxon:10090,gene,3790,protein_coding,CSPG5
+17845,True,0.31324412333371393,1.7368600768260334,4.3027563,False,0.14276044846257402,0.4560064226981332,Cx3cr1,NCBITaxon:10090,gene,4123,protein_coding,CX3CR1
+55041,True,1.1724629286176471,1.9104535494679102,3.8509784,False,0.5509059661984966,0.8465529884894608,Mobp,NCBITaxon:10090,gene,569,protein_coding,MOBP
+39222,False,0.4333286997472634,0.9704779741074708,0.85533696,False,0.452578839760956,0.6306209176057058,Cck,NCBITaxon:10090,gene,584,protein_coding,CCK
+5821,False,0.08067117901813964,1.137695584182415,0.9852214,False,0.05031953783295762,0.25869691549824386,Hhatl,NCBITaxon:10090,gene,530,protein_coding,HHATL
+215,True,0.005511327722401534,1.7570112495675623,2.9666655,False,0.0020071355453229385,0.057719495200443324,Ccr2,NCBITaxon:10090,gene,3257,protein_coding,CCR2
+4233,True,0.08377535058784269,1.7335209466160375,2.8915102,False,0.048920184721381633,0.26460417084626986,Vip,NCBITaxon:10090,gene,2031,protein_coding,VIP
+20815,True,0.35963433653939075,1.5128042971831948,3.2572632,False,0.1739901160715616,0.4880821292758469,Cnksr3,NCBITaxon:10090,gene,3000,protein_coding,CNKSR3
+72775,False,0.8374652790809772,0.8897901266023104,0.5696502,False,0.49112321944956616,0.6586915943213217,Samd5,NCBITaxon:10090,gene,1315,protein_coding,SAMD5
+86622,False,0.9268151333266,0.8830229925667905,0.62442,False,0.7624044650732249,0.7205992168767309,Sgk1,NCBITaxon:10090,gene,749,protein_coding,SGK1
+19788,True,0.3862628671310683,1.481745576734599,3.1123364,False,0.20246387690674933,0.536899966941315,Eya4,NCBITaxon:10090,gene,2602,protein_coding,EYA4
+45907,True,0.9276922412259434,1.6790300044559845,3.4466996,False,0.45597595475456115,0.7686813360089275,Lama2,NCBITaxon:10090,gene,2740,protein_coding,LAMA2
+556,True,0.006415649576738433,1.2871049446789664,1.463243,False,0.003675687148781191,0.06536752077939448,Themis,NCBITaxon:10090,gene,2882,protein_coding,THEMIS
+162876,True,2.3592398788594777,0.9060291731051503,2.0309267,False,1.854504578275191,0.3434620851098576,Nkain2,NCBITaxon:10090,gene,1088,protein_coding,NKAIN2
+12670,True,0.17407265339712608,1.2324647706046536,1.6191748,False,0.11539493746792641,0.37171037708454785,Lama4,NCBITaxon:10090,gene,2592,protein_coding,LAMA4
+126909,False,1.2223061187540387,0.5757446938726222,-0.13211936,False,0.9685932041689934,0.6759599864999415,Fyn,NCBITaxon:10090,gene,2114,protein_coding,FYN
+2759,False,0.02203179990915341,0.8591545242745923,0.09405468,False,0.010116967431035536,0.10664258079326479,Cd24a,NCBITaxon:10090,gene,743,protein_coding,CD24A
+1305,False,0.011827403285508308,0.752316265404744,-0.24776459,False,0.007338105050596367,0.0888394390554991,Ros1,NCBITaxon:10090,gene,7180,protein_coding,ROS1
+19671,True,0.4245713553223084,1.5954212410128132,3.6427717,False,0.1456042822550795,0.46699237036335756,Gja1,NCBITaxon:10090,gene,1746,protein_coding,GJA1
+25769,True,0.42948336137930737,1.3341419449500358,2.4235857,False,0.21636071952578279,0.5580077352567163,Unc5b,NCBITaxon:10090,gene,5713,protein_coding,UNC5B
+127331,True,1.5575355145228347,1.2177511351597512,1.6886967,False,1.1567299007899594,0.7370633194883628,Ctnna3,NCBITaxon:10090,gene,2795,protein_coding,CTNNA3
+1878,False,0.015941161636589384,0.7282170881025752,-0.32486773,False,0.019171319831521486,0.14470807757568807,Egr2,NCBITaxon:10090,gene,902,protein_coding,EGR2
+252,True,0.0024410487955910084,1.1753643523380994,1.1057392,False,0.0012801893378368875,0.04244614195849287,Cdk1,NCBITaxon:10090,gene,695,protein_coding,CDK1
+125167,True,1.6859008873613426,1.1580618664229494,1.2333685,False,1.116302035531895,0.7857680702000803,Pcdh15,NCBITaxon:10090,gene,5256,protein_coding,PCDH15
+23968,True,0.3203958364953629,1.135715188256789,1.4976833,False,0.15733123030921045,0.4428531570563352,S100b,NCBITaxon:10090,gene,1484,protein_coding,S100B
+1427,True,0.03125176507723766,1.4219274662122119,1.8945954,False,0.013880340013677963,0.15184970939628395,Itgb2,NCBITaxon:10090,gene,2411,protein_coding,ITGB2
+61543,False,0.5864280477418531,0.9287899986719804,0.6807317,False,0.39586175848544186,0.5937143271415284,Hcn2,NCBITaxon:10090,gene,3195,protein_coding,HCN2
+5433,False,0.04528913531832514,0.8864195567242947,0.18128666,False,0.028355931758234195,0.17492684502583755,Gas2l3,NCBITaxon:10090,gene,565,protein_coding,GAS2L3
+1960,True,0.03928648604576784,1.6767599022497752,2.7099085,False,0.010243056510788731,0.12189914585742254,Dcn,NCBITaxon:10090,gene,1758,protein_coding,DCN
+1826,False,0.013723887708747997,0.6582457801102354,-0.54873455,False,0.014301038842416288,0.12037137924935695,Otogl,NCBITaxon:10090,gene,8763,protein_coding,OTOGL
+64018,True,0.814761929689304,1.1629808578220875,1.6369066,False,0.5434098060957324,0.6947188511374491,Trhde,NCBITaxon:10090,gene,4639,protein_coding,TRHDE
+934,False,0.010084084647242883,0.939765318348578,0.35196155,False,0.006673435597322357,0.09265078691529202,Tph2,NCBITaxon:10090,gene,1459,protein_coding,TPH2
+124661,False,1.581534435992838,0.9318656361229387,1.0292153,False,1.1368721239264572,0.7374999117829114,Grip1,NCBITaxon:10090,gene,3462,protein_coding,GRIP1
+980,False,0.008553369113082328,0.7535989454811907,-0.24366078,False,0.005797571018874082,0.08026352441857026,Wif1,NCBITaxon:10090,gene,2183,protein_coding,WIF1
+30809,False,0.23367253190660095,0.5466219186906359,-1.3301538,False,0.2181597297501094,0.43907302744833493,B4galnt1,NCBITaxon:10090,gene,1499,protein_coding,B4GALNT1
+92506,False,0.853835969087735,0.7078669408685442,-0.14105725,False,0.698571602597479,0.6699620269737511,Lrp1,NCBITaxon:10090,gene,772,protein_coding,LRP1
+16120,True,0.3100605854395649,1.3682784708776836,2.5828743,False,0.14827829576112334,0.47230609859071954,Erbb3,NCBITaxon:10090,gene,6016,protein_coding,ERBB3
+2352,False,0.016335919846932723,0.5220927771019204,-0.98434365,False,0.012174793914734253,0.1080318633012382,Tespa1,NCBITaxon:10090,gene,1382,protein_coding,TESPA1
+4735,False,0.04367915573672036,0.8092645518369979,-0.065563746,False,0.028014233599934074,0.17728362697793854,Igfbp3,NCBITaxon:10090,gene,2065,protein_coding,IGFBP3
+19929,True,0.4506729340524153,1.6548247494137927,4.0605793,False,0.17410801580291702,0.5097782673850819,Egfr,NCBITaxon:10090,gene,716,protein_coding,EGFR
+85207,True,1.2231145551257996,1.325366183389836,2.1049345,False,0.9688004772790635,0.7973444889915517,Slit3,NCBITaxon:10090,gene,865,protein_coding,SLIT3
+155166,False,2.0505077483952983,0.6592265856621221,0.90105623,False,1.6362919920677446,0.4959266608044107,Tenm2,NCBITaxon:10090,gene,2408,protein_coding,TENM2
+21080,False,0.2181364721455151,0.8874013638439827,0.13529935,False,0.17525300538402072,0.427382531963512,Adra1b,NCBITaxon:10090,gene,3047,protein_coding,ADRA1B
+17310,True,0.3646494363020272,1.7264241363312136,4.25406,False,0.12671068912380387,0.42679863821627234,Ebf1,NCBITaxon:10090,gene,2864,protein_coding,EBF1
+4323,True,0.058805320494309456,1.1710535002745683,1.091947,False,0.029400621659441444,0.20011412639512055,Irgm1,NCBITaxon:10090,gene,2259,protein_coding,IRGM1
+75215,True,1.000862346218396,1.1656257070084302,1.626401,False,0.7464463395669692,0.7671803203233643,Fstl4,NCBITaxon:10090,gene,4380,protein_coding,FSTL4
+1190,True,0.017598397343930722,1.2301694166806048,1.281083,False,0.007992951957381055,0.10733014608892953,Igtp,NCBITaxon:10090,gene,1832,protein_coding,IGTP
+2191,True,0.04508619807555597,1.3930500484327355,1.8022047,False,0.02024022914458113,0.18191361997761865,Irgm2,NCBITaxon:10090,gene,3470,protein_coding,IRGM2
+2949,False,0.044698962601882275,1.1017215729507626,0.87012583,False,0.02170713380857675,0.17498542643780565,Adora2b,NCBITaxon:10090,gene,1845,protein_coding,ADORA2B
+13342,True,0.2335987119642938,1.2846173333661255,1.8434464,False,0.11315173924164541,0.4114282889334046,Pmp22,NCBITaxon:10090,gene,733,protein_coding,PMP22
+2895,True,0.042053101179718645,1.2348117374062724,1.2959356,False,0.02139994859132809,0.1737186189968585,Cd68,NCBITaxon:10090,gene,1008,protein_coding,CD68
+2199,False,0.022753836346094285,1.0173895136657527,0.60031307,False,0.010598113946623664,0.1203107449156659,Itgae,NCBITaxon:10090,gene,3835,protein_coding,ITGAE
+3202,True,0.06951686621846306,1.862784018801305,3.3050756,False,0.017251002114739702,0.16391464671756936,Vtn,NCBITaxon:10090,gene,1038,protein_coding,VTN
+138605,False,1.9088602675393922,0.8741201844153842,0.65330845,False,1.4102639737048648,0.6825506763799755,Asic2,NCBITaxon:10090,gene,3068,protein_coding,ASIC2
+103,True,0.0017838842709367094,1.4112855493057663,1.8605475,False,0.0006507844642237325,0.030689987564204907,Ccl2,NCBITaxon:10090,gene,954,protein_coding,CCL2
+629,True,0.006446817063181775,1.2028738240766514,1.1937532,False,0.002833072544872504,0.058563762333709746,Ccl5,NCBITaxon:10090,gene,600,protein_coding,CCL5
+229,True,0.005795275690046671,1.8090959044684396,3.1333055,False,0.0015954805780349214,0.05189500031482947,Ccl4,NCBITaxon:10090,gene,660,protein_coding,CCL4
+716,True,0.012122271518939734,1.3357656393842665,1.6189284,False,0.0058197029446424905,0.09209678903784008,Wfdc18,NCBITaxon:10090,gene,534,protein_coding,WFDC18
+535,False,0.005118114183442055,0.898772370637597,0.22080836,False,0.00205196513409837,0.04957750556806063,Tbx21,NCBITaxon:10090,gene,2488,protein_coding,TBX21
+864,True,0.008938605967544061,1.4014052910910593,1.8289366,False,0.004809023161059064,0.08230162768573267,Top2a,NCBITaxon:10090,gene,803,protein_coding,TOP2A
+10169,False,0.1014577275965666,0.9331386207472172,0.33076003,False,0.09992116328988128,0.3254529768508778,Igfbp4,NCBITaxon:10090,gene,763,protein_coding,IGFBP4
+77,False,0.0006475693823962469,1.0678892910891153,0.76188254,False,0.0006569787834291785,0.026005977629808243,Ccr7,NCBITaxon:10090,gene,1978,protein_coding,CCR7
+63159,False,0.656850547472368,0.9614482975445497,0.8240556,False,0.4483346628004342,0.6467308342897585,Stat3,NCBITaxon:10090,gene,749,protein_coding,STAT3
+1430,True,0.022632709926790676,1.287890647647197,1.4657568,False,0.00657305207124954,0.09817836979025839,Cavin1,NCBITaxon:10090,gene,2074,protein_coding,CAVIN1
+3889,False,0.03915001389928378,0.9442441656307082,0.36629122,False,0.024842622139703868,0.17384673572632178,Brca1,NCBITaxon:10090,gene,852,protein_coding,BRCA1
+6605,True,0.11323401546011085,1.3953687687673624,1.8096232,False,0.03637338199448726,0.23367052693967766,Gfap,NCBITaxon:10090,gene,2600,protein_coding,GFAP
+4877,True,0.07149555112270572,1.459670051885073,2.0153494,False,0.02786541788569684,0.20587706520365867,Pecam1,NCBITaxon:10090,gene,2991,protein_coding,PECAM1
+11352,True,0.17470172196827893,1.2357837829955571,1.6334475,False,0.08598543842360731,0.3393674088198492,Sox9,NCBITaxon:10090,gene,4135,protein_coding,SOX9
+113615,False,1.236034270751285,0.9650200904581089,1.0295737,False,0.9338699451742846,0.7485389327063301,2610035D17Rik,NCBITaxon:10090,gene,1431,lncRNA,2610035D17RIK
+9438,True,0.13776783781323584,1.260716132381568,1.3788145,False,0.05915201790283385,0.2937776840764829,Rab37,NCBITaxon:10090,gene,2393,protein_coding,RAB37
+102069,False,1.2633470557843742,0.8736846457942338,0.75700635,False,0.9842316077974356,0.7675101856792267,Rbfox3,NCBITaxon:10090,gene,1547,protein_coding,RBFOX3
+188,True,0.003883449308583358,1.4218700418509558,1.8944117,False,0.0011119010262400316,0.042968601153665895,Cbr2,NCBITaxon:10090,gene,575,protein_coding,CBR2
+625,False,0.005725676498531526,0.9082952601493268,0.251276,False,0.0028994005841515877,0.06002027361817723,Sdc1,NCBITaxon:10090,gene,746,protein_coding,SDC1
+39812,False,0.33450781713524513,0.672107991917454,-0.6656088,False,0.18541771220462394,0.43224507464858253,Sox11,NCBITaxon:10090,gene,8311,protein_coding,SOX11
+7946,False,0.07488713839231252,0.8411965387407433,0.036599737,False,0.08792326249149399,0.3061936335235979,Slc26a4,NCBITaxon:10090,gene,1730,protein_coding,SLC26A4
+84875,True,1.1717161474699318,1.2704721255578797,1.9411173,False,0.7893468778055912,0.8156577283224221,Stxbp6,NCBITaxon:10090,gene,489,protein_coding,STXBP6
+122141,True,1.7392435613139237,1.4482910841261745,2.1924813,False,1.0108030453603574,0.8686853499333724,Npas3,NCBITaxon:10090,gene,3193,protein_coding,NPAS3
+65035,False,0.735085897468549,1.0002783469693453,1.0012873,False,0.48719476324524913,0.6643029100577101,Kcnh5,NCBITaxon:10090,gene,4058,protein_coding,KCNH5
+87902,True,1.3095409511842595,1.362798812616214,2.0619173,False,1.0318186066044335,0.7914607342074415,Rgs6,NCBITaxon:10090,gene,1420,protein_coding,RGS6
+82425,False,0.743573767839178,0.6939755603698954,-0.1953258,False,0.5936800073483204,0.6593987665388851,Psen1,NCBITaxon:10090,gene,1978,protein_coding,PSEN1
+8822,False,0.0806800726172073,0.8092996334183042,-0.0654515,False,0.06687190393913323,0.267277847059242,Fos,NCBITaxon:10090,gene,814,protein_coding,FOS
+20,False,0.0001264959398075718,0.47697031379558275,-1.1287088,False,6.86379637294376e-05,0.008029499185118432,Serpina3c,NCBITaxon:10090,gene,2213,protein_coding,SERPINA3C
+17,False,0.0001424646788293805,0.7259489766424697,-0.33212432,False,6.28516857403822e-05,0.009114454685922443,Serpina3f,NCBITaxon:10090,gene,2207,protein_coding,SERPINA3F
+126,False,0.0007225632005042592,0.49927047769610283,-1.0573615,False,0.00010953976151029449,0.009905960834487302,Serpina3i,NCBITaxon:10090,gene,1450,protein_coding,SERPINA3I
+34,False,0.00030174053741310614,0.8952512987705992,0.20954302,False,0.0002030367249247943,0.01647286750741701,Serpina3m,NCBITaxon:10090,gene,1658,protein_coding,SERPINA3M
+4404,False,0.053825230058022776,1.1335040351099215,0.97181094,False,0.02206249940162973,0.17820835166890364,Serpina3n,NCBITaxon:10090,gene,2064,protein_coding,SERPINA3N
+164329,False,2.2810979929188986,0.4872288060352314,0.51289517,False,1.758336673294109,0.40161205778212794,Meg3,NCBITaxon:10090,gene,1819,lncRNA,MEG3
+1503,True,0.023787781200723758,1.341911936137922,1.638593,False,0.0074810188059356904,0.10501588502369125,Crip1,NCBITaxon:10090,gene,534,protein_coding,CRIP1
+6707,False,0.07850588108272799,1.0783573270194482,0.79537404,False,0.0694947394825287,0.2877622780876471,Ighm,NCBITaxon:10090,gene,1595,IG_C_gene,IGHM
+58194,True,0.7157124458488998,1.094092137222205,1.4061751,False,0.40047880066480046,0.6541851934420314,Itgb8,NCBITaxon:10090,gene,3676,protein_coding,ITGB8
+33083,True,0.4460046082998508,1.1739701822334276,1.8084238,False,0.29568328920507764,0.5562825811050156,Pou6f2,NCBITaxon:10090,gene,960,protein_coding,POU6F2
+20376,False,0.20011540382355578,0.9795644833397271,0.53162825,False,0.13299171930157466,0.38897174778305926,Sox4,NCBITaxon:10090,gene,4795,protein_coding,SOX4
+34773,False,0.33255478887571893,0.7813967275159107,-0.15564378,False,0.3234415006408333,0.5425345268954841,Nrn1,NCBITaxon:10090,gene,1443,protein_coding,NRN1
+7051,True,0.238942015257383,2.004153549426225,4.9376664,False,0.0856338871754308,0.39047259668377404,Ly86,NCBITaxon:10090,gene,1909,protein_coding,LY86
+14754,False,0.13512288265915878,0.801940115547486,-0.08899761,False,0.09362453803903947,0.3145993021243507,Elovl2,NCBITaxon:10090,gene,3818,protein_coding,ELOVL2
+16200,False,0.14798788307992203,0.8572542387636148,0.0056577283,False,0.04445658711165948,0.2300675690991956,Cd83,NCBITaxon:10090,gene,2317,protein_coding,CD83
+31445,False,0.3182510202200007,0.8904167691558411,0.3530675,False,0.24979268107410116,0.49919291314536596,Rnf144b,NCBITaxon:10090,gene,3892,protein_coding,RNF144B
+94846,False,0.9234343426052116,0.8408984213192512,0.47506535,False,0.7289909004891268,0.71040171951487,Sema4d,NCBITaxon:10090,gene,571,protein_coding,SEMA4D
+11933,True,0.17280137763440834,1.1618309245586005,1.3154283,False,0.08849751391248564,0.34612959668554244,Cxcl14,NCBITaxon:10090,gene,1647,protein_coding,CXCL14
+4777,True,0.08915414605457873,1.3909293125613351,1.7954196,False,0.039385533266087736,0.25185442012404957,Tgfbi,NCBITaxon:10090,gene,782,protein_coding,TGFBI
+27173,False,0.2612617914247867,0.8422296244120914,-0.058952592,False,0.16977141240808838,0.42541575161862194,Adamts16,NCBITaxon:10090,gene,4179,protein_coding,ADAMTS16
+35274,False,0.33257293759147927,0.8155991119684964,0.0039520017,False,0.26016573105280727,0.49817632310978927,Pcsk1,NCBITaxon:10090,gene,1419,protein_coding,PCSK1
+27609,True,0.4483560223936217,1.63955564351643,3.9890642,False,0.2290242444573041,0.5408102848863108,Vcan,NCBITaxon:10090,gene,2273,protein_coding,VCAN
+3395,False,0.03576366751527003,0.9256135367409477,0.30668423,False,0.02536842745502948,0.17411101429491363,Crhbp,NCBITaxon:10090,gene,1740,protein_coding,CRHBP
+37031,True,0.4800265877515538,1.1405304683645563,1.6518037,False,0.2752796888354433,0.5549323972204552,Sv2c,NCBITaxon:10090,gene,1616,protein_coding,SV2C
+50889,True,0.5785426463112335,1.4589251720157814,3.0072782,False,0.36618272387067297,0.6137740347029862,Hexb,NCBITaxon:10090,gene,1316,protein_coding,HEXB
+6650,False,0.07988094312096053,1.11052929940121,0.89830536,False,0.021802513748162337,0.16321609111387217,Cartpt,NCBITaxon:10090,gene,1032,protein_coding,CARTPT
+26,True,0.000314188528997657,1.389701834702485,1.7914925,False,6.783739988706332e-05,0.010087463361911533,Gzma,NCBITaxon:10090,gene,937,protein_coding,GZMA
+102307,False,1.2672570392119917,0.9610847120774092,1.0178295,False,0.9836608820420207,0.7468676057511723,Hcn1,NCBITaxon:10090,gene,9580,protein_coding,HCN1
+12846,True,0.218879154793641,1.3114829897004592,1.9589767,False,0.10539167321878841,0.39142805868763114,Fam107a,NCBITaxon:10090,gene,2622,protein_coding,FAM107A
+10392,False,0.09810863359638355,0.8076011799302412,-0.07088555,False,0.12081932048921384,0.35444263093354583,Fezf2,NCBITaxon:10090,gene,2188,protein_coding,FEZF2
+51190,True,0.7409463688659199,1.4688718801747473,2.831911,False,0.40576667408760203,0.6727610982533363,Synpr,NCBITaxon:10090,gene,2541,protein_coding,SYNPR
+112694,True,1.5052524661151592,1.1835938951000087,1.6099027,False,1.1323778346231979,0.7766407781474661,Cacna2d3,NCBITaxon:10090,gene,918,protein_coding,CACNA2D3
+125894,False,1.4233102035795677,0.4889033768464198,-0.25004265,False,1.1404299956898383,0.7025581767479067,Cacna1d,NCBITaxon:10090,gene,4210,protein_coding,CACNA1D
+3959,True,0.10209968041974084,1.7177319759646252,2.8409948,False,0.04644149571706934,0.2800478319605745,Stab1,NCBITaxon:10090,gene,1236,protein_coding,STAB1
+1193,False,0.009669165849673393,0.7882194921787762,-0.13289548,False,0.0054737359754084575,0.08121122660021837,Chat,NCBITaxon:10090,gene,658,protein_coding,CHAT
+34,False,0.0004842127942140711,1.1280772729714859,0.95444846,False,5.5098357211336384e-05,0.00790525334703147,Anxa8,NCBITaxon:10090,gene,1544,protein_coding,ANXA8
+2099,False,0.018306971850632542,0.7655272390687147,-0.20549728,False,0.0010845987965002514,0.034305452882546945,Sncg,NCBITaxon:10090,gene,852,protein_coding,SNCG
+2116,True,0.0418370688289434,1.4718876225296653,2.0544384,False,0.015449119480281064,0.15311326158077837,Bmp4,NCBITaxon:10090,gene,1075,protein_coding,BMP4
+2569,True,0.051613886188104835,1.4048753358128252,1.8400387,False,0.022097745275486063,0.1905518333155467,Rnase4,NCBITaxon:10090,gene,1489,protein_coding,RNASE4
+368,True,0.005437911372783759,1.179167033289163,1.1179055,False,0.0007935850128600849,0.032975350620380366,Trac,NCBITaxon:10090,gene,674,TR_C_gene,TRAC
+48322,False,0.5443667546143193,0.9597515686302879,0.80509824,False,0.36024130113301384,0.5959779641002311,Adra1a,NCBITaxon:10090,gene,3883,protein_coding,ADRA1A
+28602,False,0.3049581467737679,0.884543574932425,0.32566187,False,0.25287130846025496,0.5031576648177191,Htr2a,NCBITaxon:10090,gene,5606,protein_coding,HTR2A
+17046,True,0.2608927107642915,1.87551944249467,4.3845015,False,0.10877138671464762,0.37467721931004144,9630013A20Rik,NCBITaxon:10090,gene,1605,lncRNA,9630013A20RIK
+54479,True,0.7978249015727401,1.3736618236299007,2.4599602,False,0.4718033372431873,0.7136140134435227,Dach1,NCBITaxon:10090,gene,5063,protein_coding,DACH1
+17159,True,0.34031551949769,1.4659045891511342,3.038419,False,0.12323782667988076,0.42392021617990533,Ednrb,NCBITaxon:10090,gene,3989,protein_coding,EDNRB
+80339,True,1.3706515129373649,1.9244746725888806,3.5478756,False,0.6075056442006328,0.8074320383776485,Gpc5,NCBITaxon:10090,gene,1953,protein_coding,GPC5
+15947,True,0.24643610103156788,1.2749079606228562,1.8016932,False,0.10336225088500345,0.37059966066814687,Cldn10,NCBITaxon:10090,gene,1148,protein_coding,CLDN10
+104305,True,1.4430591435747295,1.2769951627393215,1.8253611,False,1.0528316185336044,0.7841763773975984,Hs6st3,NCBITaxon:10090,gene,1708,protein_coding,HS6ST3
+59531,False,0.5347680619411113,0.7568037265090525,-0.14543891,False,0.4106060728416346,0.5891847589061088,Rap2a,NCBITaxon:10090,gene,4191,protein_coding,RAP2A
+5143,True,0.07950410598216344,1.3612040736552036,1.7003164,False,0.03820581359664088,0.23612104913793291,Gpr183,NCBITaxon:10090,gene,1542,protein_coding,GPR183
+26980,True,0.4850969346428108,1.3687502743735234,2.720706,False,0.20769594875170233,0.5497583421414359,Selenop,NCBITaxon:10090,gene,627,protein_coding,SELENOP
+1010,True,0.021875931176867307,1.4856527421912893,2.0984786,False,0.010278122434236975,0.13120542089490217,Il7r,NCBITaxon:10090,gene,2532,protein_coding,IL7R
+6489,True,0.1055512935745394,1.4774742521662148,2.0723124,False,0.018513846322133765,0.1650325652208397,Adamts12,NCBITaxon:10090,gene,2289,protein_coding,ADAMTS12
+80932,True,0.9484272386985799,1.0416301856139214,1.1867691,False,0.8065251946368477,0.7286979617174488,Pdzd2,NCBITaxon:10090,gene,1051,protein_coding,PDZD2
+54014,True,0.6648545662453265,1.083958626905317,1.3617034,False,0.48997845700759557,0.6687731000984493,Cdh6,NCBITaxon:10090,gene,4981,protein_coding,CDH6
+86750,True,1.148093084943974,1.142103054608432,1.6028792,False,0.8921020842929359,0.7653505095742227,Cdh12,NCBITaxon:10090,gene,5636,protein_coding,CDH12
+30111,True,0.4610886054320119,1.410008039235517,2.913943,False,0.1594944653810077,0.4600332438509738,Fbxl7,NCBITaxon:10090,gene,3561,protein_coding,FBXL7
+62672,True,0.847753131028552,1.2771656037122179,2.0829847,False,0.46149110504898777,0.6934672166996683,Sema5a,NCBITaxon:10090,gene,3158,protein_coding,SEMA5A
+26206,True,0.3724468026670025,1.2551065609706413,2.0547895,False,0.18017956307332175,0.4575456892495081,Angpt1,NCBITaxon:10090,gene,2895,protein_coding,ANGPT1
+21228,False,0.2647763097642294,1.096004581710772,1.0323553,False,0.2737218740325581,0.5297029674218912,Rspo2,NCBITaxon:10090,gene,3259,protein_coding,RSPO2
+14605,True,0.24563918221204448,1.3908794368003567,2.300405,False,0.06733375178498259,0.3139816665986819,Sntb1,NCBITaxon:10090,gene,1225,protein_coding,SNTB1
+15323,False,0.1443701025040047,0.8251485834364674,-0.13240616,False,0.16271827665690233,0.40386884112166327,Arc,NCBITaxon:10090,gene,3054,protein_coding,ARC
+4762,False,0.055697458277511804,1.0036879958206135,0.55647635,False,0.031806914939420936,0.19705721453545383,Pvalb,NCBITaxon:10090,gene,965,protein_coding,PVALB
+2754,False,0.03527491368082029,1.0885105053214863,0.8278582,False,0.019576272377820655,0.15623911334790688,Lgals1,NCBITaxon:10090,gene,714,protein_coding,LGALS1
+34210,True,0.6536834489345695,1.4405941693595756,2.926831,False,0.3368606602950925,0.6859241613761456,Sox10,NCBITaxon:10090,gene,1847,protein_coding,SOX10
+24570,False,0.2765584080874845,1.0202525788599073,0.7065992,False,0.27679630378290787,0.5234215841865771,Scube1,NCBITaxon:10090,gene,3913,protein_coding,SCUBE1
+4436,True,0.057771502074229514,1.2139673998101674,1.2292461,False,0.023013266526868806,0.17729748490933106,Fbln1,NCBITaxon:10090,gene,2273,protein_coding,FBLN1
+64110,False,0.6607255683024303,0.8628793464589322,0.39147678,False,0.6105193087772057,0.6688828180942905,Lrrk2,NCBITaxon:10090,gene,2827,protein_coding,LRRK2
+38764,False,0.3663335126775276,0.7709280940624716,-0.20449269,False,0.399695327162783,0.5772409880783571,Nr4a1,NCBITaxon:10090,gene,1685,protein_coding,NR4A1
+158553,False,2.2013900125371317,0.709794628094738,1.0128222,False,1.8130971056009013,0.3184841254352716,Rbfox1,NCBITaxon:10090,gene,1546,protein_coding,RBFOX1
+109286,True,1.4947196528547742,1.1246975810221993,1.4740406,False,1.2584460285563235,0.7405148267533271,Grin2a,NCBITaxon:10090,gene,4150,protein_coding,GRIN2A
+4227,False,0.03283963923341294,0.7755970241462576,-0.17327993,False,0.02230839031668314,0.14976994676908129,Ciita,NCBITaxon:10090,gene,3862,protein_coding,CIITA
+5097,False,0.04611262728083078,0.8452517480702707,0.04957401,False,0.027233737160897446,0.1745086567642836,Serpind1,NCBITaxon:10090,gene,2121,protein_coding,SERPIND1
+79224,False,0.854844035856005,0.8403068618523548,0.37633714,False,0.6332636901572373,0.690641783893361,Dgkg,NCBITaxon:10090,gene,2725,protein_coding,DGKG
+20090,True,0.28000468374568227,1.366072248526532,2.1937268,False,0.13034306291338774,0.40806860599198447,Sst,NCBITaxon:10090,gene,721,protein_coding,SST
+147700,False,1.915772148835402,0.42436888270615064,-0.39460853,False,1.5522170393231156,0.5574561828764174,Fgf12,NCBITaxon:10090,gene,1679,protein_coding,FGF12
+12438,False,0.1450395751444222,1.0754197969515449,0.94383466,False,0.09684751995393888,0.3264718574583622,Hes1,NCBITaxon:10090,gene,529,protein_coding,HES1
+42929,True,0.877152846303992,1.7212790617673897,3.5964956,False,0.40958050019483655,0.7563223938947049,Apod,NCBITaxon:10090,gene,700,protein_coding,APOD
+8112,True,0.09835488900138882,1.2078906732346792,1.2098042,False,0.03468343441768899,0.20933862048138233,Mylk,NCBITaxon:10090,gene,1210,protein_coding,MYLK
+4771,True,0.13458905992486112,1.7289820453760327,2.8769884,False,0.05778214932520515,0.3166780418778191,Cd86,NCBITaxon:10090,gene,814,protein_coding,CD86
+24536,False,0.19938088145214083,0.6221151931295644,-1.0055102,False,0.21476306835393363,0.4432490501959323,Zdhhc23,NCBITaxon:10090,gene,1363,protein_coding,ZDHHC23
+15130,True,0.2642061739416405,1.4516988479337776,2.5619466,False,0.12278625100515504,0.41699542341822626,Phldb2,NCBITaxon:10090,gene,664,protein_coding,PHLDB2
+145247,False,1.8871215461847917,0.7471327956914088,0.35742882,False,1.3824142539110793,0.669982789935469,Robo1,NCBITaxon:10090,gene,2326,protein_coding,ROBO1
+117299,False,1.463421005683044,0.9115785774713618,0.9824171,False,1.194020742752335,0.7178111773188873,Robo2,NCBITaxon:10090,gene,4057,protein_coding,ROBO2
+4062,False,0.03654800384002591,0.7936841886145793,-0.11541169,False,0.0014489859711798116,0.04355410734149598,Chodl,NCBITaxon:10090,gene,1758,protein_coding,CHODL
+159262,False,1.8706273994986184,0.2795249276753378,-0.32905838,False,1.5719850622672569,0.4893071364830668,App,NCBITaxon:10090,gene,3120,protein_coding,APP
+15087,True,0.2553550598052614,1.238134614250798,1.6435568,False,0.1158414476538829,0.41009443858969763,Olig2,NCBITaxon:10090,gene,2530,protein_coding,OLIG2
+26020,True,0.4461549838315373,1.3221428718597599,2.502413,False,0.21602260877897342,0.5535525085864152,Olig1,NCBITaxon:10090,gene,2162,protein_coding,OLIG1
+32247,True,0.4337951230050877,1.1409104339687968,1.6535834,False,0.30521562008718445,0.5851295611088975,Sox8,NCBITaxon:10090,gene,997,protein_coding,SOX8
+12711,False,0.1117139802600221,0.7964107284004178,-0.10668838,False,0.10324388875097854,0.32803515513331105,Dusp1,NCBITaxon:10090,gene,1698,protein_coding,DUSP1
+4738,False,0.04258034436904458,0.8696067388421503,0.12749559,False,0.038908750770102994,0.2043498881016673,Cdkn1a,NCBITaxon:10090,gene,1172,protein_coding,CDKN1A
+9014,True,0.15587532648360525,1.332488098559508,2.049305,False,0.06560808010182839,0.31427844958043005,H2-K1,NCBITaxon:10090,gene,861,protein_coding,H2-K1
+19068,False,0.16865803045628952,0.8173317189422932,-0.166021,False,0.11885225447627155,0.35432286490383497,H2-DMa,NCBITaxon:10090,gene,726,protein_coding,H2-DMA
+1024,True,0.01935440339762304,1.345853470744324,1.6512035,False,0.008099317412995471,0.11431990517749197,Psmb9,NCBITaxon:10090,gene,783,protein_coding,PSMB9
+2002,True,0.029663487988544002,1.1903074843393353,1.1535484,False,0.012459727877965555,0.13533073606211518,Tap1,NCBITaxon:10090,gene,2638,protein_coding,TAP1
+967,True,0.015583385629422594,1.2673509956097693,1.400042,False,0.006468760087703251,0.1002080713846215,Psmb8,NCBITaxon:10090,gene,1234,protein_coding,PSMB8
+412,True,0.009991456195117925,1.8181748082906932,3.1623526,False,0.0017588979742987792,0.05382526967559502,H2-Ab1,NCBITaxon:10090,gene,728,protein_coding,H2-AB1
+451,True,0.011207351162450632,1.9156844323491207,3.4743257,False,0.002005695579194633,0.05664686696846611,H2-Aa,NCBITaxon:10090,gene,977,protein_coding,H2-AA
+616,True,0.010397226481072741,1.7210408210853967,2.8515813,False,0.002370517651484434,0.0567833782386202,H2-Eb1,NCBITaxon:10090,gene,1082,protein_coding,H2-EB1
+13411,True,0.29130679678178056,1.5472086959765832,3.4178016,False,0.13167474966475234,0.4588296957889702,C4b,NCBITaxon:10090,gene,763,protein_coding,C4B
+3169,False,0.025758239473449043,0.6775454941235868,-0.48698685,False,0.015990312978828615,0.1294016853958182,Hspa1a,NCBITaxon:10090,gene,2967,protein_coding,HSPA1A
+2590,True,0.04396421734672942,1.3368732924553752,1.6224723,False,0.02433705944817855,0.188935231519958,Aif1,NCBITaxon:10090,gene,775,protein_coding,AIF1
+13606,True,0.16727127227475141,1.1731286278574697,1.3640118,False,0.08217534934886182,0.3297146079016395,H2-D1,NCBITaxon:10090,gene,1744,protein_coding,H2-D1
+67401,True,0.7871681625518947,1.1587476600550897,1.620369,False,0.48553335497345096,0.7053594099421276,Ddr1,NCBITaxon:10090,gene,2284,protein_coding,DDR1
+6060,False,0.0631473910137768,0.9985591593872596,0.5400671,False,0.04585109055973741,0.2291602629888527,H2-T23,NCBITaxon:10090,gene,1707,protein_coding,H2-T23
+46671,True,1.0463136378040536,1.831412446306107,3.845571,False,0.4828467520636858,0.8174667256613111,Mog,NCBITaxon:10090,gene,1165,protein_coding,MOG
+68492,True,0.8472995118074944,1.0706448215424544,1.2761834,False,0.5214982207167367,0.6942790045177536,Ptchd4,NCBITaxon:10090,gene,3918,protein_coding,PTCHD4
+3988,True,0.08898188124232484,1.5311313044738297,2.243983,False,0.04083584229099534,0.2574725973929889,Trem2,NCBITaxon:10090,gene,1072,protein_coding,TREM2
+63055,True,0.8503519062783473,1.2769617335608792,2.0821881,False,0.44055454405263245,0.7130117161337169,Rftn1,NCBITaxon:10090,gene,2385,protein_coding,RFTN1
+3036,False,0.039943541247718484,1.11722943321619,0.9197418,False,0.012136010732243392,0.13014435304230967,Slc5a7,NCBITaxon:10090,gene,4084,protein_coding,SLC5A7
+2169,True,0.03306997188641832,1.256316449985797,1.364738,False,0.013921414908608395,0.14232588968190277,Plin4,NCBITaxon:10090,gene,6053,protein_coding,PLIN4
+20549,False,0.23527467750585057,1.0322648781208124,0.75825566,False,0.12737472294034327,0.38144587021578163,L3mbtl4,NCBITaxon:10090,gene,2549,protein_coding,L3MBTL4
+56367,True,0.8256500792041712,1.3136009971547333,2.2253244,False,0.32120524901953623,0.6242073899471102,Alk,NCBITaxon:10090,gene,2902,protein_coding,ALK
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data/prepared_sources/allen_mouse_reference_bf8cb800/obs.csv

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+version https://git-lfs.github.com/spec/v1
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+size 23135702

data/prepared_sources/allen_mouse_reference_bf8cb800/source_manifest.json

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+{
+  "counts_path": "counts.mtx",
+  "obs_path": "obs.csv",
+  "var_path": "var.csv",
+  "count_orientation": "cell_by_gene",
+  "cell_id_col": "cell_id",
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+  "source_id": "allen_mouse_reference_bf8cb800",
+  "species": "mouse",
+  "family_use": "gaussian",
+  "label_column": "native_label",
+  "sample_column": "sample_id"
+}

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data/prepared_sources/allen_mouse_reference_e63beb9b/source_manifest.json

@@ -0,0 +1,15 @@
+{
+  "counts_path": "counts.mtx",
+  "obs_path": "obs.csv",
+  "var_path": "var.csv",
+  "count_orientation": "cell_by_gene",
+  "cell_id_col": "cell_id",
+  "feature_id_col": "feature_id",
+  "n_obs": 50000,
+  "n_vars": 480,
+  "source_id": "allen_mouse_reference_e63beb9b",
+  "species": "mouse",
+  "family_use": "gaussian",
+  "label_column": "native_label",
+  "sample_column": "sample_id"
+}

data/prepared_sources/allen_mouse_reference_e63beb9b/var.csv

@@ -0,0 +1,481 @@
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+57112,False,58720,13.405033,30.097734604716496,1126063.0,12.939832203307155,514.8324711547417,20.551759719848633,102.0,True,False,Cacna2d3,NCBITaxon:10090,gene,918,protein_coding,CACNA2D3
+14029,False,14554,0.42049688,82.67442829422758,35323.0,0.41056533827618164,1.7105360314976754,2.63457989692688,,False,False,Calb1,NCBITaxon:10090,gene,736,protein_coding,CALB1
+4367,False,4505,0.11303168,94.63709629417997,9495.0,0.11272620847763523,0.42130115884580666,6.068356513977051,836.0,True,False,Calcrl,NCBITaxon:10090,gene,2691,protein_coding,CALCRL
+3415,False,3524,0.07258074,95.804911729343,6097.0,0.07239890737037218,0.18400750810265282,7.653591632843018,570.0,True,False,Capn3,NCBITaxon:10090,gene,2056,protein_coding,CAPN3
+23097,False,23559,0.939276,71.95457305096247,78902.0,0.9479171747719624,4.81034296615277,12.868504524230957,256.0,True,False,Car2,NCBITaxon:10090,gene,881,protein_coding,CAR2
+1513,False,1558,0.030915562,98.14530433436902,2597.0,0.030791180918004,0.12171813553384878,3.5057485103607178,,False,False,Cartpt,NCBITaxon:10090,gene,1032,protein_coding,CARTPT
+1865,False,1971,0.03378451,97.65365522659904,2838.0,0.03280327788888347,0.06640926773994875,3.12223744392395,,False,False,Cav1,NCBITaxon:10090,gene,472,protein_coding,CAV1
+6214,False,6580,0.11394831,92.16694641858028,9572.0,0.10964099312228671,0.2053731916007645,1.3948308229446411,,False,False,Cbln2,NCBITaxon:10090,gene,2338,protein_coding,CBLN2
+28710,False,29971,0.925586,64.3215123269407,77752.0,0.8817374762206721,3.772192359357622,2.515153646469116,,False,False,Cck,NCBITaxon:10090,gene,584,protein_coding,CCK
+353,False,383,0.004868874,99.54406390247968,409.0,0.004499780498512268,0.005235657000305477,1.1958162784576416,,False,False,Ccl17,NCBITaxon:10090,gene,514,protein_coding,CCL17
+40,False,41,0.00060712115,99.9511922193255,51.0,0.0006097263548119604,0.001023980996820352,3.7577877044677734,1796.0,True,False,Ccl2,NCBITaxon:10090,gene,954,protein_coding,CCL2
+82,False,82,0.0026665714,99.902384438651,224.0,0.0027315740695575824,0.020089364138000117,12.495329856872559,271.0,True,False,Ccl4,NCBITaxon:10090,gene,660,protein_coding,CCL4
+210,False,215,0.003357023,99.74405675987762,282.0,0.0033778840056582606,0.012805194031077279,4.598616123199463,1280.0,True,False,Ccl5,NCBITaxon:10090,gene,600,protein_coding,CCL5
+96,False,103,0.0018570765,99.87738533147625,156.0,0.0014267596702599874,0.0023759261139053426,4.615943431854248,1274.0,True,False,Ccr2,NCBITaxon:10090,gene,3257,protein_coding,CCR2
+56,False,63,0.00083330355,99.92500267847578,70.0,0.0007316716257743525,0.0008287026048126715,0.9592373371124268,,False,False,Ccr7,NCBITaxon:10090,gene,1978,protein_coding,CCR7
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+713,False,738,0.011785293,99.12145994785901,990.0,0.011511633578849812,0.02786645632356822,5.022581100463867,1126.0,True,False,Cd163,NCBITaxon:10090,gene,4404,protein_coding,CD163
+8,False,8,9.5234696e-05,99.9904765306001,8.0,9.755621676991366e-05,9.75478891048601e-05,1.4078588485717773,,False,False,Cd19,NCBITaxon:10090,gene,2432,protein_coding,CD19
+99,False,101,0.0014642334,99.87976619882623,123.0,0.0014755377786449442,0.0022050590823358184,1.880106806755066,,False,False,Cd2,NCBITaxon:10090,gene,968,protein_coding,CD2
+51,False,58,0.0009880599,99.93095484685071,83.0,0.0009145895322179406,0.001962506320318569,5.746449947357178,906.0,True,False,Cd209a,NCBITaxon:10090,gene,867,protein_coding,CD209A
+153,False,159,0.0022023022,99.81072104567694,185.0,0.002170625823130579,0.002873231815951654,3.665569305419922,1881.0,True,False,Cd22,NCBITaxon:10090,gene,1263,protein_coding,CD22
+1186,False,1234,0.016844636,98.53100484506506,1415.0,0.016413833471537975,0.021193212201350387,2.469180107116699,,False,False,Cd24a,NCBITaxon:10090,gene,743,protein_coding,CD24A
+192,False,201,0.003428449,99.76072283132746,288.0,0.0033291058972733036,0.007634978648220712,4.7953572273254395,1208.0,True,False,Cd36,NCBITaxon:10090,gene,2692,protein_coding,CD36
+20,False,21,0.00030951275,99.97500089282526,26.0,0.0003048631774059802,0.00045111006207778807,2.574002981185913,,False,False,Cd3g,NCBITaxon:10090,gene,1023,protein_coding,CD3G
+1482,False,1512,0.019475495,98.20006428341846,1636.0,0.019572215989463928,0.022628276933000843,1.1846426725387573,,False,False,Cd4,NCBITaxon:10090,gene,946,protein_coding,CD4
+1952,False,2075,0.0315584,97.52985012440033,2651.0,0.03032778888834691,0.052919707681849754,2.5640969276428223,,False,False,Cd44,NCBITaxon:10090,gene,1981,protein_coding,CD44
+740,False,760,0.011463876,99.09527040700927,963.0,0.011487244524657334,0.018160055310876118,4.853121280670166,1185.0,True,False,Cd48,NCBITaxon:10090,gene,1054,protein_coding,CD48
+881,False,924,0.012082902,98.90003928431128,1015.0,0.011743329593678357,0.014312783356722347,2.1743557453155518,,False,False,Cd52,NCBITaxon:10090,gene,506,protein_coding,CD52
+254,False,296,0.008535409,99.64763163220361,717.0,0.006231403346178235,0.04784966102325693,12.863944053649902,257.0,True,False,Cd74,NCBITaxon:10090,gene,963,protein_coding,CD74
+3161,False,3367,0.04801019,95.99180981631609,4033.0,0.04608311789668797,0.07415401750507425,2.6753592491149902,,False,False,Cd83,NCBITaxon:10090,gene,2317,protein_coding,CD83
+2634,False,2686,0.06796186,96.80249514898277,5709.0,0.06854543680796059,0.22613448410060963,10.043813705444336,364.0,True,False,Cd86,NCBITaxon:10090,gene,814,protein_coding,CD86
+14,False,15,0.00023808674,99.98214349487519,20.0,0.00021950148773230575,0.0003901814442547324,2.200859308242798,,False,False,Cd8a,NCBITaxon:10090,gene,2975,protein_coding,CD8A
+49,False,83,0.0016904158,99.90119400497602,142.0,0.0008414223696405054,0.001669962584984632,2.8011956214904785,,False,False,Cdh1,NCBITaxon:10090,gene,2609,protein_coding,CDH1
+47863,False,49294,6.6363583,41.318762425151476,557474.0,6.228025462172577,146.96800302286277,9.004799842834473,449.0,True,False,Cdh12,NCBITaxon:10090,gene,5636,protein_coding,CDH12
+34093,False,35256,1.8962537,58.03007035463019,159291.0,1.8275693868591776,15.02710670500503,5.577017784118652,944.0,True,False,Cdh4,NCBITaxon:10090,gene,587,protein_coding,CDH4
+29323,False,30406,1.3546302,63.803673678321005,113793.0,1.2863031071655042,8.223750053655277,3.7212331295013428,1828.0,True,False,Cdh6,NCBITaxon:10090,gene,4981,protein_coding,CDH6
+160,False,164,0.0022737284,99.804768877302,191.0,0.002280376566996732,0.003177610204284868,2.8613600730895996,,False,False,Cdk1,NCBITaxon:10090,gene,695,protein_coding,CDK1
+3091,False,3276,0.043998428,96.10013928073997,3696.0,0.04221745280718014,0.05109377923223985,1.1402837038040161,,False,False,Cdkn1a,NCBITaxon:10090,gene,1172,protein_coding,CDKN1A
+17421,False,18268,0.81049484,78.2531576253229,68084.0,0.7723281791132139,9.177656513835068,10.022605895996094,368.0,True,False,Cemip,NCBITaxon:10090,gene,4287,protein_coding,CEMIP
+19269,False,20220,0.4403057,75.92943109174672,36987.0,0.4276254816838203,4.76155088513737,12.156526565551758,285.0,True,False,Cemip2,NCBITaxon:10090,gene,2954,protein_coding,CEMIP2
+624,False,648,0.012118615,99.2285989786079,1018.0,0.01201160918979562,0.03440323603951136,5.364957809448242,1016.0,True,False,Cenpf,NCBITaxon:10090,gene,2014,protein_coding,CENPF
+421,False,432,0.013392379,99.48573265240526,1125.0,0.013340812643285693,0.11015944722715232,4.906472682952881,1162.0,True,False,Chat,NCBITaxon:10090,gene,658,protein_coding,CHAT
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+1860,False,2029,0.025713367,97.58461007344977,2160.0,0.023803716891858933,0.025871433296674056,0.9390116333961487,,False,False,Ciita,NCBITaxon:10090,gene,3862,protein_coding,CIITA
+6174,False,6393,0.107924715,92.389557515803,9066.0,0.10673869567338179,0.19114808234784406,2.997468948364258,,False,False,Cldn10,NCBITaxon:10090,gene,1148,protein_coding,CLDN10
+20375,False,20753,0.81438756,75.29492994297823,68411.0,0.8238622506219209,3.877954054705295,10.119566917419434,359.0,True,False,Cldn11,NCBITaxon:10090,gene,1870,protein_coding,CLDN11
+19022,False,19447,0.5195886,76.8496363225123,43647.0,0.5208404468074728,1.917117467366273,7.11061954498291,634.0,True,False,Clic4,NCBITaxon:10090,gene,3606,protein_coding,CLIC4
+50302,False,51816,8.303239,38.31648869683225,697497.0,7.872396468464953,182.7767125183835,8.627090454101562,477.0,True,False,Clstn2,NCBITaxon:10090,gene,4269,protein_coding,CLSTN2
+9533,False,9731,0.34520197,88.41588990869373,28998.0,0.3499585386078728,9.660364741036084,30.054567337036133,55.0,True,False,Cnksr3,NCBITaxon:10090,gene,3000,protein_coding,CNKSR3
+27881,False,28837,0.6267871,65.67146411437686,52652.0,0.6188112774986586,1.2356550580264374,4.230031967163086,1461.0,True,False,Cntn2,NCBITaxon:10090,gene,3561,protein_coding,CNTN2
+62965,False,64613,20.564016,23.082508958013403,1727439.0,19.945161211648212,1299.0591855173132,28.065425872802734,61.0,True,False,Cntn5,NCBITaxon:10090,gene,3297,protein_coding,CNTN5
+9527,False,9931,0.28170422,88.17780317369618,23664.0,0.2707794741719916,1.6099452675636294,4.099677085876465,1543.0,True,False,Col11a1,NCBITaxon:10090,gene,2112,protein_coding,COL11A1
+11027,False,11389,0.42075878,86.44215087556397,35345.0,0.41449197600117066,2.2085706344368448,3.348015069961548,,False,False,Col12a1,NCBITaxon:10090,gene,3265,protein_coding,COL12A1
+6606,False,6923,0.09446091,91.75862766805948,7935.0,0.09206867957660601,0.11249443885221053,1.1722508668899536,,False,False,Col20a1,NCBITaxon:10090,gene,3867,protein_coding,COL20A1
+28481,False,29412,1.822804,64.9869647512589,153121.0,1.7627554753426662,43.674443712700196,14.430548667907715,206.0,True,False,Col25a1,NCBITaxon:10090,gene,2723,protein_coding,COL25A1
+14263,False,15037,0.22771806,82.09944882920848,19129.0,0.21869664894395396,0.28152499662007624,1.0365521907806396,,False,False,Coro1a,NCBITaxon:10090,gene,781,protein_coding,CORO1A
+1899,False,1958,0.050879136,97.66913086437388,4274.0,0.049826837715233406,0.1965099757459589,2.7597708702087402,,False,False,Crhbp,NCBITaxon:10090,gene,1740,protein_coding,CRHBP
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+80095,False,82061,83.72627,2.3118221968263075,7033258.0,79.85186088483489,8056.637981612798,12.632401466369629,266.0,True,False,Csmd1,NCBITaxon:10090,gene,2911,protein_coding,CSMD1
+4142,False,4204,0.12225754,94.9954168303513,10270.0,0.12395736793327154,0.47455548400617764,6.376034736633301,764.0,True,False,Cspg4,NCBITaxon:10090,gene,5067,protein_coding,CSPG4
+43432,False,44856,1.5136483,46.60190707474733,127151.0,1.4894273450075606,5.324047089839804,5.696574687957764,916.0,True,False,Cspg5,NCBITaxon:10090,gene,3790,protein_coding,CSPG5
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+60254,False,61694,4.665595,26.55738485530279,391924.0,4.626445051460904,38.546954273155414,15.38327407836914,179.0,True,False,Ctnna3,NCBITaxon:10090,gene,2795,protein_coding,CTNNA3
+2204,False,2311,0.0395224,97.2489077771032,3320.0,0.03860787278669333,0.07118968177697643,5.655831336975098,930.0,True,False,Ctsh,NCBITaxon:10090,gene,818,protein_coding,CTSH
+3744,False,3830,0.11017463,95.44063902479674,9255.0,0.110726306033852,0.4099679906980532,11.688470840454102,301.0,True,False,Ctss,NCBITaxon:10090,gene,1355,protein_coding,CTSS
+28148,False,28936,1.5842649,65.55361118055308,133083.0,1.5214745622164771,13.124656211309295,4.693815231323242,1240.0,True,False,Cux2,NCBITaxon:10090,gene,740,protein_coding,CUX2
+8005,False,8313,0.23327738,90.10392485982644,19596.0,0.2325252426710892,1.0478666472293894,13.720402717590332,230.0,True,False,Cx3cr1,NCBITaxon:10090,gene,4123,protein_coding,CX3CR1
+5178,False,5308,0.11004369,93.68117805316477,9244.0,0.11025071947709868,0.46254677566926183,4.156888484954834,1507.0,True,False,Cxcl14,NCBITaxon:10090,gene,1647,protein_coding,CXCL14
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+26117,False,26839,1.7899956,68.04995059700249,150365.0,1.7863640798009852,22.397696575899193,10.164752960205078,355.0,True,False,Dach1,NCBITaxon:10090,gene,5063,protein_coding,DACH1
+70096,False,71761,18.963026,14.573289049200621,1592951.0,18.757255743622263,691.9923897034433,17.508838653564453,139.0,True,False,Dcc,NCBITaxon:10090,gene,4855,protein_coding,DCC
+70497,False,72278,15.114317,13.95783483923193,1269648.0,14.375591434564168,239.54884313763083,3.86076283454895,1709.0,True,False,Dclk1,NCBITaxon:10090,gene,2922,protein_coding,DCLK1
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+2844,False,3032,0.05421235,96.390605097437,4554.0,0.05002195014877323,0.09993305074801564,3.0136215686798096,,False,False,Ddr2,NCBITaxon:10090,gene,1800,protein_coding,DDR2
+38339,False,39585,2.3276193,52.87668297560801,195527.0,2.244353933954441,18.37773783729585,4.126771450042725,1530.0,True,False,Dgkg,NCBITaxon:10090,gene,2725,protein_coding,DGKG
+45058,False,46517,4.37376,44.624596740592594,367409.0,4.235903126676748,67.27397112320335,9.154718399047852,438.0,True,False,Dlc1,NCBITaxon:10090,gene,3423,protein_coding,DLC1
+4768,False,4879,0.09265146,94.19187409973453,7783.0,0.09270279498561046,0.20849570567562367,3.7971489429473877,1762.0,True,False,Dlx1,NCBITaxon:10090,gene,808,protein_coding,DLX1
+4745,False,5035,0.06545004,94.00616644643644,5498.0,0.06285059265401688,0.07038849853617238,1.1265809535980225,,False,False,Dnajb1,NCBITaxon:10090,gene,2925,protein_coding,DNAJB1
+32878,False,34032,1.6378106,59.48716117281525,137581.0,1.5811667723525682,12.567305512766476,4.881592750549316,1168.0,True,False,Dner,NCBITaxon:10090,gene,1389,protein_coding,DNER
+7729,False,8196,0.12056712,90.2432055998,10128.0,0.11525047558655675,0.16082055236064957,1.1549015045166016,,False,False,Dusp1,NCBITaxon:10090,gene,1698,protein_coding,DUSP1
+6704,False,6949,0.38667667,91.72767639250979,32482.0,0.3750304863177406,3.7592077414703544,15.901297569274902,165.0,True,False,Ebf1,NCBITaxon:10090,gene,2864,protein_coding,EBF1
+6472,False,6601,0.14775662,92.14194731140554,12412.0,0.14862689624896347,0.4133084812569683,4.963766574859619,1147.0,True,False,Ednrb,NCBITaxon:10090,gene,3989,protein_coding,EDNRB
+6273,False,6379,0.10346059,92.40622358725284,8691.0,0.10442173552509634,0.16824795042495,3.864978075027466,1707.0,True,False,Efhd1,NCBITaxon:10090,gene,1726,protein_coding,EFHD1
+8960,False,9252,0.52016,88.98610763901289,43695.0,0.5172186722598897,6.142320247244495,14.842013359069824,191.0,True,False,Egfr,NCBITaxon:10090,gene,716,protein_coding,EGFR
+25934,False,27003,0.6914158,67.85471947430449,58081.0,0.667723525681674,1.9030539203171852,1.7704883813858032,,False,False,Egr1,NCBITaxon:10090,gene,4484,protein_coding,EGR1
+7234,False,7572,0.11136507,90.98603621299239,9355.0,0.10837276230427784,0.15021269052842565,1.3489108085632324,,False,False,Elovl2,NCBITaxon:10090,gene,3818,protein_coding,ELOVL2
+7322,False,7391,0.12774543,91.20150470816519,10731.0,0.12968879566850397,0.2391590509867116,3.7753474712371826,1784.0,True,False,Erbb3,NCBITaxon:10090,gene,6016,protein_coding,ERBB3
+12583,False,12739,0.29841793,84.83506541433043,25068.0,0.30211940880932636,0.7739228033943534,5.834437370300293,881.0,True,False,Ermn,NCBITaxon:10090,gene,3635,protein_coding,ERMN
+2839,False,2906,0.07286645,96.54059974048546,6121.0,0.07324032974001268,0.2567969291316335,6.162989616394043,817.0,True,False,Etnppl,NCBITaxon:10090,gene,1597,protein_coding,ETNPPL
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+5562,False,5755,0.11805531,93.14905420044522,9917.0,0.11742110140968733,0.29831045467270323,4.320046424865723,1421.0,True,False,Fam107a,NCBITaxon:10090,gene,2622,protein_coding,FAM107A
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+1738,False,1853,0.02657048,97.7941264002476,2232.0,0.024950002438905418,0.03425426258807612,2.4544894695281982,,False,False,Fbln1,NCBITaxon:10090,gene,2273,protein_coding,FBLN1
+9321,False,9749,0.4803876,88.39446210254395,40354.0,0.4547583044729525,7.783168339080096,11.748757362365723,297.0,True,False,Fbxl7,NCBITaxon:10090,gene,3561,protein_coding,FBXL7
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+842,False,867,0.013249527,98.96789400378559,1113.0,0.013145700209745866,0.02238733869704615,5.065394878387451,1111.0,True,False,Fcgr2b,NCBITaxon:10090,gene,1462,protein_coding,FCGR2B
+8730,False,9256,0.17537469,88.98134590431295,14732.0,0.1636261645773377,0.32555472511617134,1.3941991329193115,,False,False,Fezf2,NCBITaxon:10090,gene,2188,protein_coding,FEZF2
+73183,False,75002,22.268158,10.715093508565165,1870592.0,21.09655626554802,654.0721128187515,7.012170791625977,649.0,True,False,Fgf12,NCBITaxon:10090,gene,1679,protein_coding,FGF12
+38274,False,39545,2.479078,52.92430032260753,208250.0,2.4662333544705137,63.804025464404006,22.560537338256836,88.0,True,False,Fgf13,NCBITaxon:10090,gene,617,protein_coding,FGF13
+68213,False,69979,3.0783663,16.69464185802888,258592.0,3.0410589727330373,9.03053247367488,6.088261127471924,831.0,True,False,Fgfr2,NCBITaxon:10090,gene,2209,protein_coding,FGFR2
+5420,False,5557,0.122055165,93.3847600680928,10253.0,0.12246963562753037,0.3097574167943985,4.592517375946045,1286.0,True,False,Fgfr3,NCBITaxon:10090,gene,968,protein_coding,FGFR3
+15938,False,16499,0.38858137,80.35903479637632,32642.0,0.38032291107750843,1.1165504804619126,2.2342216968536377,,False,False,Filip1,NCBITaxon:10090,gene,3775,protein_coding,FILIP1
+1151,False,1316,0.11710296,98.43338928371605,9837.0,0.0935564118823472,1.9702966380259184,60.92631912231445,11.0,True,False,Flt1,NCBITaxon:10090,gene,2039,protein_coding,FLT1
+7054,False,7610,0.1583872,90.94079973334286,13305.0,0.14460270230720454,0.3582222742587827,1.4568668603897095,,False,False,Flt3,NCBITaxon:10090,gene,3804,protein_coding,FLT3
+5083,False,5362,0.09283002,93.61689463471544,7798.0,0.08958099604897322,0.1809682329064115,1.5084404945373535,,False,False,Fos,NCBITaxon:10090,gene,814,protein_coding,FOS
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+35309,False,36219,3.8861232,56.88368272561695,326446.0,3.862409150773133,85.10908823395293,15.126602172851562,185.0,True,False,Foxp2,NCBITaxon:10090,gene,1575,protein_coding,FOXP2
+226,False,246,0.0029522756,99.70715331595301,248.0,0.0027803521779425394,0.0028214343341515435,1.029082179069519,,False,False,Foxp3,NCBITaxon:10090,gene,1891,protein_coding,FOXP3
+72580,False,74376,19.90667,11.460304989107527,1672220.0,19.127518169845374,792.6476536528586,33.92348861694336,39.0,True,False,Frmd4a,NCBITaxon:10090,gene,2025,protein_coding,FRMD4A
+67442,False,69201,19.071093,17.620799257169384,1602029.0,18.50699965855324,992.5166028338969,19.008859634399414,125.0,True,False,Frmpd4,NCBITaxon:10090,gene,6330,protein_coding,FRMPD4
+40209,False,41474,5.1810055,50.627953763556064,435220.0,4.8803716891858935,88.48674373220157,7.207931995391846,621.0,True,False,Fstl4,NCBITaxon:10090,gene,4380,protein_coding,FSTL4
+57961,False,59534,2.2971442,29.12872159327643,192967.0,2.2318789327349884,15.890026244085105,8.463277816772461,492.0,True,False,Fyn,NCBITaxon:10090,gene,2114,protein_coding,FYN
+21178,False,21538,0.8606597,74.3604395081128,72298.0,0.8610067801570656,5.396506899505855,5.252429962158203,1052.0,True,False,Gad1,NCBITaxon:10090,gene,1451,protein_coding,GAD1
+20966,False,21320,1.0125234,74.61995404926014,85055.0,1.0184625140237062,7.155129053250745,5.845412254333496,872.0,True,False,Gad2,NCBITaxon:10090,gene,3232,protein_coding,GAD2
+61125,False,62658,18.804483,25.409806792614553,1579633.0,18.351897468416176,2604.3755508312397,62.787654876708984,8.0,True,False,Galntl6,NCBITaxon:10090,gene,899,protein_coding,GALNTL6
+2263,False,2482,0.035736818,97.04534361868029,3002.0,0.03298619579532706,0.04633699182192691,1.4382672309875488,,False,False,Gas2l3,NCBITaxon:10090,gene,565,protein_coding,GAS2L3
+1398,False,1484,0.021368284,98.23339642631811,1795.0,0.02063313984683674,0.030231683321087752,3.4209399223327637,,False,False,Gbp7,NCBITaxon:10090,gene,4055,protein_coding,GBP7
+1976,False,2079,0.037462946,97.52508838970037,3147.0,0.03618116189454173,0.10011402622658688,5.341304779052734,1023.0,True,False,Gfap,NCBITaxon:10090,gene,2600,protein_coding,GFAP
+7246,False,7540,0.21042106,91.02413009059201,17676.0,0.20640456563094484,0.7401240969492564,6.842605113983154,683.0,True,False,Gja1,NCBITaxon:10090,gene,1746,protein_coding,GJA1
+25757,False,26610,0.6561194,68.3225599085747,55116.0,0.6501877957172821,1.9146041865289984,6.248278617858887,799.0,True,False,Glul,NCBITaxon:10090,gene,1376,protein_coding,GLUL
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+2268,False,2352,0.03315358,97.2000999964287,2785.0,0.032730110726306036,0.04517093743900449,3.4149577617645264,,False,False,Gpr183,NCBITaxon:10090,gene,1542,protein_coding,GPR183
+3223,False,3304,0.07065224,96.06680713784031,5935.0,0.07089898053753475,0.19381965692907072,8.156832695007324,522.0,True,False,Gpr34,NCBITaxon:10090,gene,1993,protein_coding,GPR34
+8573,False,8763,0.19542159,89.56822970608192,16416.0,0.19589288327398663,0.5076787034997238,4.8397603034973145,1189.0,True,False,Gpr37l1,NCBITaxon:10090,gene,2269,protein_coding,GPR37L1
+77361,False,79193,21.504446,5.725985976691305,1806438.0,20.72328179113214,521.8899882980501,9.862470626831055,381.0,True,False,Grid2,NCBITaxon:10090,gene,4125,protein_coding,GRID2
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+19446,False,20102,1.2456102,76.06990226539529,104635.0,1.2188185942149163,20.682944759890194,12.428901672363281,275.0,True,False,Grin3a,NCBITaxon:10090,gene,5678,protein_coding,GRIN3A
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+75008,False,76822,29.20285,8.548504220087382,2453127.0,27.925015852885224,1117.2866349095784,10.77957820892334,338.0,True,False,Grm7,NCBITaxon:10090,gene,3127,protein_coding,GRM7
+47484,False,48929,8.183315,41.75327071652203,687423.0,7.718916150431686,197.14216574107724,10.056806564331055,361.0,True,False,Grm8,NCBITaxon:10090,gene,2830,protein_coding,GRM8
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+89,False,114,0.0033689274,99.86429056105139,283.0,0.0023779327837666455,0.01800588153003137,8.590787887573242,482.0,True,False,H2-Aa,NCBITaxon:10090,gene,977,protein_coding,H2-AA
+83,False,96,0.0022856328,99.88571836720116,192.0,0.0015608994683186186,0.005655893136446892,5.080850601196289,1105.0,True,False,H2-Ab1,NCBITaxon:10090,gene,728,protein_coding,H2-AB1
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+8850,False,9359,0.12650739,88.85873123578921,10627.0,0.12149407345983122,0.13722125466286192,1.1364258527755737,,False,False,H2-DMa,NCBITaxon:10090,gene,726,protein_coding,H2-DMA
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+3444,False,3618,0.053629037,95.69301096389414,4505.0,0.05179015657772792,0.08200977043540023,3.7384324073791504,1810.0,True,False,H2-K1,NCBITaxon:10090,gene,861,protein_coding,H2-K1
+3325,False,3522,0.048164945,95.80729259669297,4046.0,0.0465587044534413,0.060220222021104754,1.4164700508117676,,False,False,H2-T23,NCBITaxon:10090,gene,1707,protein_coding,H2-T23
+10778,False,10934,0.23708677,86.98379819768341,19916.0,0.24034193453977853,0.5932965966141175,5.912144660949707,857.0,True,False,Hapln2,NCBITaxon:10090,gene,1184,protein_coding,HAPLN2
+52965,False,54478,6.37957,35.147554254014736,535903.0,5.956758206916736,102.55171962733336,6.662291049957275,713.0,True,False,Hcn1,NCBITaxon:10090,gene,9580,protein_coding,HCN1
+26839,False,27853,0.4993512,66.84285085056486,41947.0,0.4860616555289986,0.7169380202219452,1.8116415739059448,,False,False,Hcn2,NCBITaxon:10090,gene,3195,protein_coding,HCN2
+6918,False,7371,0.1097937,91.22531338166495,9223.0,0.10408028876640164,0.14871010493981288,1.4303804636001587,,False,False,Hes1,NCBITaxon:10090,gene,529,protein_coding,HES1
+23428,False,24511,0.6003714,70.82128019237408,50433.0,0.5877030388761524,2.4332797549408265,12.339934349060059,278.0,True,False,Hexb,NCBITaxon:10090,gene,1316,protein_coding,HEXB
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+3020,False,3138,0.04839113,96.26441912788829,4065.0,0.04787571337983513,0.07690011279185761,3.304710865020752,,False,False,Hopx,NCBITaxon:10090,gene,1217,protein_coding,HOPX
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+29201,False,30353,2.4813042,63.86676666309536,208437.0,2.3430076581630166,31.834614350585266,6.182796001434326,815.0,True,False,Hs3st2,NCBITaxon:10090,gene,1713,protein_coding,HS3ST2
+49028,False,50364,11.287359,40.04499839291454,948172.0,11.12753036437247,690.3865991175074,26.57139015197754,68.0,True,False,Hs3st4,NCBITaxon:10090,gene,3189,protein_coding,HS3ST4
+54220,False,55759,14.155888,33.62260871635537,1189137.0,13.457404516852836,706.3466728531721,22.456701278686523,89.0,True,False,Hs6st3,NCBITaxon:10090,gene,1708,protein_coding,HS6ST3
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+20623,False,21211,1.4446388,74.7497113198338,121354.0,1.4311131164333448,21.982535492537913,10.036974906921387,366.0,True,False,Htr2c,NCBITaxon:10090,gene,3057,protein_coding,HTR2C
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+2520,False,2719,0.045081723,96.76321083770819,3787.0,0.0419247841568704,0.07750768376850686,4.032724857330322,1578.0,True,False,Id3,NCBITaxon:10090,gene,738,protein_coding,ID3
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+741,False,857,0.013118579,98.97979834053545,1102.0,0.011304326618213746,0.017859357453079986,3.7834153175354004,1771.0,True,False,Ifitm3,NCBITaxon:10090,gene,678,protein_coding,IFITM3
+7410,False,7893,0.14068545,90.60390700332131,11818.0,0.1314935856787474,0.2665646947748977,1.4973504543304443,,False,False,Igfbp4,NCBITaxon:10090,gene,763,protein_coding,IGFBP4
+7275,False,7678,0.1485185,90.85985024344369,12476.0,0.1391883322764743,0.352246922373561,3.983299493789673,1616.0,True,False,Igfbp5,NCBITaxon:10090,gene,5143,protein_coding,IGFBP5
+4729,False,4938,0.09297287,94.12163851291025,7810.0,0.09004438807863031,0.18851886197367704,1.8241231441497803,,False,False,Ighm,NCBITaxon:10090,gene,1595,IG_C_gene,IGHM
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+12673,False,12888,0.4903039,84.65769079675725,41187.0,0.4955489976098727,2.6667484773011605,13.327932357788086,237.0,True,False,Il33,NCBITaxon:10090,gene,725,protein_coding,IL33
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+3269,False,3329,0.062212065,96.03704629596562,5226.0,0.06277742549143944,0.13261492644866862,6.349934101104736,769.0,True,False,Irf8,NCBITaxon:10090,gene,1284,protein_coding,IRF8
+1824,False,1945,0.026856184,97.68460650214872,2256.0,0.025779230281449687,0.03523654875047366,2.2417151927948,,False,False,Irgm1,NCBITaxon:10090,gene,2259,protein_coding,IRGM1
+988,False,1037,0.014844708,98.76552027903766,1247.0,0.014511487244524657,0.021788609321354645,4.003710746765137,1604.0,True,False,Irgm2,NCBITaxon:10090,gene,3470,protein_coding,IRGM2
+18679,False,19545,0.7761866,76.73297382236348,65202.0,0.7384517828398615,4.099829443292849,2.953329086303711,,False,False,Itga8,NCBITaxon:10090,gene,924,protein_coding,ITGA8
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+3067,False,3118,0.07952097,96.28822780138805,6680.0,0.08038632261840886,0.24243129350511167,9.589424133300781,401.0,True,False,Itgam,NCBITaxon:10090,gene,3513,protein_coding,ITGAM
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+8594,False,9130,0.3911289,89.1313405473614,32856.0,0.34737329886347007,3.3717391340385725,4.871127605438232,1179.0,True,False,L3mbtl4,NCBITaxon:10090,gene,2549,protein_coding,L3MBTL4
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+80354,False,82296,37.239574,2.032070283204168,3128236.0,36.052668162528654,1982.1912555175068,16.710826873779297,150.0,True,False,Lrrc4c,NCBITaxon:10090,gene,3312,protein_coding,LRRC4C
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+14644,False,15008,1.1733867,82.13397140578313,98568.0,1.1831861860397055,14.094632844083,24.336519241333008,78.0,True,False,Mertk,NCBITaxon:10090,gene,3523,protein_coding,MERTK
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+45,False,48,0.0008094949,99.94285918360059,68.0,0.0007926442612555486,0.001987103863553996,5.268961429595947,1048.0,True,False,Ms4a1,NCBITaxon:10090,gene,3014,protein_coding,MS4A1
+185,False,195,0.0047260216,99.76786543337738,397.0,0.004633920296570899,0.01473408424432401,8.498522758483887,490.0,True,False,Ms4a7,NCBITaxon:10090,gene,1334,protein_coding,MS4A7
+14367,False,14988,0.24487221,82.15778007928289,20570.0,0.23865908980049755,0.39476852009703783,2.9305694103240967,,False,False,Mt1,NCBITaxon:10090,gene,633,protein_coding,MT1
+3626,False,3840,0.053640943,95.42873468804686,4506.0,0.051473098873225695,0.06718939611545971,2.363542318344116,,False,False,Mt2,NCBITaxon:10090,gene,449,protein_coding,MT2
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+34043,False,35207,1.7856982,58.088401604704586,150004.0,1.7037461587239646,12.351899432702545,5.038989067077637,1121.0,True,False,Myo16,NCBITaxon:10090,gene,6719,protein_coding,MYO16
+11486,False,11921,0.32586932,85.80884016047045,27374.0,0.3168991756499683,2.0180449632622133,3.944958448410034,1643.0,True,False,Myo5b,NCBITaxon:10090,gene,764,protein_coding,MYO5B
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+8681,False,9014,0.87189746,89.26943085366,73242.0,0.8224476854787571,30.760055586999425,19.90997886657715,114.0,True,False,Ndst4,NCBITaxon:10090,gene,3019,protein_coding,NDST4
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+80373,False,82319,66.09061,2.0046903086794465,5551809.0,63.9131017999122,3435.7432626058603,30.14775848388672,54.0,True,False,Nkain2,NCBITaxon:10090,gene,1088,protein_coding,NKAIN2
+80606,False,82563,49.940205,1.71422449198243,4195127.0,47.198990293156434,2902.8266877599485,10.851819038391113,336.0,True,False,Nlgn1,NCBITaxon:10090,gene,4272,protein_coding,NLGN1
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+49794,False,51043,7.7592707,39.23669392759782,651802.0,7.766986976245061,329.38804174412013,43.27238082885742,25.0,True,False,Npas3,NCBITaxon:10090,gene,3193,protein_coding,NPAS3
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+6957,False,7171,0.3403569,91.4634001166625,28591.0,0.3369591727232818,9.492154483851873,21.79045295715332,93.0,True,False,Npy,NCBITaxon:10090,gene,566,protein_coding,NPY
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+27180,False,28358,0.7615085,66.24168184469602,63969.0,0.7226476757231355,2.034143857379199,1.5603796243667603,,False,False,Nr4a1,NCBITaxon:10090,gene,1685,protein_coding,NR4A1
+5573,False,5892,0.17666036,92.98596478697189,14840.0,0.16766255304619287,1.1441265265681697,4.067044734954834,1559.0,True,False,Nr4a2,NCBITaxon:10090,gene,2761,protein_coding,NR4A2
+76938,False,78857,51.402653,6.125971691487209,4317977.0,49.225379249792695,4331.091454467634,18.507549285888672,131.0,True,False,Nrg1,NCBITaxon:10090,gene,186,protein_coding,NRG1
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+32246,False,33407,1.7348785,60.23118221968263,145735.0,1.6761011657967904,12.156456752371268,4.311429500579834,1428.0,True,False,Nrp1,NCBITaxon:10090,gene,3229,protein_coding,NRP1
+23883,False,24938,1.6090021,70.3129650131543,135161.0,1.4888785912882299,22.236270103227806,8.027914047241211,537.0,True,False,Ntng1,NCBITaxon:10090,gene,1620,protein_coding,NTNG1
+18997,False,19854,0.7353666,76.36512981679225,61773.0,0.6998317155260719,5.926714360176956,4.6093549728393555,1276.0,True,False,Ntng2,NCBITaxon:10090,gene,1141,protein_coding,NTNG2
+32132,False,33384,2.632013,60.25856219420736,221097.0,2.469050290229745,30.825511867083815,5.512429714202881,964.0,True,False,Nwd2,NCBITaxon:10090,gene,2825,protein_coding,NWD2
+3045,False,3141,0.10296061,96.26084782686333,8649.0,0.10260475098775669,0.8041984271940229,5.211811065673828,1071.0,True,False,Nxph2,NCBITaxon:10090,gene,2579,protein_coding,NXPH2
+1865,False,1939,0.027058557,97.69174910419865,2273.0,0.02665723623237891,0.037434329007427075,3.6022777557373047,1930.0,True,False,Olfml3,NCBITaxon:10090,gene,3981,protein_coding,OLFML3
+10894,False,11074,0.1995881,86.81713748318512,16766.0,0.20134383688600557,0.3963588150333153,4.008847236633301,1599.0,True,False,Olig1,NCBITaxon:10090,gene,2162,protein_coding,OLIG1
+6086,False,6169,0.09735367,92.65621465900027,8178.0,0.098592751573094,0.16206574885714667,3.0300071239471436,,False,False,Olig2,NCBITaxon:10090,gene,2530,protein_coding,OLIG2
+15953,False,16492,0.4458293,80.36736783210124,37451.0,0.43875908492268667,1.8380235860206682,3.5642240047454834,1958.0,True,False,Onecut2,NCBITaxon:10090,gene,16234,protein_coding,ONECUT2
+15143,False,15367,0.52445745,81.7066057164625,44056.0,0.5311814057850837,2.3214671310246198,9.214946746826172,432.0,True,False,Opalin,NCBITaxon:10090,gene,2316,protein_coding,OPALIN
+11922,False,12415,0.30556053,85.22076592502648,25668.0,0.3031803326666992,1.2888593169666283,12.583724975585938,268.0,True,False,P2ry12,NCBITaxon:10090,gene,2157,protein_coding,P2RY12
+14747,False,15737,0.39658108,81.26614525671702,33314.0,0.3682137456709429,1.0980181938398015,1.8800188302993774,,False,False,Palmd,NCBITaxon:10090,gene,2869,protein_coding,PALMD
+4337,False,4426,0.08147328,94.731140554504,6844.0,0.0817033315448027,0.18763444847931837,4.596546173095703,1283.0,True,False,Pax6,NCBITaxon:10090,gene,1365,protein_coding,PAX6
+56989,False,58463,13.125079,30.403676059188367,1102546.0,12.531144822203794,713.714808679525,27.73393440246582,64.0,True,False,Pcdh15,NCBITaxon:10090,gene,5256,protein_coding,PCDH15
+6208,False,6509,0.08479459,92.25146720950443,7123.0,0.08267889371250182,0.09133125740068875,1.474867820739746,,False,False,Pcna,NCBITaxon:10090,gene,824,protein_coding,PCNA
+18266,False,19181,0.5848243,77.16629168005905,49127.0,0.5487903029120531,2.5867564601055477,2.4264917373657227,,False,False,Pcsk1,NCBITaxon:10090,gene,1419,protein_coding,PCSK1
+16192,False,16450,0.35676107,80.41736604645072,29969.0,0.3602019413687137,0.8813379395370281,4.550292491912842,1309.0,True,False,Pcsk6,NCBITaxon:10090,gene,2207,protein_coding,PCSK6
+62,False,64,0.0008213992,99.92381224480079,69.0,0.000817033315448027,0.0010114905401981027,2.4621596336364746,,False,False,Pdcd1,NCBITaxon:10090,gene,1933,protein_coding,PDCD1
+76404,False,78252,29.021797,6.846184064854821,2437918.0,28.728196185551923,1211.729970425234,67.90591430664062,5.0,True,False,Pde4b,NCBITaxon:10090,gene,2129,protein_coding,PDE4B
+2822,False,3104,0.117876746,96.30489387283787,9902.0,0.10604360762889616,0.6368772458088114,12.075648307800293,290.0,True,False,Pdgfd,NCBITaxon:10090,gene,2862,protein_coding,PDGFD
+4478,False,4596,0.24781258,94.52876682975608,20817.0,0.25029266865030975,1.974168542321022,12.186630249023438,283.0,True,False,Pdgfra,NCBITaxon:10090,gene,1561,protein_coding,PDGFRA
+3578,False,3732,0.06848565,95.55730152494554,5753.0,0.06720403882737427,0.15344119778572402,5.25679874420166,1051.0,True,False,Pdgfrb,NCBITaxon:10090,gene,5407,protein_coding,PDGFRB
+45948,False,47334,3.3760936,43.65201242812756,283602.0,3.242305253402273,31.821047944775486,5.349605083465576,1019.0,True,False,Pdzd2,NCBITaxon:10090,gene,1051,protein_coding,PDZD2
+1591,False,1726,0.031189362,97.94531147697107,2620.0,0.027949856104580263,0.059606830601421454,5.460186004638672,980.0,True,False,Pecam1,NCBITaxon:10090,gene,2991,protein_coding,PECAM1
+65413,False,67034,12.674845,20.20046903086794,1064725.0,11.792741817472319,353.00461495226784,19.746736526489258,115.0,True,False,Pex5l,NCBITaxon:10090,gene,3136,protein_coding,PEX5L
+88,False,93,0.0015594681,99.88928966822613,131.0,0.0014877323057411833,0.002802562060640455,5.846094131469727,871.0,True,False,Pf4,NCBITaxon:10090,gene,411,protein_coding,PF4
+6649,False,6961,0.14975655,91.71339118840993,12580.0,0.139834642212575,0.43219779956523613,6.084267616271973,832.0,True,False,Phldb2,NCBITaxon:10090,gene,664,protein_coding,PHLDB2
+20950,False,21944,0.37927216,73.87712343606776,31860.0,0.36679918052777916,0.560906886929994,2.427088499069214,,False,False,Phyhd1,NCBITaxon:10090,gene,783,protein_coding,PHYHD1
+52,False,54,0.0008094949,99.93571658155066,68.0,0.0008048387883517877,0.0011944304554707605,3.0560362339019775,,False,False,Plac8,NCBITaxon:10090,gene,741,protein_coding,PLAC8
+73773,False,75658,24.95874,9.934169017773176,2096609.0,23.899370762401833,673.2270234933965,8.098559379577637,531.0,True,False,Plcb1,NCBITaxon:10090,gene,2228,protein_coding,PLCB1
+11719,False,12022,0.33808318,85.6886063592967,28400.0,0.3373128140090727,1.2736191389545226,6.420853614807129,752.0,True,False,Plce1,NCBITaxon:10090,gene,6769,protein_coding,PLCE1
+7433,False,7744,0.14418532,90.78128162089449,12112.0,0.14072484269060045,0.3075257650982293,2.454289436340332,,False,False,Plch1,NCBITaxon:10090,gene,5554,protein_coding,PLCH1
+13602,False,13955,0.82560146,83.38749806554529,69353.0,0.806741134578801,9.680929155040277,7.635573387145996,572.0,True,False,Pld5,NCBITaxon:10090,gene,3278,protein_coding,PLD5
+5771,False,5912,0.085330285,92.96215611347213,7168.0,0.08515438271303839,0.11407347225728671,3.084230661392212,,False,False,Pmp22,NCBITaxon:10090,gene,733,protein_coding,PMP22
+1252,False,1317,0.020534981,98.43219885004108,1725.0,0.01953563240817521,0.03615360312283722,1.648301601409912,,False,False,Postn,NCBITaxon:10090,gene,2670,protein_coding,POSTN
+18225,False,18931,0.97045344,77.463900098806,81521.0,0.9153090093166187,6.99221761484104,3.853780746459961,1714.0,True,False,Pou6f2,NCBITaxon:10090,gene,960,protein_coding,POU6F2
+57283,False,58816,10.175541,29.983452971917668,854776.0,9.83226427979123,209.76954251882856,9.45382022857666,412.0,True,False,Prkg1,NCBITaxon:10090,gene,1541,protein_coding,PRKG1
+15269,False,15459,0.4205445,81.59708581836362,35327.0,0.4264670016096776,1.4784287894308994,6.020876884460449,841.0,True,False,Prox1,NCBITaxon:10090,gene,3285,protein_coding,PROX1
+21160,False,21493,3.7894838,74.41400902348725,318328.0,3.8488976147505,86.598279213505,45.8573112487793,22.0,True,False,Prr5l,NCBITaxon:10090,gene,561,protein_coding,PRR5L
+7165,False,7367,0.18554099,91.2300751163649,15586.0,0.18453977854738793,0.5761540374283849,5.5375075340271,950.0,True,False,Prrx1,NCBITaxon:10090,gene,4952,protein_coding,PRRX1
+38790,False,40124,0.8218159,52.235039224789595,69035.0,0.7958758109360519,1.2062505664337262,1.3834987878799438,,False,False,Psen1,NCBITaxon:10090,gene,1978,protein_coding,PSEN1
+9018,False,9495,0.12950727,88.69683225599086,10879.0,0.12489634651968197,0.14256565758503406,1.1954668760299683,,False,False,Psen2,NCBITaxon:10090,gene,1462,protein_coding,PSEN2
+11182,False,11670,0.15954192,86.1076390128924,13402.0,0.15589483439832202,0.17590869159729666,1.4299123287200928,,False,False,Psenen,NCBITaxon:10090,gene,488,protein_coding,PSENEN
+347,False,369,0.0046784044,99.56072997392951,393.0,0.004511975025608507,0.005198963077454825,2.8063626289367676,,False,False,Psmb8,NCBITaxon:10090,gene,1234,protein_coding,PSMB8
+408,False,428,0.0054878993,99.49049438710522,461.0,0.005377786449441491,0.006202558396348851,3.2518177032470703,,False,False,Psmb9,NCBITaxon:10090,gene,783,protein_coding,PSMB9
+30369,False,31419,1.9703344,62.597764365558376,165514.0,1.9085288522511097,20.271903161111762,6.404720306396484,757.0,True,False,Ptchd4,NCBITaxon:10090,gene,3918,protein_coding,PTCHD4
+17606,False,18105,0.84195805,78.44719831434593,70727.0,0.7802668162528658,8.651581378267483,30.18625831604004,53.0,True,False,Ptgds,NCBITaxon:10090,gene,830,protein_coding,PTGDS
+3325,False,3401,0.10499625,95.9513350713665,8820.0,0.10542168674698796,0.46312487979796046,12.840498924255371,258.0,True,False,Ptprc,NCBITaxon:10090,gene,3213,protein_coding,PTPRC
+27143,False,27750,2.5778842,66.9654655190886,216550.0,2.6023364713916393,46.39281724244179,27.78154182434082,63.0,True,False,Ptprz1,NCBITaxon:10090,gene,4089,protein_coding,PTPRZ1
+2145,False,2258,0.054498054,97.31200076187754,4578.0,0.052399882932539874,0.1822352560642737,2.3463387489318848,,False,False,Pvalb,NCBITaxon:10090,gene,965,protein_coding,PVALB
+15612,False,16055,0.29240623,80.8875873480709,24563.0,0.29199795131944783,0.5392376992995616,4.382282257080078,1398.0,True,False,Qdpr,NCBITaxon:10090,gene,1189,protein_coding,QDPR
+3313,False,3390,0.0617597,95.96442984179137,5188.0,0.06160675089020048,0.13041916386598607,3.2833101749420166,,False,False,Rab37,NCBITaxon:10090,gene,2393,protein_coding,RAB37
+80132,False,82085,90.06976,2.2832517886265946,7566130.0,86.30876542607677,12216.371670056576,23.34195327758789,83.0,True,False,Rbfox1,NCBITaxon:10090,gene,1546,protein_coding,RBFOX1
+51299,False,52782,7.1584945,37.16652976679404,601335.0,6.795827032827667,75.52114595820818,3.241225242614746,,False,False,Rbfox3,NCBITaxon:10090,gene,1547,protein_coding,RBFOX3
+19297,False,19920,1.7446996,76.28656119424306,146560.0,1.7293424710989709,47.78083431272126,18.588329315185547,130.0,True,False,Reln,NCBITaxon:10090,gene,2555,protein_coding,RELN
+23826,False,24434,0.77369857,70.91294358534815,64993.0,0.7667674747573289,3.1334365172278504,5.987277507781982,843.0,True,False,Rftn1,NCBITaxon:10090,gene,2385,protein_coding,RFTN1
+65864,False,67588,3.8704214,19.540968774924707,325127.0,3.73991512609141,29.419154296905756,3.951190233230591,1637.0,True,False,Rfx3,NCBITaxon:10090,gene,2682,protein_coding,RFX3
+6849,False,7038,0.13184053,91.62172779543589,11075.0,0.13177405980196089,0.28147811067619505,4.953034400939941,1151.0,True,False,Rgs10,NCBITaxon:10090,gene,463,protein_coding,RGS10
+10062,False,10431,0.21650417,87.58258633620228,18187.0,0.2118067411345788,0.7609737291713795,3.1511592864990234,,False,False,Rgs12,NCBITaxon:10090,gene,795,protein_coding,RGS12
+39614,False,40861,2.8633263,51.35768960632359,240528.0,2.8250329252231596,27.84319564419725,14.095159530639648,219.0,True,False,Rgs20,NCBITaxon:10090,gene,883,protein_coding,RGS20
+51937,False,53460,9.888945,36.35941573515231,830701.0,9.403748597629384,255.6116152785034,10.566479682922363,341.0,True,False,Rgs6,NCBITaxon:10090,gene,1420,protein_coding,RGS6
+49408,False,50829,3.1037345,39.49144673404521,260723.0,2.982305741183357,16.839570816075465,2.5829861164093018,,False,False,Rit2,NCBITaxon:10090,gene,1740,protein_coding,RIT2
+16854,False,17709,0.44296038,78.91861004964107,37210.0,0.4267230866786986,1.427248747336733,2.243295669555664,,False,False,Rnf144b,NCBITaxon:10090,gene,3892,protein_coding,RNF144B
+67623,False,69356,14.8672905,17.43628203754628,1248897.0,14.240646797717185,479.7438273733199,13.684073448181152,231.0,True,False,Robo1,NCBITaxon:10090,gene,2326,protein_coding,ROBO1
+61969,False,63665,9.174435,24.211040081901835,770680.0,8.782461831130188,141.78457296203024,10.48474407196045,345.0,True,False,Robo2,NCBITaxon:10090,gene,4057,protein_coding,ROBO2
+23145,False,24004,2.289561,71.4248300655929,192330.0,2.234451977952295,39.7052111053468,19.613370895385742,116.0,True,False,Rorb,NCBITaxon:10090,gene,2854,protein_coding,RORB
+561,False,593,0.010844851,99.29407283073223,911.0,0.010438515194380762,0.022451186310179064,1.3372617959976196,,False,False,Ros1,NCBITaxon:10090,gene,7180,protein_coding,ROS1
+35474,False,36800,1.594812,56.19204076044903,133969.0,1.5206819179552218,6.98886532769662,2.111492395401001,,False,False,Rph3a,NCBITaxon:10090,gene,3358,protein_coding,RPH3A
+27251,False,28475,0.77679366,66.10240110472245,65253.0,0.7420247792790595,3.084979015469069,3.2583844661712646,,False,False,Rras2,NCBITaxon:10090,gene,2904,protein_coding,RRAS2
+17605,False,18443,0.7641156,78.04483173220004,64188.0,0.7217818642993025,4.813622102044443,3.5033111572265625,,False,False,Rspo2,NCBITaxon:10090,gene,3259,protein_coding,RSPO2
+7544,False,7959,0.31986952,90.52533838077211,26870.0,0.30269255158284963,2.4598681204908828,4.621973037719727,1269.0,True,False,Rxfp1,NCBITaxon:10090,gene,3433,protein_coding,RXFP1
+49474,False,50900,7.0246897,39.406925943121074,590095.0,6.8765304131505784,159.5033702478723,12.942646026611328,255.0,True,False,Ryr3,NCBITaxon:10090,gene,1984,protein_coding,RYR3
+3316,False,3584,0.048962537,95.73348570884373,4113.0,0.046070923369591725,0.056850896327082526,1.2205817699432373,,False,False,S100a10,NCBITaxon:10090,gene,611,protein_coding,S100A10
+204,False,213,0.0025951455,99.7464376272276,218.0,0.0025364616360177552,0.002627616257694338,2.12839674949646,,False,False,S100a4,NCBITaxon:10090,gene,417,protein_coding,S100A4
+9507,False,9975,0.15953001,88.12542409199672,13401.0,0.15344373445197795,0.24279864748108426,2.236375093460083,,False,False,S100b,NCBITaxon:10090,gene,1484,protein_coding,S100B
+29743,False,30643,1.310727,63.521540897348906,110105.0,1.2834739768791765,7.264421593521003,3.234767436981201,,False,False,Samd5,NCBITaxon:10090,gene,1315,protein_coding,SAMD5
+36837,False,38176,3.2229445,54.55400402366581,270737.0,3.039900492658895,29.113269582673386,3.9072532653808594,1678.0,True,False,Satb2,NCBITaxon:10090,gene,4694,protein_coding,SATB2
+18030,False,19058,0.8354225,77.31271502208254,70178.0,0.7681942344275889,5.433491508874783,3.448298931121826,,False,False,Scube1,NCBITaxon:10090,gene,3913,protein_coding,SCUBE1
+281,False,291,0.0037736748,99.65358380057855,317.0,0.0037071362372567192,0.004351950911355347,1.8885700702667236,,False,False,Sdc1,NCBITaxon:10090,gene,746,protein_coding,SDC1
+42445,False,43668,4.6723094,48.016142280632835,392488.0,4.550351202380372,104.41480311553863,11.501208305358887,306.0,True,False,Sdk1,NCBITaxon:10090,gene,4369,protein_coding,SDK1
+10172,False,10456,0.18654096,87.55282549432758,15670.0,0.18517389395639236,0.34712308598798364,4.543840408325195,1314.0,True,False,Selenop,NCBITaxon:10090,gene,627,protein_coding,SELENOP
+3881,False,3951,0.1394474,95.29659655012321,11714.0,0.14100531681381395,0.6191792437067196,14.43935489654541,205.0,True,False,Selplg,NCBITaxon:10090,gene,553,protein_coding,SELPLG
+24057,False,24816,1.1403046,70.4581979215028,95789.0,1.1023486659187356,8.379107930770088,5.00383186340332,1135.0,True,False,Sema3e,NCBITaxon:10090,gene,5206,protein_coding,SEMA3E
+44154,False,45501,1.1113532,45.8340773543802,93357.0,1.0878981513096921,3.8978382513479133,4.243503570556641,1458.0,True,False,Sema4d,NCBITaxon:10090,gene,571,protein_coding,SEMA4D
+26423,False,27028,1.714546,67.82495863242978,144027.0,1.7105872884249549,24.160315697180675,11.16407299041748,322.0,True,False,Sema5a,NCBITaxon:10090,gene,3158,protein_coding,SEMA5A
+66190,False,67831,7.4073067,19.25169339190267,622236.0,7.120177064533437,96.74368809714785,8.203691482543945,517.0,True,False,Sema6d,NCBITaxon:10090,gene,4503,protein_coding,SEMA6D
+4,False,4,4.7617348e-05,99.99523826530005,4.0,4.877810838495683e-05,4.877632388530249e-05,1.3982572555541992,,False,False,Serpina3f,NCBITaxon:10090,gene,2207,protein_coding,SERPINA3F
+1316,False,1386,0.019285025,98.35005892646691,1620.0,0.018840544363689576,0.025900166566565748,2.6471893787384033,,False,False,Serpina3n,NCBITaxon:10090,gene,2064,protein_coding,SERPINA3N
+2055,False,2236,0.02774901,97.33819030272728,2331.0,0.026071898931759426,0.027514338256941398,1.1021233797073364,,False,False,Serpind1,NCBITaxon:10090,gene,2121,protein_coding,SERPIND1
+23863,False,24650,0.7267955,70.65580991155078,61053.0,0.7153065704111995,2.6993103568503463,4.138582229614258,1522.0,True,False,Serpine2,NCBITaxon:10090,gene,1669,protein_coding,SERPINE2
+236,False,244,0.0033451186,99.70953418330298,281.0,0.0033169113701770644,0.004330302551215355,1.5162957906723022,,False,False,Sfrp2,NCBITaxon:10090,gene,600,protein_coding,SFRP2
+44267,False,45577,1.9426568,45.74360439508113,163189.0,1.9005048534217843,9.645930259855922,3.2018301486968994,,False,False,Sgk1,NCBITaxon:10090,gene,749,protein_coding,SGK1
+12475,False,13000,0.2591455,84.52436222515863,21769.0,0.253926637724989,0.5773872373285927,2.7590630054473877,,False,False,Sh3bp4,NCBITaxon:10090,gene,2668,protein_coding,SH3BP4
+92,False,97,0.0017380331,99.88452793352619,146.0,0.0016706502121847716,0.003448302146518186,5.888533592224121,866.0,True,False,Siglec1,NCBITaxon:10090,gene,5790,protein_coding,SIGLEC1
+3684,False,3755,0.16264895,95.52992155042081,13663.0,0.16399200039022488,0.9015357435551483,17.74919319152832,135.0,True,False,Siglech,NCBITaxon:10090,gene,1794,protein_coding,SIGLECH
+1436,False,1485,0.02207064,98.23220599264312,1854.0,0.021803814448075703,0.03240143384713789,3.3612048625946045,,False,False,Slc14a1,NCBITaxon:10090,gene,2217,protein_coding,SLC14A1
+3234,False,3370,0.06047403,95.98823851529113,5080.0,0.05922881810643383,0.1286699953715964,1.8331429958343506,,False,False,Slc17a6,NCBITaxon:10090,gene,1686,protein_coding,SLC17A6
+38885,False,40336,2.1219122,51.98266728569219,178247.0,2.033998341544315,9.499447735129877,2.071423053741455,,False,False,Slc17a7,NCBITaxon:10090,gene,2722,protein_coding,SLC17A7
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data/prepared_sources/allen_mouse_reference_fbe9c55e/source_manifest.json

@@ -0,0 +1,15 @@
+{
+  "counts_path": "counts.mtx",
+  "obs_path": "obs.csv",
+  "var_path": "var.csv",
+  "count_orientation": "cell_by_gene",
+  "cell_id_col": "cell_id",
+  "feature_id_col": "feature_id",
+  "n_obs": 50000,
+  "n_vars": 108,
+  "source_id": "allen_mouse_reference_fbe9c55e",
+  "species": "mouse",
+  "family_use": "gaussian",
+  "label_column": "native_label",
+  "sample_column": "sample_id"
+}

data/prepared_sources/allen_mouse_reference_fbe9c55e/var.csv

@@ -0,0 +1,109 @@
+feature_is_filtered,feature_name,feature_reference,feature_biotype,feature_length,feature_type,feature_id
+False,Aif1,NCBITaxon:10090,gene,775,protein_coding,AIF1
+False,Anln,NCBITaxon:10090,gene,670,protein_coding,ANLN
+False,Aqp4,NCBITaxon:10090,gene,1248,protein_coding,AQP4
+False,B2m,NCBITaxon:10090,gene,860,protein_coding,B2M
+False,Bmp4,NCBITaxon:10090,gene,1075,protein_coding,BMP4
+False,C4b,NCBITaxon:10090,gene,763,protein_coding,C4B
+False,Calb1,NCBITaxon:10090,gene,736,protein_coding,CALB1
+False,Ccl2,NCBITaxon:10090,gene,954,protein_coding,CCL2
+False,Ccr2,NCBITaxon:10090,gene,3257,protein_coding,CCR2
+False,Cd14,NCBITaxon:10090,gene,1254,protein_coding,CD14
+False,Cd19,NCBITaxon:10090,gene,2432,protein_coding,CD19
+False,Cd24a,NCBITaxon:10090,gene,743,protein_coding,CD24A
+False,Chat,NCBITaxon:10090,gene,658,protein_coding,CHAT
+False,Chodl,NCBITaxon:10090,gene,1758,protein_coding,CHODL
+False,Clic4,NCBITaxon:10090,gene,3606,protein_coding,CLIC4
+False,Crhbp,NCBITaxon:10090,gene,1740,protein_coding,CRHBP
+False,Cspg4,NCBITaxon:10090,gene,5067,protein_coding,CSPG4
+False,Ctla4,NCBITaxon:10090,gene,691,protein_coding,CTLA4
+False,Ctss,NCBITaxon:10090,gene,1355,protein_coding,CTSS
+False,Cux2,NCBITaxon:10090,gene,740,protein_coding,CUX2
+False,Cx3cr1,NCBITaxon:10090,gene,4123,protein_coding,CX3CR1
+False,Egfr,NCBITaxon:10090,gene,716,protein_coding,EGFR
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+False,Lamp5,NCBITaxon:10090,gene,1384,protein_coding,LAMP5
+False,Lhx6,NCBITaxon:10090,gene,3188,protein_coding,LHX6
+False,Mbp,NCBITaxon:10090,gene,1803,protein_coding,MBP
+False,Mrc1,NCBITaxon:10090,gene,3721,protein_coding,MRC1
+False,Ms4a1,NCBITaxon:10090,gene,3014,protein_coding,MS4A1
+False,Ndst4,NCBITaxon:10090,gene,3019,protein_coding,NDST4
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+False,Pdcd1,NCBITaxon:10090,gene,1933,protein_coding,PDCD1
+False,Pdgfra,NCBITaxon:10090,gene,1561,protein_coding,PDGFRA
+False,Prox1,NCBITaxon:10090,gene,3285,protein_coding,PROX1
+False,Ptprc,NCBITaxon:10090,gene,3213,protein_coding,PTPRC
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+False,Rorb,NCBITaxon:10090,gene,2854,protein_coding,RORB
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+False,Satb2,NCBITaxon:10090,gene,4694,protein_coding,SATB2
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+False,Selplg,NCBITaxon:10090,gene,553,protein_coding,SELPLG
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+False,Tap1,NCBITaxon:10090,gene,2638,protein_coding,TAP1
+False,Tbx21,NCBITaxon:10090,gene,2488,protein_coding,TBX21
+False,Tgfb1,NCBITaxon:10090,gene,700,protein_coding,TGFB1
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+False,Tmem132c,NCBITaxon:10090,gene,2754,protein_coding,TMEM132C
+False,Trem2,NCBITaxon:10090,gene,1072,protein_coding,TREM2
+False,Tshz2,NCBITaxon:10090,gene,3243,protein_coding,TSHZ2
+False,Ttr,NCBITaxon:10090,gene,1186,protein_coding,TTR
+False,Vip,NCBITaxon:10090,gene,2031,protein_coding,VIP
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+False,Ano3,NCBITaxon:10090,gene,2432,protein_coding,ANO3
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+False,Onecut2,NCBITaxon:10090,gene,16234,protein_coding,ONECUT2
+False,Cdkn1a,NCBITaxon:10090,gene,1172,protein_coding,CDKN1A
+False,Slc24a3,NCBITaxon:10090,gene,1788,protein_coding,SLC24A3
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+False,Prr5l,NCBITaxon:10090,gene,561,protein_coding,PRR5L
+False,Grin3a,NCBITaxon:10090,gene,5678,protein_coding,GRIN3A
+False,Npas3,NCBITaxon:10090,gene,3193,protein_coding,NPAS3
+False,Hs3st4,NCBITaxon:10090,gene,3189,protein_coding,HS3ST4
+False,Sdk1,NCBITaxon:10090,gene,4369,protein_coding,SDK1
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+False,Itga8,NCBITaxon:10090,gene,924,protein_coding,ITGA8
+False,Col11a1,NCBITaxon:10090,gene,2112,protein_coding,COL11A1
+False,Astn2,NCBITaxon:10090,gene,4817,protein_coding,ASTN2
+False,Gpc5,NCBITaxon:10090,gene,1953,protein_coding,GPC5
+False,Kcnip3,NCBITaxon:10090,gene,1296,protein_coding,KCNIP3
+False,Olig2,NCBITaxon:10090,gene,2530,protein_coding,OLIG2
+False,Dach1,NCBITaxon:10090,gene,5063,protein_coding,DACH1
+False,Apoe,NCBITaxon:10090,gene,817,protein_coding,APOE
+False,Olig1,NCBITaxon:10090,gene,2162,protein_coding,OLIG1
+False,Cd74,NCBITaxon:10090,gene,963,protein_coding,CD74
+False,Trpc6,NCBITaxon:10090,gene,3168,protein_coding,TRPC6
+False,S100a10,NCBITaxon:10090,gene,611,protein_coding,S100A10
+False,Adra1a,NCBITaxon:10090,gene,3883,protein_coding,ADRA1A

data/prepared_sources/human_whole_brain_whb_10xv3_nonneurons_log2/obs.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93aa02df14ccc1da683a29715fadc2301c8cea5c8fec758c5f75cac229ce9531
+size 11585094

data/prepared_sources/human_whole_brain_whb_10xv3_nonneurons_log2/source_manifest.json

@@ -0,0 +1,15 @@
+{
+  "counts_path": "counts.mtx",
+  "obs_path": "obs.csv",
+  "var_path": "var.csv",
+  "count_orientation": "cell_by_gene",
+  "cell_id_col": "cell_id",
+  "feature_id_col": "feature_id",
+  "n_obs": 50000,
+  "n_vars": 536,
+  "source_id": "human_whole_brain_whb_10xv3_nonneurons_log2",
+  "species": "human",
+  "family_use": "gaussian",
+  "label_column": "native_label",
+  "sample_column": "sample_id"
+}

data/prepared_sources/human_whole_brain_whb_10xv3_nonneurons_log2/var.csv

@@ -0,0 +1,537 @@
+gene_symbol,feature_id
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+NKG7,NKG7
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+PEX5L,PEX5L
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+PRR5L,PRR5L
+TESPA1,TESPA1
+B4GALNT1,B4GALNT1
+GLUL,GLUL
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+PHLDB2,PHLDB2
+ALDH1L1,ALDH1L1
+PLAC8,PLAC8
+SFRP2,SFRP2
+ADAMTS16,ADAMTS16
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+CRHBP,CRHBP
+CXCL14,CXCL14
+TENM2,TENM2
+VIP,VIP
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+EGFR,EGFR
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+HTR2C,HTR2C
+ST18,ST18
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+RSPO2,RSPO2
+ASTN2,ASTN2
+NOTCH1,NOTCH1
+MKI67,MKI67
+RGS10,RGS10
+LRRC4C,LRRC4C
+SERPING1,SERPING1
+KIRREL3,KIRREL3
+CNTN5,CNTN5
+PCDH15,PCDH15
+FCGR1A,FCGR1A
+LYPD6B,LYPD6B
+CNDP1,CNDP1
+BTBD11,BTBD11
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+ADAMTS12,ADAMTS12
+FRMD4A,FRMD4A
+QDPR,QDPR
+ZNF385D,ZNF385D
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+TMEM163,TMEM163
+GRID2,GRID2
+RIT2,RIT2
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+STK32B,STK32B
+MERTK,MERTK
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+L3MBTL4,L3MBTL4
+CA10,CA10
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+CACNA1D,CACNA1D
+KIT,KIT
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+CLSTN2,CLSTN2
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+NCSTN,NCSTN
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+CTSS,CTSS
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+ICOS,ICOS
+SYNPR,SYNPR
+PF4,PF4
+GRIK3,GRIK3
+ETNPPL,ETNPPL
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+NPY1R,NPY1R
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+MS4A7,MS4A7
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+TTYH1,TTYH1
+PLIN4,PLIN4
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+MOBP,MOBP
+CX3CR1,CX3CR1
+TAP1,TAP1
+STAT3,STAT3
+IL7R,IL7R
+LRP1B,LRP1B
+NPNT,NPNT
+SNTB2,SNTB2
+STXBP6,STXBP6
+ZBBX,ZBBX
+VXN,VXN
+THBS3,THBS3
+P2RY12,P2RY12
+CD52,CD52
+CLIC4,CLIC4
+GPR183,GPR183
+GJB1,GJB1
+NLGN1,NLGN1
+ROBO1,ROBO1
+ITGAM,ITGAM
+FRMPD4,FRMPD4
+GPR37L1,GPR37L1
+ADRA1B,ADRA1B
+CDK1,CDK1
+FOS,FOS
+SEMA3E,SEMA3E
+ZNF804A,ZNF804A
+ADORA2B,ADORA2B
+CD14,CD14
+PDGFD,PDGFD
+ALK,ALK
+HOPX,HOPX
+RXFP1,RXFP1
+GPR34,GPR34
+NINJ2,NINJ2
+AQP4,AQP4
+MAL,MAL
+SNTB1,SNTB1
+RASGRP1,RASGRP1
+ZFAND4,ZFAND4
+THEMIS,THEMIS
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+HPSE2,HPSE2
+SNCG,SNCG
+NDNF,NDNF
+CSPG4,CSPG4
+NWD2,NWD2
+VWC2L,VWC2L
+GALNTL6,GALNTL6
+UGT8,UGT8
+PHYHD1,PHYHD1
+PCSK1,PCSK1
+SLCO3A1,SLCO3A1
+SOX11,SOX11
+CD19,CD19
+CAVIN1,CAVIN1
+CD163,CD163
+ADAMTSL1,ADAMTSL1
+CDH4,CDH4
+GPC5,GPC5
+CIITA,CIITA
+GRM8,GRM8
+ANKRD18A,ANKRD18A
+TH,TH
+PLD5,PLD5
+TMEM132C,TMEM132C
+SOX2,SOX2
+P2RY13,P2RY13
+IDH2,IDH2
+TSHZ2,TSHZ2
+HS3ST4,HS3ST4
+PLCB1,PLCB1
+RGS6,RGS6
+CSMD1,CSMD1
+TMEM119,TMEM119
+CTNNA3,CTNNA3
+GRIN2A,GRIN2A
+FBXL7,FBXL7
+TAFA1,TAFA1
+CNTN2,CNTN2
+OLIG1,OLIG1
+TSSC4,TSSC4
+ZDHHC23,ZDHHC23
+SLIT3,SLIT3
+PDE4B,PDE4B
+ROBO2,ROBO2
+SLC24A3,SLC24A3
+HS6ST3,HS6ST3
+IRF7,IRF7
+PRKG1,PRKG1
+NR2F2,NR2F2
+IFIT1,IFIT1
+KCNIP4,KCNIP4
+CCK,CCK
+LYPD6,LYPD6
+DCC,DCC
+LUZP2,LUZP2
+THSD4,THSD4
+SEMA4D,SEMA4D
+DNER,DNER
+PDCD1,PDCD1
+COL25A1,COL25A1
+NKAIN2,NKAIN2
+LRRK2,LRRK2
+RELN,RELN
+APOD,APOD
+KAZN,KAZN
+SPOCK3,SPOCK3
+SERPINA3,SERPINA3
+S100A4,S100A4
+GRM7,GRM7
+NTNG2,NTNG2
+SULT1A1,SULT1A1
+LAMA2,LAMA2
+HLA-DQA1,HLA-DQA1
+OPALIN,OPALIN
+KCNMB2,KCNMB2
+S100A10,S100A10
+MPZL1,MPZL1
+MBP,MBP
+ELOVL2,ELOVL2
+CLEC9A,CLEC9A
+ARC,ARC
+GRIN3A,GRIN3A
+GRM3,GRM3
+RYR3,RYR3
+TOX,TOX
+RORB,RORB
+SAMD5,SAMD5
+FCGR3A,FCGR3A
+PSMB8,PSMB8
+HSPA1A,HSPA1A
+AIF1,AIF1
+DDR1,DDR1
+MOG,MOG
+PSENEN,PSENEN
+OLIG2,OLIG2
+TRBC1,TRBC1
+IGHM,IGHM
+CRIP1,CRIP1
+GBP7,GBP7
+LBH,LBH
+LAT,LAT
+MEG3,MEG3
+C4B,C4B
+PRRT4,PRRT4
+PSMB9,PSMB9
+HLA-DMB,HLA-DMB
+PTCHD4,PTCHD4
+SELENOP,SELENOP
+TRIL,TRIL
+RNASE4,RNASE4
+TUBB3,TUBB3
+MRC1,MRC1
+PECAM1,PECAM1
+SPON1,SPON1
+ANXA8,ANXA8
+CCL5,CCL5
+CCL4,CCL4
+DACH1,DACH1
+TRAC,TRAC

data/prepared_sources/mouse_whole_brain_wmb_10xv3_isocortex_2_log2/source_manifest.json

@@ -0,0 +1,15 @@
+{
+  "counts_path": "counts.mtx",
+  "obs_path": "obs.csv",
+  "var_path": "var.csv",
+  "count_orientation": "cell_by_gene",
+  "cell_id_col": "cell_id",
+  "feature_id_col": "feature_id",
+  "n_obs": 50000,
+  "n_vars": 547,
+  "source_id": "mouse_whole_brain_wmb_10xv3_isocortex_2_log2",
+  "species": "mouse",
+  "family_use": "gaussian",
+  "label_column": "native_label",
+  "sample_column": "sample_id"
+}

data/prepared_sources/mouse_whole_brain_wmb_10xv3_isocortex_2_log2/var.csv

@@ -0,0 +1,548 @@
+gene_symbol,feature_id
+Rgs20,RGS20
+St18,ST18
+Vxn,VXN
+Sulf1,SULF1
+Stat1,STAT1
+Satb2,SATB2
+Ctla4,CTLA4
+Icos,ICOS
+Idh1,IDH1
+Vwc2l,VWC2L
+Igfbp5,IGFBP5
+Serpine2,SERPINE2
+Sphkap,SPHKAP
+Dner,DNER
+Efhd1,EFHD1
+Sh3bp4,SH3BP4
+Pdcd1,PDCD1
+Tmem163,TMEM163
+Cxcr4,CXCR4
+Thsd7b,THSD7B
+Cntn2,CNTN2
+Btg2,BTG2
+Gpr37l1,GPR37L1
+Ptprc,PTPRC
+Brinp3,BRINP3
+Rgs16,RGS16
+Glul,GLUL
+Tnr,TNR
+Prrx1,PRRX1
+Mpzl1,MPZL1
+Mgst3,MGST3
+Ddr2,DDR2
+Fcgr2b,FCGR2B
+Fcer1g,FCER1G
+Cd48,CD48
+Ncstn,NCSTN
+Pld5,PLD5
+Psen2,PSEN2
+Tgfb2,TGFB2
+Cenpf,CENPF
+Prox1,PROX1
+Batf3,BATF3
+Fam107b,FAM107B
+Frmd4a,FRMD4A
+Itih5,ITIH5
+Itga8,ITGA8
+C1ql3,C1QL3
+Mrc1,MRC1
+Gad2,GAD2
+Nxph2,NXPH2
+Ptgds,PTGDS
+Notch1,NOTCH1
+Ntng2,NTNG2
+Phyhd1,PHYHD1
+Lhx6,LHX6
+Lrp1b,LRP1B
+Lypd6b,LYPD6B
+Lypd6,LYPD6
+Nr4a2,NR4A2
+Ermn,ERMN
+Cytip,CYTIP
+Ifih1,IFIH1
+Nostrin,NOSTRIN
+Gad1,GAD1
+Dlx1,DLX1
+Calcrl,CALCRL
+Serping1,SERPING1
+Spi1,SPI1
+Lrrc4c,LRRC4C
+Prr5l,PRR5L
+Cd44,CD44
+Cd59a,CD59A
+Pax6,PAX6
+Ano3,ANO3
+Ryr3,RYR3
+Meis2,MEIS2
+Rasgrp1,RASGRP1
+Thbs1,THBS1
+Capn3,CAPN3
+B2m,B2M
+Sema6d,SEMA6D
+Kcnip3,KCNIP3
+Mal,MAL
+Mertk,MERTK
+Siglec1,SIGLEC1
+Pcna,PCNA
+Plcb1,PLCB1
+Lamp5,LAMP5
+Slc24a3,SLC24A3
+Nnat,NNAT
+Slc32a1,SLC32A1
+Tshz2,TSHZ2
+Zbp1,ZBP1
+Cdh4,CDH4
+Col20a1,COL20A1
+Zbtb46,ZBTB46
+Stmn2,STMN2
+Car2,CAR2
+Nlgn1,NLGN1
+Cldn11,CLDN11
+Kcnmb2,KCNMB2
+Pex5l,PEX5L
+Sox2,SOX2
+Anxa5,ANXA5
+Postn,POSTN
+Ccna1,CCNA1
+Dclk1,DCLK1
+P2ry13,P2RY13
+P2ry12,P2RY12
+Plch1,PLCH1
+Zbbx,ZBBX
+Rxfp1,RXFP1
+Sfrp2,SFRP2
+Fcrls,FCRLS
+Nes,NES
+Bcan,BCAN
+Hapln2,HAPLN2
+Thbs3,THBS3
+S100a4,S100A4
+S100a10,S100A10
+Ctss,CTSS
+Cd2,CD2
+Olfml3,OLFML3
+Tmigd3,TMIGD3
+Ntng1,NTNG1
+Col11a1,COL11A1
+Palmd,PALMD
+Ndst4,NDST4
+Col25a1,COL25A1
+Etnppl,ETNPPL
+Npnt,NPNT
+Gbp7,GBP7
+Tox,TOX
+Calb1,CALB1
+Grin3a,GRIN3A
+Klf4,KLF4
+Astn2,ASTN2
+Adamtsl1,ADAMTSL1
+Slc24a2,SLC24A2
+Nfia,NFIA
+Cachd1,CACHD1
+Pde4b,PDE4B
+Ctps,CTPS
+Mycl,MYCL
+Grik3,GRIK3
+Cd52,CD52
+Clic4,CLIC4
+Id3,ID3
+Kazn,KAZN
+Grm3,GRM3
+Sema3e,SEMA3E
+Cd36,CD36
+Reln,RELN
+Fgfr3,FGFR3
+Rgs12,RGS12
+Stk32b,STK32B
+Qdpr,QDPR
+Kcnip4,KCNIP4
+Nwd2,NWD2
+Pdgfra,PDGFRA
+Kit,KIT
+Hopx,HOPX
+Npffr2,NPFFR2
+Adamts3,ADAMTS3
+Pf4,PF4
+Plac8,PLAC8
+Arhgap24,ARHGAP24
+Mepe,MEPE
+Tmem119,TMEM119
+Selplg,SELPLG
+Nos1,NOS1
+Rph3a,RPH3A
+Cux2,CUX2
+Tmem132c,TMEM132C
+Tmem132d,TMEM132D
+Sdk1,SDK1
+Flt3,FLT3
+Flt1,FLT1
+Gng11,GNG11
+Tac1,TAC1
+Foxp2,FOXP2
+Cav1,CAV1
+Ptprz1,PTPRZ1
+Grm8,GRM8
+Prrt4,PRRT4
+Hilpda,HILPDA
+Trbc1,TRBC1
+Tmem176a,TMEM176A
+Gpnmb,GPNMB
+Npy,NPY
+Tril,TRIL
+Grid2,GRID2
+Ndnf,NDNF
+Smyd1,SMYD1
+Cd8a,CD8A
+Capg,CAPG
+Tacr1,TACR1
+Aldh1l1,ALDH1L1
+Grip2,GRIP2
+Tafa1,TAFA1
+Grm7,GRM7
+Slc6a1,SLC6A1
+Zfand4,ZFAND4
+Ninj2,NINJ2
+Cd163,CD163
+Cd4,CD4
+Tnfrsf1a,TNFRSF1A
+Klrb1,KLRB1
+Cd69,CD69
+Clec9a,CLEC9A
+Mgst1,MGST1
+Abcc9,ABCC9
+Itpr2,ITPR2
+Ttyh1,TTYH1
+Hif3a,HIF3A
+Fosb,FOSB
+Apoe,APOE
+Tgfb1,TGFB1
+Psenen,PSENEN
+Cd22,CD22
+Mag,MAG
+Nkg7,NKG7
+Klk8,KLK8
+Klk6,KLK6
+Slc17a7,SLC17A7
+Slc6a5,SLC6A5
+Slc17a6,SLC17A6
+Luzp2,LUZP2
+Siglech,SIGLECH
+Pcsk6,PCSK6
+Lrrk1,LRRK1
+Nr2f2,NR2F2
+Mctp2,MCTP2
+Slco3a1,SLCO3A1
+Idh2,IDH2
+Cemip,CEMIP
+Trim34a,TRIM34A
+Lyve1,LYVE1
+Spon1,SPON1
+Rras2,RRAS2
+Sox6,SOX6
+Crym,CRYM
+Hs3st2,HS3ST2
+Hs3st4,HS3ST4
+Lat,LAT
+Cd19,CD19
+Sult1a1,SULT1A1
+Coro1a,CORO1A
+Itgam,ITGAM
+Itgax,ITGAX
+Rgs10,RGS10
+Fgfr2,FGFR2
+Mki67,MKI67
+C030029H02Rik,C030029H02RIK
+Ifitm3,IFITM3
+Irf7,IRF7
+Th,TH
+Tssc4,TSSC4
+Cdkn1c,CDKN1C
+Stxbp2,STXBP2
+Cd209a,CD209A
+Myo16,MYO16
+Csmd1,CSMD1
+Ank1,ANK1
+Zmat4,ZMAT4
+Ido1,IDO1
+Nrg1,NRG1
+Dlc1,DLC1
+Tlr3,TLR3
+Galntl6,GALNTL6
+Spock3,SPOCK3
+Tll1,TLL1
+Npy1r,NPY1R
+Il12rb1,IL12RB1
+Arrdc2,ARRDC2
+Klf2,KLF2
+Dnajb1,DNAJB1
+Mt2,MT2
+Mt1,MT1
+Nlrc5,NLRC5
+Pllp,PLLP
+Ccl17,CCL17
+Cdh1,CDH1
+Sntb2,SNTB2
+Atp2c2,ATP2C2
+Irf8,IRF8
+Tubb3,TUBB3
+Nrp1,NRP1
+Pdgfd,PDGFD
+Birc2,BIRC2
+Trpc6,TRPC6
+Cntn5,CNTN5
+Panx1,PANX1
+Anln,ANLN
+Kirrel3,KIRREL3
+Mcam,MCAM
+Cd3g,CD3G
+Cspg4,CSPG4
+Thsd4,THSD4
+Ccnb2,CCNB2
+Fam214a,FAM214A
+Col12a1,COL12A1
+Filip1,FILIP1
+Ctsh,CTSH
+Clstn2,CLSTN2
+Cspg5,CSPG5
+Cx3cr1,CX3CR1
+Mobp,MOBP
+Cck,CCK
+Hhatl,HHATL
+Ccr2,CCR2
+Vip,VIP
+Cnksr3,CNKSR3
+Samd5,SAMD5
+Sgk1,SGK1
+Eya4,EYA4
+Lama2,LAMA2
+Themis,THEMIS
+Nkain2,NKAIN2
+Lama4,LAMA4
+Fyn,FYN
+Cd24a,CD24A
+Ros1,ROS1
+Gja1,GJA1
+Unc5b,UNC5B
+Ctnna3,CTNNA3
+Egr2,EGR2
+Cdk1,CDK1
+Pcdh15,PCDH15
+S100b,S100B
+Itgb2,ITGB2
+Hcn2,HCN2
+Btbd11,BTBD11
+Gas2l3,GAS2L3
+Dcn,DCN
+Otogl,OTOGL
+Trhde,TRHDE
+Tph2,TPH2
+Grip1,GRIP1
+Wif1,WIF1
+B4galnt1,B4GALNT1
+Lrp1,LRP1
+Erbb3,ERBB3
+Tespa1,TESPA1
+Igfbp3,IGFBP3
+Egfr,EGFR
+Slit3,SLIT3
+Tenm2,TENM2
+Adra1b,ADRA1B
+Ebf1,EBF1
+Irgm1,IRGM1
+Fstl4,FSTL4
+Igtp,IGTP
+Irgm2,IRGM2
+Adora2b,ADORA2B
+Pmp22,PMP22
+Cd68,CD68
+Itgae,ITGAE
+Vtn,VTN
+Asic2,ASIC2
+Ccl2,CCL2
+Ccl5,CCL5
+Ccl4,CCL4
+Wfdc18,WFDC18
+Tbx21,TBX21
+Top2a,TOP2A
+Igfbp4,IGFBP4
+Ccr7,CCR7
+Stat3,STAT3
+Cavin1,CAVIN1
+Brca1,BRCA1
+Gfap,GFAP
+Pecam1,PECAM1
+Sox9,SOX9
+2610035D17Rik,2610035D17RIK
+Rab37,RAB37
+Rbfox3,RBFOX3
+Cbr2,CBR2
+Sdc1,SDC1
+Sox11,SOX11
+Slc26a4,SLC26A4
+Stxbp6,STXBP6
+Npas3,NPAS3
+Kcnh5,KCNH5
+Rgs6,RGS6
+Psen1,PSEN1
+Fos,FOS
+Serpina3c,SERPINA3C
+Serpina3f,SERPINA3F
+Serpina3i,SERPINA3I
+Serpina3m,SERPINA3M
+Serpina3n,SERPINA3N
+Meg3,MEG3
+Crip1,CRIP1
+Ighm,IGHM
+Itgb8,ITGB8
+Pou6f2,POU6F2
+Sox4,SOX4
+Nrn1,NRN1
+Ly86,LY86
+Elovl2,ELOVL2
+Cd83,CD83
+Rnf144b,RNF144B
+Sema4d,SEMA4D
+Cxcl14,CXCL14
+Tgfbi,TGFBI
+Adamts16,ADAMTS16
+Pcsk1,PCSK1
+Vcan,VCAN
+Crhbp,CRHBP
+Sv2c,SV2C
+Hexb,HEXB
+Cartpt,CARTPT
+Gzma,GZMA
+Hcn1,HCN1
+Fam107a,FAM107A
+Fezf2,FEZF2
+Synpr,SYNPR
+Cacna2d3,CACNA2D3
+Cacna1d,CACNA1D
+Stab1,STAB1
+Chat,CHAT
+Anxa8,ANXA8
+Sncg,SNCG
+Bmp4,BMP4
+Rnase4,RNASE4
+Trac,TRAC
+Adra1a,ADRA1A
+Htr2a,HTR2A
+9630013A20Rik,9630013A20RIK
+Dach1,DACH1
+Ednrb,EDNRB
+Gpc5,GPC5
+Cldn10,CLDN10
+Hs6st3,HS6ST3
+Rap2a,RAP2A
+Gpr183,GPR183
+Selenop,SELENOP
+Il7r,IL7R
+Adamts12,ADAMTS12
+Pdzd2,PDZD2
+Cdh6,CDH6
+Cdh12,CDH12
+Fbxl7,FBXL7
+Sema5a,SEMA5A
+Angpt1,ANGPT1
+Rspo2,RSPO2
+Sntb1,SNTB1
+Arc,ARC
+Pvalb,PVALB
+Lgals1,LGALS1
+Sox10,SOX10
+Scube1,SCUBE1
+Fbln1,FBLN1
+Lrrk2,LRRK2
+Nr4a1,NR4A1
+Rbfox1,RBFOX1
+Grin2a,GRIN2A
+Ciita,CIITA
+Serpind1,SERPIND1
+Dgkg,DGKG
+Sst,SST
+Fgf12,FGF12
+Hes1,HES1
+Apod,APOD
+Mylk,MYLK
+Cd86,CD86
+Zdhhc23,ZDHHC23
+Phldb2,PHLDB2
+Robo1,ROBO1
+Robo2,ROBO2
+Chodl,CHODL
+App,APP
+Olig2,OLIG2
+Olig1,OLIG1
+Slc9a3r2,SLC9A3R2
+Sox8,SOX8
+Dusp1,DUSP1
+Cdkn1a,CDKN1A
+H2-K1,H2-K1
+H2-DMa,H2-DMA
+Psmb9,PSMB9
+Tap1,TAP1
+Psmb8,PSMB8
+H2-Ab1,H2-AB1
+H2-Aa,H2-AA
+H2-Eb1,H2-EB1
+C4b,C4B
+Hspa1a,HSPA1A
+Aif1,AIF1
+H2-D1,H2-D1
+Ddr1,DDR1
+H2-T23,H2-T23
+Mog,MOG
+Ptchd4,PTCHD4
+Trem2,TREM2
+Rftn1,RFTN1
+Slc5a7,SLC5A7
+Plin4,PLIN4
+L3mbtl4,L3MBTL4
+Alk,ALK
+Lbh,LBH
+Jcad,JCAD
+Aqp4,AQP4
+Ttr,TTR
+Rit2,RIT2
+Syt4,SYT4
+Egr1,EGR1
+Cd14,CD14
+Lox,LOX
+Cd74,CD74
+Pdgfrb,PDGFRB
+Onecut2,ONECUT2
+Dcc,DCC
+Myo5b,MYO5B
+Slc14a1,SLC14A1
+Mbp,MBP
+Cndp1,CNDP1
+Cbln2,CBLN2
+Myrf,MYRF
+Ms4a1,MS4A1
+Ms4a7,MS4A7
+Rorb,RORB
+Anxa1,ANXA1
+Cemip2,CEMIP2
+Rfx3,RFX3
+Slc1a1,SLC1A1
+Il33,IL33
+Prkg1,PRKG1
+Ifit1,IFIT1
+Plce1,PLCE1
+Opalin,OPALIN
+Hpse2,HPSE2
+Slk,SLK
+Sorcs3,SORCS3
+Sorcs1,SORCS1
+Atrnl1,ATRNL1
+Foxp3,FOXP3
+Gpr34,GPR34
+Tmem255a,TMEM255A
+Tenm1,TENM1
+Fgf13,FGF13
+Gjb1,GJB1
+Tspan6,TSPAN6
+Trpc5,TRPC5
+Htr2c,HTR2C
+Frmpd4,FRMPD4

data/prepared_sources/spinal_gse190442/source_manifest.json

@@ -0,0 +1,15 @@
+{
+  "counts_path": "counts.mtx",
+  "obs_path": "obs.csv",
+  "var_path": "var.csv",
+  "count_orientation": "cell_by_gene",
+  "cell_id_col": "cell_id",
+  "feature_id_col": "feature_id",
+  "n_obs": 50000,
+  "n_vars": 535,
+  "source_id": "spinal_gse190442",
+  "species": "human",
+  "family_use": "nb",
+  "label_column": "native_label",
+  "sample_column": "sample_id"
+}

data/prepared_sources/spinal_gse190442/var.csv

@@ -0,0 +1,536 @@
+feature_id
+KAZN
+ID3
+CLIC4
+CD52
+GRIK3
+MYCL
+NFIA
+CACHD1
+PDE4B
+GBP7
+PALMD
+COL11A1
+NTNG1
+C1ORF162
+TMIGD3
+OLFML3
+CD2
+FCGR1A
+CTSS
+S100A10
+S100A4
+THBS3
+HAPLN2
+BCAN
+NES
+NCSTN
+CD48
+FCER1G
+FCGR3A
+FCGR2B
+DDR2
+MGST3
+MPZL1
+PRRX1
+FASLG
+TNR
+GLUL
+RGS16
+BRINP3
+PTPRC
+GPR37L1
+BTG2
+CNTN2
+BATF3
+PROX1
+CENPF
+TGFB2
+PSEN2
+PLD5
+SOX11
+SDC1
+ALK
+LBH
+TACR1
+CAPG
+GNLY
+CD8A
+SMYD1
+MAL
+KCNIP3
+SLC5A7
+MERTK
+CNTNAP5
+TMEM163
+CXCR4
+THSD7B
+NXPH2
+LRP1B
+LYPD6B
+LYPD6
+NR4A2
+ERMN
+CYTIP
+IFIH1
+NOSTRIN
+GAD1
+DLX1
+ZNF804A
+CALCRL
+STAT1
+SATB2
+CTLA4
+ICOS
+IDH1
+VWC2L
+IGFBP5
+SERPINE2
+SPHKAP
+DNER
+EFHD1
+SH3BP4
+PDCD1
+GRM7
+SLC6A1
+GRIP2
+RFTN1
+ZNF385D
+CX3CR1
+MOBP
+CCK
+HHATL
+CCR2
+CSPG5
+STAB1
+CACNA1D
+CACNA2D3
+FAM107A
+FEZF2
+SYNPR
+ROBO2
+ROBO1
+PHLDB2
+ZDHHC23
+CD86
+MYLK
+ALDH1L1
+CLSTN2
+P2RY13
+P2RY12
+PLCH1
+ZBBX
+CLDN11
+NLGN1
+KCNMB2
+PEX5L
+SOX2
+DGKG
+SST
+FGF12
+HES1
+APOD
+FGFR3
+RGS12
+STK32B
+QDPR
+KCNIP4
+NWD2
+PDGFRA
+KIT
+HOPX
+NPFFR2
+ADAMTS3
+PF4
+PLAC8
+ARHGAP24
+MEPE
+GRID2
+NPNT
+ETNPPL
+COL25A1
+UGT8
+NDST4
+NDNF
+ANXA5
+SFRP2
+RXFP1
+NPY1R
+TLL1
+SPOCK3
+GALNTL6
+TLR3
+ADAMTS16
+SEMA5A
+FBXL7
+CDH12
+CDH6
+PDZD2
+ADAMTS12
+IL7R
+SELENOP
+HCN1
+GZMA
+CARTPT
+HEXB
+SV2C
+CRHBP
+VCAN
+PCSK1
+LOX
+FSTL4
+CXCL14
+TGFBI
+EGR1
+CD14
+PDGFRB
+CD74
+EBF1
+ADRA1B
+TENM2
+SLIT3
+DUSP1
+NRN1
+LY86
+ELOVL2
+CD83
+RNF144B
+SOX4
+MOG
+DDR1
+AIF1
+HSPA1A
+C4B
+HLA-DQA1
+PSMB8
+PSMB9
+TAP1
+HLA-DMB
+CDKN1A
+TREM2
+PTCHD4
+COL12A1
+FILIP1
+FYN
+LAMA4
+ROS1
+GJA1
+NKAIN2
+THEMIS
+LAMA2
+EYA4
+SGK1
+SAMD5
+VIP
+CNKSR3
+RNASET2
+SDK1
+ITGB8
+GPNMB
+NPY
+TRIL
+ANLN
+POU6F2
+IGFBP3
+EGFR
+CD36
+SEMA3E
+GRM3
+GNG11
+TAC1
+RELN
+SLC26A4
+FOXP2
+CAV1
+PTPRZ1
+GRM8
+PRRT4
+HILPDA
+TRBC1
+TMEM176A
+FRMPD4
+GPR34
+FOXP3
+GJB1
+TSPAN6
+TRPC5
+HTR2C
+TMEM255A
+TENM1
+FGF13
+CSMD1
+DLC1
+ADRA1A
+NRG1
+IDO1
+ZMAT4
+ANK1
+ST18
+RGS20
+TOX
+VXN
+SULF1
+STMN2
+CALB1
+ANGPT1
+RSPO2
+SNTB1
+ARC
+RFX3
+SLC1A1
+IL33
+ADAMTSL1
+SLC24A2
+ANKRD18A
+CNTNAP3B
+CEMIP2
+ANXA1
+RORB
+SEMA4D
+GRIN3A
+KLF4
+ASTN2
+LHX6
+PHYHD1
+NTNG2
+NOTCH1
+PTGDS
+IFITM3
+IRF7
+TH
+TSSC4
+CDKN1C
+LYVE1
+SPON1
+RRAS2
+SOX6
+SLC6A5
+SLC17A6
+LUZP2
+ANO3
+PAX6
+CD44
+PRR5L
+LRRC4C
+SPI1
+SERPING1
+MS4A6A
+MS4A7
+MS4A1
+MYRF
+PANX1
+CNTN5
+TRPC6
+BIRC2
+PDGFD
+CD3G
+MCAM
+KIRREL3
+ITIH5
+FRMD4A
+FAM107B
+ITGA8
+C1QL3
+MRC1
+KIAA1217
+GAD2
+JCAD
+NRP1
+ZFAND4
+ANXA8
+CHAT
+PRKG1
+PCDH15
+CDK1
+EGR2
+CTNNA3
+UNC5B
+SNCG
+IFIT1
+PLCE1
+OPALIN
+HPSE2
+SLK
+SORCS3
+SORCS1
+ATRNL1
+RGS10
+FGFR2
+MKI67
+NINJ2
+TNFRSF1A
+CD4
+CD163
+KLRB1
+CD69
+CLEC9A
+MGST1
+ABCC9
+ITPR2
+LRRK2
+NR4A1
+TESPA1
+ERBB3
+LRP1
+B4GALNT1
+WIF1
+GRIP1
+TPH2
+TRHDE
+OTOGL
+DCN
+GAS2L3
+BTBD11
+TMEM119
+SELPLG
+CUX2
+RPH3A
+NOS1
+TMEM132C
+TMEM132D
+FLT3
+FLT1
+DCLK1
+CCNA1
+POSTN
+HTR2A
+DACH1
+EDNRB
+GPC5
+CLDN10
+HS6ST3
+RAP2A
+GPR183
+MYO16
+RNASE4
+TRAC
+STXBP6
+NPAS3
+BMP4
+KCNH5
+RGS6
+PSEN1
+FOS
+SERPINA3
+MEG3
+CRIP1
+IGHM
+RYR3
+MEIS2
+RASGRP1
+THBS1
+CAPN3
+B2M
+SEMA6D
+FAM214A
+CCNB2
+THSD4
+CSPG4
+CTSH
+CEMIP
+IDH2
+SLCO3A1
+MCTP2
+NR2F2
+LRRK1
+PCSK6
+SOX8
+SLC9A3R2
+RBFOX1
+GRIN2A
+CIITA
+CRYM
+HS3ST2
+HS3ST4
+SULT1A1
+CD19
+LAT
+CORO1A
+ITGAM
+ITGAX
+NLRC5
+PLLP
+CCL17
+CDH1
+SNTB2
+ATP2C2
+IRF8
+TUBB3
+ITGAE
+CD68
+TP53
+PMP22
+ADORA2B
+VTN
+ASIC2
+CCL2
+CCL5
+CCL4
+TOP2A
+IGFBP4
+CCR7
+STAT3
+CAVIN1
+BRCA1
+GFAP
+TBX21
+CA10
+PECAM1
+SOX9
+RAB37
+RBFOX3
+L3MBTL4
+AQP4
+TTR
+RIT2
+SYT4
+SLC14A1
+MYO5B
+DCC
+ONECUT2
+CBLN2
+CNDP1
+MBP
+SIGLEC1
+PCNA
+PLCB1
+LAMP5
+SLC24A3
+NNAT
+SLC32A1
+TSHZ2
+ZBP1
+CDH4
+COL20A1
+ZBTB46
+HCN2
+PLIN4
+STXBP2
+DNAJB1
+KLF2
+ARRDC2
+IL12RB1
+MAG
+CD22
+PSENEN
+TGFB1
+APOE
+FOSB
+HIF3A
+SLC17A7
+KLK6
+KLK8
+NKG7
+TTYH1
+SERPIND1
+PVALB
+LGALS1
+SOX10
+SCUBE1
+FBLN1
+CHODL
+APP
+OLIG2
+OLIG1
+ITGB2
+S100B

external/SEA-AD/MJM/data/merfish_gene_names.json

@@ -0,0 +1,142 @@
+[
+  "CD22",
+  "MEIS2",
+  "TLL1",
+  "SV2C",
+  "KIAA1217",
+  "CNTN5",
+  "ADAMTS3",
+  "LRRC4C",
+  "TACR1",
+  "CALB1",
+  "GALNTL6",
+  "DCC",
+  "NPAS3",
+  "ZNF804A",
+  "NTNG2",
+  "NOS1",
+  "ETNPPL",
+  "ATRNL1",
+  "GRM8",
+  "ZMAT4",
+  "NKAIN2",
+  "GRIN2A",
+  "PAX6",
+  "LRP1B",
+  "TOX",
+  "ASIC2",
+  "HTR2C",
+  "ROBO2",
+  "PDZD2",
+  "PEX5L",
+  "TNR",
+  "CACNA2D3",
+  "VIP",
+  "DCLK1",
+  "GRIP1",
+  "ITGA8",
+  "CD74",
+  "PLD5",
+  "NLGN1",
+  "LAMP5",
+  "ASTN2",
+  "DLX1",
+  "KCNMB2",
+  "GRID2",
+  "FGF13",
+  "GRIK3",
+  "RGS6",
+  "RBFOX3",
+  "SATB2",
+  "CUX2",
+  "PRRT4",
+  "ANK1",
+  "SLC32A1",
+  "TSPAN6",
+  "GRIP2",
+  "GAD2",
+  "CBLN2",
+  "TAFA1",
+  "KCNIP4",
+  "CA10",
+  "CSMD1",
+  "CNTNAP5",
+  "FRMPD4",
+  "HS6ST3",
+  "SORCS1",
+  "TENM2",
+  "CLSTN2",
+  "ROBO1",
+  "ZNF385D",
+  "COL11A1",
+  "SLC24A2",
+  "PDE4B",
+  "RBFOX1",
+  "GRIN3A",
+  "SEMA6D",
+  "TMEM132D",
+  "ITGB8",
+  "PLCB1",
+  "SORCS3",
+  "STXBP6",
+  "GRM7",
+  "HCN1",
+  "RFX3",
+  "GPC5",
+  "EBF1",
+  "UNC5B",
+  "CARTPT",
+  "HTR2A",
+  "LAMA4",
+  "RGS12",
+  "CACHD1",
+  "EGFR",
+  "SLIT3",
+  "NFIA",
+  "LUZP2",
+  "CHODL",
+  "NPY",
+  "DLC1",
+  "L3MBTL4",
+  "MOG",
+  "NXPH2",
+  "SULF1",
+  "NDNF",
+  "FOXP2",
+  "KAZN",
+  "NRG1",
+  "HS3ST2",
+  "TMEM255A",
+  "RORB",
+  "DCN",
+  "KIRREL3",
+  "CDH6",
+  "SCUBE1",
+  "FEZF2",
+  "PDGFD",
+  "TH",
+  "PRKG1",
+  "FBXL7",
+  "EYA4",
+  "RYR3",
+  "SOX6",
+  "HPSE2",
+  "FGF12",
+  "DGKG",
+  "SLC14A1",
+  "SNTB1",
+  "LRRK1",
+  "LHX6",
+  "CTSS",
+  "NOSTRIN",
+  "ID3",
+  "SMYD1",
+  "SEMA3E",
+  "TSHZ2",
+  "PALMD",
+  "DACH1",
+  "THEMIS",
+  "PRRX1",
+  "PVALB",
+  "ADAMTSL1"
+]

external/SEA-AD/MJM/src/engines/fm_trainer_kukanja.py

@@ -0,0 +1,191 @@
+"""
+N-level FM training engine for Kukanja data.
+Adapts ScGPTTrainerEngine for:
+  - N classification levels (instead of hardcoded 3)
+  - Kukanja dataloader format (y_{name} keys, no confidence weighting)
+  - No reconstruction loss
+"""
+import os
+import time
+import numpy as np
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
+
+
+class FMTrainerKukanja:
+    """N-level foundation model trainer for Kukanja datasets."""
+
+    def __init__(self, model, optimizer, scheduler, device, logger, args,
+                 label_names=None, output_num=None):
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.device = device
+        self.logger = logger
+        self.args = args
+        self.best_val_loss = float('inf')
+        self.label_names = label_names or args.label_names
+        self.output_num = output_num or args.output_num
+        self.n_levels = len(self.label_names)
+
+    def compute_loss(self, batch_data):
+        X = batch_data['X'].to(self.device)
+        y_list = [batch_data[f'y_{name}'].to(self.device) for name in self.label_names]
+
+        logits_list, cell_emb = self.model(X)
+
+        total_loss = torch.tensor(0.0, device=self.device)
+        level_losses = {}
+        for i, name in enumerate(self.label_names):
+            loss_i = F.cross_entropy(logits_list[i], y_list[i])
+            level_losses[name] = loss_i.item()
+            total_loss = total_loss + loss_i
+
+        metrics = {'total': total_loss.item()}
+        metrics.update(level_losses)
+        logits_dict = {name: logits_list[i] for i, name in enumerate(self.label_names)}
+        return total_loss, metrics, logits_dict, cell_emb
+
+    def train_epoch(self, dataloader):
+        self.model.train()
+        keys = ['total'] + list(self.label_names)
+        agg = {k: 0.0 for k in keys}
+        for batch_data in tqdm(dataloader, desc="Training"):
+            self.optimizer.zero_grad()
+            loss, metrics, _, _ = self.compute_loss(batch_data)
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=5.0)
+            self.optimizer.step()
+            for k in agg:
+                agg[k] += metrics[k]
+        return {k: v / len(dataloader) for k, v in agg.items()}
+
+    @torch.no_grad()
+    def eval_epoch(self, dataloader):
+        self.model.eval()
+        keys = ['total'] + list(self.label_names)
+        agg = {k: 0.0 for k in keys}
+        for batch_data in tqdm(dataloader, desc="Evaluating"):
+            _, metrics, _, _ = self.compute_loss(batch_data)
+            for k in agg:
+                agg[k] += metrics[k]
+        return {k: v / len(dataloader) for k, v in agg.items()}
+
+    def _fmt(self, m):
+        parts = [f"total:{m['total']:.4f}"]
+        for name in self.label_names:
+            parts.append(f"{name}:{m[name]:.4f}")
+        return " ".join(parts)
+
+    def train(self, train_loader, val_loader):
+        os.makedirs(self.args.save_dir, exist_ok=True)
+        best_path = os.path.join(self.args.save_dir, 'best_model.pth')
+        patience_counter = 0
+        best_val_m = None
+
+        if os.path.exists(best_path):
+            self.model.load_state_dict(torch.load(best_path, map_location=self.device))
+            self.logger.info(f"[Resume] Loaded checkpoint from {best_path}")
+
+        for epoch in range(1, self.args.max_epochs + 1):
+            t0 = time.time()
+            train_m = self.train_epoch(train_loader)
+            val_m = self.eval_epoch(val_loader)
+            self.scheduler.step()
+
+            self.logger.info(
+                f"Epoch [{epoch:03d}/{self.args.max_epochs}] "
+                f"Time:{time.time()-t0:.1f}s LR:{self.scheduler.get_last_lr()[0]:.2e}"
+            )
+            self.logger.info(f"  [Train] {self._fmt(train_m)}")
+            self.logger.info(f"  [Val]   {self._fmt(val_m)}")
+            if best_val_m:
+                self.logger.info(f"  [Best]  {self._fmt(best_val_m)}")
+
+            if val_m['total'] < self.best_val_loss:
+                self.best_val_loss = val_m['total']
+                best_val_m = val_m
+                patience_counter = 0
+                torch.save(self.model.state_dict(), best_path)
+                self.logger.info("Best model saved!")
+            else:
+                patience_counter += 1
+                if patience_counter >= self.args.patience:
+                    self.logger.info("Early stopping triggered!")
+                    break
+
+    @torch.no_grad()
+    def test(self, test_loader):
+        self.logger.info("\nLoading best model for test...")
+        best_path = os.path.join(self.args.save_dir, 'best_model.pth')
+        self.model.load_state_dict(torch.load(best_path, map_location=self.device))
+        self.model.eval()
+
+        keys = ['total'] + list(self.label_names)
+        agg = {k: 0.0 for k in keys}
+        all_true = {t: [] for t in self.label_names}
+        all_pred = {t: [] for t in self.label_names}
+        all_prob = {t: [] for t in self.label_names}
+        res = {'cell_emb': [], 'spatial': [], 'batch': []}
+
+        for batch_data in test_loader:
+            _, metrics, logits_dict, cell_emb = self.compute_loss(batch_data)
+            for k in agg:
+                agg[k] += metrics[k]
+            res['cell_emb'].append(cell_emb.cpu())
+            res['spatial'].append(batch_data['spatial'].cpu())
+            res['batch'].append(batch_data['batch_id'].cpu())
+
+            for t in self.label_names:
+                all_true[t].append(batch_data[f'y_{t}'].cpu().numpy())
+                all_pred[t].append(logits_dict[t].argmax(-1).cpu().numpy())
+                all_prob[t].append(F.softmax(logits_dict[t], -1).cpu().numpy())
+
+        test_m = {k: v / len(test_loader) for k, v in agg.items()}
+        self.logger.info(f"  [Test] {self._fmt(test_m)}")
+
+        final_true = {k: np.concatenate(v) for k, v in all_true.items()}
+        final_pred = {k: np.concatenate(v) for k, v in all_pred.items()}
+        final_prob = {k: np.concatenate(v, axis=0) for k, v in all_prob.items()}
+
+        # Classification metrics
+        self.logger.info("========== Test Classification Metrics ==========")
+        for task in self.label_names:
+            mp, mr, mf, _ = precision_recall_fscore_support(
+                final_true[task], final_pred[task], average='macro', zero_division=0)
+            _, _, mif, _ = precision_recall_fscore_support(
+                final_true[task], final_pred[task], average='micro', zero_division=0)
+            pp, pr, _, sup = precision_recall_fscore_support(
+                final_true[task], final_pred[task], average=None, zero_division=0)
+            self.logger.info(f"[{task.upper()}] Macro  - P:{mp:.4f} R:{mr:.4f} F1:{mf:.4f}")
+            self.logger.info(f"[{task.upper()}] Micro  - F1:{mif:.4f}")
+            parts = [f"C{i}(S={sup[i]}):P={pp[i]:.4f}/R={pr[i]:.4f}"
+                     for i in range(len(pp)) if sup[i] > 0]
+            self.logger.info(f"[{task.upper()}] Per-class: {' | '.join(parts)}")
+            self.logger.info("-" * 50)
+
+        # AUC-ROC
+        self.logger.info("========== Test AUC-ROC Metrics ==========")
+        for i, task in enumerate(self.label_names):
+            y_t, y_p = final_true[task], final_prob[task]
+            y_oh = np.zeros_like(y_p)
+            y_oh[np.arange(len(y_t)), y_t] = 1
+            mi_auc = roc_auc_score(y_oh.ravel(), y_p.ravel())
+            va = []
+            for c in range(self.output_num[i]):
+                b = (y_t == c).astype(int)
+                if len(np.unique(b)) == 2:
+                    va.append(roc_auc_score(b, y_p[:, c]))
+            ma_auc = np.mean(va) if va else 0.0
+            self.logger.info(
+                f"[{task.upper()}] AUC-ROC - Macro:{ma_auc:.4f} Micro:{mi_auc:.4f} "
+                f"(Valid:{len(va)}/{self.output_num[i]})")
+            self.logger.info("-" * 50)
+
+        res_np = {k: torch.cat(v, 0).numpy() for k, v in res.items()}
+        out_path = os.path.join(self.args.save_dir, 'test_features.npz')
+        np.savez_compressed(out_path, **res_np)
+        self.logger.info(f"Features saved: {out_path}")
+        return res_np

external/SEA-AD/MJM/src/engines/heist_trainer.py

@@ -0,0 +1,44 @@
+"""
+Training engine for HEIST cell-type annotation.
+
+Extends ScGPTTrainerEngine to pass spatial coordinates and batch_id
+to the model's forward method, which needs them for on-the-fly graph
+construction.
+"""
+import torch
+import torch.nn.functional as F
+from src.engines.scgpt_trainer import ScGPTTrainerEngine
+
+
+class HEISTTrainerEngine(ScGPTTrainerEngine):
+
+    def compute_loss(self, batch_data):
+        X          = batch_data["X"].to(self.device)
+        spatial    = batch_data["spatial"].to(self.device)
+        batch_id   = batch_data["batch_id"].to(self.device)
+        y_c        = batch_data["y_class"].to(self.device)
+        y_sc       = batch_data["y_subclass"].to(self.device)
+        y_st       = batch_data["y_supertype"].to(self.device)
+        confidence = batch_data["confidence"].to(self.device)
+
+        logits, cell_emb = self.model(X, spatial=spatial, batch_id=batch_id)
+        logit_c, logit_sc, logit_st = logits
+
+        loss_c  = F.cross_entropy(logit_c, y_c)
+        loss_sc = F.cross_entropy(logit_sc, y_sc)
+        loss_st = (F.cross_entropy(logit_st, y_st, reduction="none") * confidence).mean()
+
+        total_loss = loss_c + loss_sc + loss_st
+
+        metrics = {
+            "total":     total_loss.item(),
+            "class":     loss_c.item(),
+            "subclass":  loss_sc.item(),
+            "supertype": loss_st.item(),
+        }
+        logits_dict = {
+            "class":     logit_c,
+            "subclass":  logit_sc,
+            "supertype": logit_st,
+        }
+        return total_loss, metrics, logits_dict, cell_emb

external/SEA-AD/MJM/src/engines/scgpt_trainer.py

@@ -0,0 +1,286 @@
+"""
+Training engine for scGPT cell-type annotation.
+
+Differences from MJM TrainerEngine:
+  - No reconstruction loss (scGPT is classification-only here).
+  - Model forward: (logits, cell_emb)  vs MJM's (recon_X, logits, z).
+  - val / eval uses torch.no_grad() (pure evaluation, no gradient step).
+  - test saves cell_emb as the latent representation.
+"""
+import os
+import time
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
+
+
+class ScGPTTrainerEngine:
+    def __init__(self, model, optimizer, scheduler, device, logger, args):
+        self.model     = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.device    = device
+        self.logger    = logger
+        self.args      = args
+        self.best_val_loss = float("inf")
+
+    # ── loss ─────────────────────────────────────────────────────────────────
+
+    def compute_loss(self, batch_data):
+        X          = batch_data["X"].to(self.device)
+        y_c        = batch_data["y_class"].to(self.device)
+        y_sc       = batch_data["y_subclass"].to(self.device)
+        y_st       = batch_data["y_supertype"].to(self.device)
+        confidence = batch_data["confidence"].to(self.device)
+
+        logits, cell_emb = self.model(X)
+        logit_c, logit_sc, logit_st = logits
+
+        loss_c  = F.cross_entropy(logit_c,  y_c)
+        loss_sc = F.cross_entropy(logit_sc, y_sc)
+
+        # confidence-weighted supertype loss (same as MJM)
+        loss_st = (F.cross_entropy(logit_st, y_st, reduction="none") * confidence).mean()
+
+        total_loss = loss_c + loss_sc + loss_st
+
+        metrics = {
+            "total":     total_loss.item(),
+            "class":     loss_c.item(),
+            "subclass":  loss_sc.item(),
+            "supertype": loss_st.item(),
+        }
+        logits_dict = {
+            "class":     logit_c,
+            "subclass":  logit_sc,
+            "supertype": logit_st,
+        }
+        return total_loss, metrics, logits_dict, cell_emb
+
+    # ── one epoch ────────────────────────────────────────────────────────────
+
+    def train_epoch(self, dataloader):
+        self.model.train()
+        agg = {"total": 0.0, "class": 0.0, "subclass": 0.0, "supertype": 0.0}
+
+        for batch_data in tqdm(dataloader, desc="Training"):
+            self.optimizer.zero_grad()
+            loss, metrics, _, _ = self.compute_loss(batch_data)
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=5.0)
+            self.optimizer.step()
+            for k in agg:
+                agg[k] += metrics[k]
+
+        return {k: v / len(dataloader) for k, v in agg.items()}
+
+    @torch.no_grad()
+    def eval_epoch(self, dataloader):
+        self.model.eval()
+        agg = {"total": 0.0, "class": 0.0, "subclass": 0.0, "supertype": 0.0}
+
+        for batch_data in tqdm(dataloader, desc="Evaluating"):
+            _, metrics, _, _ = self.compute_loss(batch_data)
+            for k in agg:
+                agg[k] += metrics[k]
+
+        return {k: v / len(dataloader) for k, v in agg.items()}
+
+    # ── full training loop ───────────────────────────────────────────────────
+
+    def train(self, train_loader, val_loader):
+        os.makedirs(self.args.save_dir, exist_ok=True)
+        best_model_path   = os.path.join(self.args.save_dir, "best_model.pth")
+        patience_counter  = 0
+        best_val_metrics  = None
+
+        # Resume from existing checkpoint if available
+        if os.path.exists(best_model_path):
+            self.model.load_state_dict(
+                torch.load(best_model_path, map_location=self.device)
+            )
+            self.logger.info(f"[Resume] Loaded checkpoint from {best_model_path}")
+
+        for epoch in range(1, self.args.max_epochs + 1):
+            t0 = time.time()
+            train_m = self.train_epoch(train_loader)
+            val_m   = self.eval_epoch(val_loader)
+            self.scheduler.step()
+
+            self.logger.info(
+                f"Epoch [{epoch:03d}/{self.args.max_epochs:03d}] | "
+                f"Time: {time.time()-t0:.1f}s | "
+                f"LR: {self.scheduler.get_last_lr()[0]:.2e}"
+            )
+            self.logger.info(
+                f"  [Train] Total: {train_m['total']:.4f} | "
+                f"Class: {train_m['class']:.4f} | "
+                f"Subclass: {train_m['subclass']:.4f} | "
+                f"Supertype: {train_m['supertype']:.4f}"
+            )
+            self.logger.info(
+                f"  [Val]   Total: {val_m['total']:.4f} | "
+                f"Class: {val_m['class']:.4f} | "
+                f"Subclass: {val_m['subclass']:.4f} | "
+                f"Supertype: {val_m['supertype']:.4f}"
+            )
+            if best_val_metrics is not None:
+                self.logger.info(
+                    f"  [Best Val] Total: {self.best_val_loss:.4f} | "
+                    f"Class: {best_val_metrics['class']:.4f} | "
+                    f"Subclass: {best_val_metrics['subclass']:.4f} | "
+                    f"Supertype: {best_val_metrics['supertype']:.4f}"
+                )
+
+            if val_m["total"] < self.best_val_loss:
+                self.best_val_loss = val_m["total"]
+                best_val_metrics   = val_m
+                patience_counter   = 0
+                torch.save(self.model.state_dict(), best_model_path)
+                self.logger.info("Best model saved!")
+            else:
+                patience_counter += 1
+                if patience_counter >= self.args.patience:
+                    self.logger.info("Early stopping triggered!")
+                    break
+
+    # ── test ─────────────────────────────────────────────────────────────────
+
+    @torch.no_grad()
+    def test(self, test_loader):
+        self.logger.info("\nLoading best model for test-set evaluation...")
+        best_model_path = os.path.join(self.args.save_dir, "best_model.pth")
+        self.model.load_state_dict(
+            torch.load(best_model_path, map_location=self.device)
+        )
+        self.model.eval()
+
+        res        = {"cell_emb": [], "spatial": [], "batch": [], "supertype": [], "cps": []}
+        agg        = {"total": 0.0, "class": 0.0, "subclass": 0.0, "supertype": 0.0}
+        all_y_true = {"class": [], "subclass": [], "supertype": []}
+        all_y_pred = {"class": [], "subclass": [], "supertype": []}
+        all_y_prob = {"class": [], "subclass": [], "supertype": []}
+
+        for batch_data in test_loader:
+            _, metrics, logits_dict, cell_emb = self.compute_loss(batch_data)
+
+            res["cell_emb"].append(cell_emb.cpu())
+            res["spatial"].append(batch_data["spatial"].cpu())
+            res["batch"].append(batch_data["batch_id"].cpu())
+            res["supertype"].append(batch_data["y_supertype"].cpu())
+            res["cps"].append(batch_data["cps"].cpu())
+
+            for k in agg:
+                agg[k] += metrics[k]
+
+            all_y_true["class"].append(batch_data["y_class"].cpu().numpy())
+            all_y_true["subclass"].append(batch_data["y_subclass"].cpu().numpy())
+            all_y_true["supertype"].append(batch_data["y_supertype"].cpu().numpy())
+
+            all_y_prob["class"].append(
+                F.softmax(logits_dict["class"], dim=-1).cpu().numpy()
+            )
+            all_y_prob["subclass"].append(
+                F.softmax(logits_dict["subclass"], dim=-1).cpu().numpy()
+            )
+            all_y_prob["supertype"].append(
+                F.softmax(logits_dict["supertype"], dim=-1).cpu().numpy()
+            )
+
+            all_y_pred["class"].append(
+                logits_dict["class"].argmax(dim=-1).cpu().numpy()
+            )
+            all_y_pred["subclass"].append(
+                logits_dict["subclass"].argmax(dim=-1).cpu().numpy()
+            )
+            all_y_pred["supertype"].append(
+                logits_dict["supertype"].argmax(dim=-1).cpu().numpy()
+            )
+
+        test_m = {k: v / len(test_loader) for k, v in agg.items()}
+        self.logger.info(
+            f"  [Test] Total: {test_m['total']:.4f} | "
+            f"Class: {test_m['class']:.4f} | "
+            f"Subclass: {test_m['subclass']:.4f} | "
+            f"Supertype: {test_m['supertype']:.4f}"
+        )
+
+        final_y_true = {k: np.concatenate(v)        for k, v in all_y_true.items()}
+        final_y_pred = {k: np.concatenate(v)        for k, v in all_y_pred.items()}
+        final_y_prob = {k: np.concatenate(v, axis=0) for k, v in all_y_prob.items()}
+
+        self.log_classification_metrics(final_y_true, final_y_pred, prefix="Test")
+        self.log_auc_roc_metrics(
+            final_y_true,
+            final_y_prob,
+            num_classes_dict={"class": 3, "subclass": 24, "supertype": 137},
+            prefix="Test",
+        )
+
+        res_np = {k: torch.cat(v, dim=0).numpy() for k, v in res.items()}
+        out_path = os.path.join(self.args.save_dir, "test_features.npz")
+        np.savez_compressed(out_path, **res_np)
+        self.logger.info(f"Features saved to: {out_path}")
+
+        return res_np
+
+    # ── metric helpers (identical to MJM TrainerEngine) ──────────────────────
+
+    def log_classification_metrics(self, y_true_dict, y_pred_dict, prefix="Test"):
+        self.logger.info(f"========== {prefix} Classification Metrics ==========")
+        for task in ["class", "subclass", "supertype"]:
+            y_true = y_true_dict[task]
+            y_pred = y_pred_dict[task]
+
+            macro_p, macro_r, macro_f1, _ = precision_recall_fscore_support(
+                y_true, y_pred, average="macro", zero_division=0
+            )
+            micro_p, micro_r, micro_f1, _ = precision_recall_fscore_support(
+                y_true, y_pred, average="micro", zero_division=0
+            )
+            per_p, per_r, _, support = precision_recall_fscore_support(
+                y_true, y_pred, average=None, zero_division=0
+            )
+
+            self.logger.info(
+                f"[{task.upper()}] Macro  - P: {macro_p:.4f} | R: {macro_r:.4f} | F1: {macro_f1:.4f}"
+            )
+            self.logger.info(
+                f"[{task.upper()}] Micro  - P: {micro_p:.4f} | R: {micro_r:.4f} | F1: {micro_f1:.4f}"
+            )
+
+            per_class_logs = [
+                f"C{i}(S={support[i]}): P={per_p[i]:.4f}/R={per_r[i]:.4f}"
+                for i in range(len(per_p))
+                if support[i] > 0 or per_p[i] > 0
+            ]
+            self.logger.info(f"[{task.upper()}] Per-class: {' | '.join(per_class_logs)}")
+            self.logger.info("-" * 50)
+
+    def log_auc_roc_metrics(self, y_true_dict, y_prob_dict, num_classes_dict, prefix="Test"):
+        self.logger.info(f"========== {prefix} AUC-ROC Metrics ==========")
+        for task in ["class", "subclass", "supertype"]:
+            y_true  = y_true_dict[task]
+            y_prob  = y_prob_dict[task]
+            num_c   = num_classes_dict[task]
+
+            y_true_onehot = np.zeros_like(y_prob)
+            y_true_onehot[np.arange(len(y_true)), y_true] = 1
+            micro_auc = roc_auc_score(y_true_onehot.ravel(), y_prob.ravel())
+
+            valid_aucs = []
+            for c in range(num_c):
+                binary = (y_true == c).astype(int)
+                if len(np.unique(binary)) == 2:
+                    valid_aucs.append(roc_auc_score(binary, y_prob[:, c]))
+            macro_auc = np.mean(valid_aucs) if valid_aucs else 0.0
+
+            self.logger.info(
+                f"[{task.upper()}] AUC-ROC - Macro: {macro_auc:.4f} | "
+                f"Micro: {micro_auc:.4f} | "
+                f"(Valid classes: {len(valid_aucs)}/{num_c})"
+            )
+            self.logger.info("-" * 50)

external/SEA-AD/MJM/src/engines/spatial_gnn_trainer.py

@@ -0,0 +1,226 @@
+"""
+Training engine for Spatial GNN cell-type annotation.
+
+Key differences from base TrainerEngine:
+  - Operates on full graph (not mini-batches) per donor subgraph
+  - Uses subgraph sampling: each batch is a donor's cells + their KNN graph
+  - Model forward: (recon, logits, z) with edge_index argument
+"""
+import os
+import time
+import numpy as np
+import torch
+import torch.nn.functional as F
+from tqdm import tqdm
+from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
+
+
+class SpatialGNNTrainerEngine:
+    def __init__(self, model, optimizer, scheduler, device, logger, args):
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.device = device
+        self.logger = logger
+        self.args = args
+        self.best_val_loss = float('inf')
+
+    def _normalize(self, x):
+        return torch.log1p(x)
+
+    def compute_loss(self, X, y_c, y_sc, y_st, confidence, edge_index):
+        X = self._normalize(X)
+        recon, logits, z = self.model(X, edge_index)
+        logit_c, logit_sc, logit_st = logits
+
+        loss_recon = F.mse_loss(recon, X)
+        loss_c = F.cross_entropy(logit_c, y_c)
+        loss_sc = F.cross_entropy(logit_sc, y_sc)
+        loss_st = (F.cross_entropy(logit_st, y_st, reduction='none') * confidence).mean()
+
+        total_loss = loss_recon + loss_c + loss_sc + loss_st
+
+        metrics = {
+            'total': total_loss.item(), 'recon': loss_recon.item(),
+            'class': loss_c.item(), 'subclass': loss_sc.item(), 'supertype': loss_st.item(),
+        }
+        logits_dict = {'class': logit_c, 'subclass': logit_sc, 'supertype': logit_st}
+        return total_loss, metrics, logits_dict, z
+
+    def _run_subgraphs(self, subgraphs, train=True):
+        """Process donor subgraphs. Each subgraph is a dict with tensors + edge_index."""
+        if train:
+            self.model.train()
+        else:
+            self.model.eval()
+
+        agg = {'total': 0.0, 'recon': 0.0, 'class': 0.0, 'subclass': 0.0, 'supertype': 0.0}
+        n_batches = 0
+
+        for sg in subgraphs:
+            X = sg['X'].to(self.device)
+            y_c = sg['y_class'].to(self.device)
+            y_sc = sg['y_subclass'].to(self.device)
+            y_st = sg['y_supertype'].to(self.device)
+            conf = sg['confidence'].to(self.device)
+            ei = sg['edge_index'].to(self.device)
+
+            if train:
+                self.optimizer.zero_grad()
+                loss, metrics, _, _ = self.compute_loss(X, y_c, y_sc, y_st, conf, ei)
+                loss.backward()
+                torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=5.0)
+                self.optimizer.step()
+            else:
+                with torch.no_grad():
+                    _, metrics, _, _ = self.compute_loss(X, y_c, y_sc, y_st, conf, ei)
+
+            for k in agg:
+                agg[k] += metrics[k]
+            n_batches += 1
+
+        return {k: v / max(n_batches, 1) for k, v in agg.items()}
+
+    def train(self, train_subgraphs, val_subgraphs):
+        os.makedirs(self.args.save_dir, exist_ok=True)
+        best_path = os.path.join(self.args.save_dir, "best_model.pth")
+        patience_counter = 0
+        best_val_metrics = None
+
+        for epoch in range(1, self.args.max_epochs + 1):
+            t0 = time.time()
+            train_m = self._run_subgraphs(train_subgraphs, train=True)
+            val_m = self._run_subgraphs(val_subgraphs, train=False)
+            self.scheduler.step()
+
+            self.logger.info(
+                f"Epoch [{epoch:03d}/{self.args.max_epochs:03d}] | "
+                f"Time: {time.time()-t0:.1f}s | LR: {self.scheduler.get_last_lr()[0]:.2e}"
+            )
+            self.logger.info(
+                f"  [Train] Total: {train_m['total']:.4f} | Recon: {train_m['recon']:.4f} | "
+                f"Class: {train_m['class']:.4f} | Subclass: {train_m['subclass']:.4f} | "
+                f"Supertype: {train_m['supertype']:.4f}"
+            )
+            self.logger.info(
+                f"  [Val]   Total: {val_m['total']:.4f} | Recon: {val_m['recon']:.4f} | "
+                f"Class: {val_m['class']:.4f} | Subclass: {val_m['subclass']:.4f} | "
+                f"Supertype: {val_m['supertype']:.4f}"
+            )
+            if best_val_metrics is not None:
+                self.logger.info(
+                    f"  [Best]  Total: {self.best_val_loss:.4f} | Recon: {best_val_metrics['recon']:.4f} | "
+                    f"Class: {best_val_metrics['class']:.4f} | Subclass: {best_val_metrics['subclass']:.4f} | "
+                    f"Supertype: {best_val_metrics['supertype']:.4f}"
+                )
+
+            if val_m['total'] < self.best_val_loss:
+                self.best_val_loss = val_m['total']
+                best_val_metrics = val_m
+                patience_counter = 0
+                torch.save(self.model.state_dict(), best_path)
+                self.logger.info("Best model saved!")
+            else:
+                patience_counter += 1
+                if patience_counter >= self.args.patience:
+                    self.logger.info("Early stopping triggered!")
+                    break
+
+    @torch.no_grad()
+    def test(self, test_subgraphs):
+        self.logger.info("\nLoading best model for test-set evaluation...")
+        best_path = os.path.join(self.args.save_dir, "best_model.pth")
+        self.model.load_state_dict(torch.load(best_path, map_location=self.device))
+        self.model.eval()
+
+        all_y_true = {'class': [], 'subclass': [], 'supertype': []}
+        all_y_pred = {'class': [], 'subclass': [], 'supertype': []}
+        all_y_prob = {'class': [], 'subclass': [], 'supertype': []}
+        all_z, all_spatial, all_batch, all_st, all_cps = [], [], [], [], []
+        agg = {'total': 0.0, 'recon': 0.0, 'class': 0.0, 'subclass': 0.0, 'supertype': 0.0}
+        n_batches = 0
+
+        for sg in test_subgraphs:
+            X = sg['X'].to(self.device)
+            y_c = sg['y_class'].to(self.device)
+            y_sc = sg['y_subclass'].to(self.device)
+            y_st = sg['y_supertype'].to(self.device)
+            conf = sg['confidence'].to(self.device)
+            ei = sg['edge_index'].to(self.device)
+
+            _, metrics, logits_dict, z = self.compute_loss(X, y_c, y_sc, y_st, conf, ei)
+
+            for k in agg:
+                agg[k] += metrics[k]
+            n_batches += 1
+
+            all_z.append(z.cpu())
+            all_spatial.append(sg['spatial'])
+            all_batch.append(sg['batch_id'])
+            all_st.append(sg['y_supertype'])
+            all_cps.append(sg['cps'])
+
+            for task_key, logit_key in [('class', 'class'), ('subclass', 'subclass'), ('supertype', 'supertype')]:
+                all_y_true[task_key].append(sg[f'y_{task_key}'].numpy())
+                all_y_pred[task_key].append(logits_dict[task_key].argmax(dim=-1).cpu().numpy())
+                all_y_prob[task_key].append(F.softmax(logits_dict[task_key], dim=-1).cpu().numpy())
+
+        test_m = {k: v / max(n_batches, 1) for k, v in agg.items()}
+        self.logger.info(
+            f"  [Test] Total: {test_m['total']:.4f} | Recon: {test_m['recon']:.4f} | "
+            f"Class: {test_m['class']:.4f} | Subclass: {test_m['subclass']:.4f} | "
+            f"Supertype: {test_m['supertype']:.4f}"
+        )
+
+        final_true = {k: np.concatenate(v) for k, v in all_y_true.items()}
+        final_pred = {k: np.concatenate(v) for k, v in all_y_pred.items()}
+        final_prob = {k: np.concatenate(v, axis=0) for k, v in all_y_prob.items()}
+
+        self._log_classification_metrics(final_true, final_pred)
+        self._log_auc_roc_metrics(final_true, final_prob, {
+            'class': self.args.output_num[0],
+            'subclass': self.args.output_num[1],
+            'supertype': self.args.output_num[2],
+        })
+
+        res = {
+            'latent': torch.cat(all_z).numpy(),
+            'spatial': torch.cat(all_spatial).numpy(),
+            'batch': torch.cat(all_batch).numpy(),
+            'supertype': torch.cat(all_st).numpy(),
+            'cps': torch.cat(all_cps).numpy(),
+        }
+        out_path = os.path.join(self.args.save_dir, 'test_features.npz')
+        np.savez_compressed(out_path, **res)
+        self.logger.info(f"Features saved to: {out_path}")
+        return res
+
+    def _log_classification_metrics(self, y_true_dict, y_pred_dict, prefix="Test"):
+        self.logger.info(f"========== {prefix} Classification Metrics ==========")
+        for task in ['class', 'subclass', 'supertype']:
+            y_true, y_pred = y_true_dict[task], y_pred_dict[task]
+            macro_p, macro_r, macro_f1, _ = precision_recall_fscore_support(y_true, y_pred, average='macro', zero_division=0)
+            micro_p, micro_r, micro_f1, _ = precision_recall_fscore_support(y_true, y_pred, average='micro', zero_division=0)
+            per_p, per_r, _, support = precision_recall_fscore_support(y_true, y_pred, average=None, zero_division=0)
+            self.logger.info(f"[{task.upper()}] Macro  - P: {macro_p:.4f} | R: {macro_r:.4f} | F1: {macro_f1:.4f}")
+            self.logger.info(f"[{task.upper()}] Micro  - P: {micro_p:.4f} | R: {micro_r:.4f} | F1: {micro_f1:.4f}")
+            logs = [f"C{i}(S={support[i]}): P={per_p[i]:.4f}/R={per_r[i]:.4f}" for i in range(len(per_p)) if support[i] > 0 or per_p[i] > 0]
+            self.logger.info(f"[{task.upper()}] Per-class: {' | '.join(logs)}")
+            self.logger.info("-" * 50)
+
+    def _log_auc_roc_metrics(self, y_true_dict, y_prob_dict, num_classes_dict, prefix="Test"):
+        self.logger.info(f"========== {prefix} AUC-ROC Metrics ==========")
+        for task in ['class', 'subclass', 'supertype']:
+            y_true, y_prob = y_true_dict[task], y_prob_dict[task]
+            num_c = num_classes_dict[task]
+            y_onehot = np.zeros_like(y_prob)
+            y_onehot[np.arange(len(y_true)), y_true] = 1
+            micro_auc = roc_auc_score(y_onehot.ravel(), y_prob.ravel())
+            valid_aucs = []
+            for c in range(num_c):
+                binary = (y_true == c).astype(int)
+                if len(np.unique(binary)) == 2:
+                    valid_aucs.append(roc_auc_score(binary, y_prob[:, c]))
+            macro_auc = np.mean(valid_aucs) if valid_aucs else 0.0
+            self.logger.info(f"[{task.upper()}] AUC-ROC - Macro: {macro_auc:.4f} | Micro: {micro_auc:.4f} | (Valid: {len(valid_aucs)}/{num_c})")
+            self.logger.info("-" * 50)

external/SEA-AD/MJM/src/engines/trainer.py

@@ -0,0 +1,285 @@
+import os
+import time
+import torch
+import torch.nn.functional as F
+import numpy as np
+from tqdm import tqdm
+import numpy as np
+from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
+
+
+class TrainerEngine:
+    def __init__(self, model, optimizer, scheduler, device, logger, args):
+        self.model = model
+        self.optimizer = optimizer
+        self.scheduler = scheduler
+        self.device = device
+        self.logger = logger
+        self.args = args
+        self.best_val_loss = float('inf')
+
+    def _normalize(self, x, is_norm=True, method='log1p'):
+        if is_norm:
+            if method == 'log1p':
+                return torch.log1p(x)
+            else:
+                raise ValueError(f"Unsupported normalization: {method}")
+        else:
+            if method == 'log1p':
+                return torch.exp(x)-1
+            else:
+                raise ValueError(f"Unsupported normalization: {method}")
+
+
+
+    def log_classification_metrics(self, y_true_dict, y_pred_dict, prefix="Test"):
+        """
+        计算并记录三个层级分类任务的 Precision, Recall, Macro/Micro F1 以及单类指标。
+        
+        Args:
+            y_true_dict: dict, 包含 'class', 'subclass', 'supertype' 的真实标签 Numpy 数组
+            y_pred_dict: dict, 包含 'class', 'subclass', 'supertype' 的预测标签 Numpy 数组
+            prefix: str, 日志前缀，例如 'Val' 或 'Test'
+        """
+        self.logger.info(f"========== {prefix} Classification Metrics ==========")
+        
+        tasks = ['class', 'subclass', 'supertype']
+        
+        for task in tasks:
+            y_true = y_true_dict[task]
+            y_pred = y_pred_dict[task]
+            
+            # 计算 Macro 和 Micro 级别的总体指标
+            # zero_division=0 极其重要，防止某些罕见细胞在当前 batch/epoch 没出现时报错
+            macro_p, macro_r, macro_f1, _ = precision_recall_fscore_support(
+                y_true, y_pred, average='macro', zero_division=0
+            )
+            micro_p, micro_r, micro_f1, _ = precision_recall_fscore_support(
+                y_true, y_pred, average='micro', zero_division=0
+            )
+            
+            # 计算 Per-class (单类) 级别的指标
+            per_class_p, per_class_r, per_class_f1, support = precision_recall_fscore_support(
+                y_true, y_pred, average=None, zero_division=0
+            )
+            
+            # 1. 记录总体指标 (Macro / Micro)
+            self.logger.info(f"[{task.upper()}] Macro  - Precision: {macro_p:.4f} | Recall: {macro_r:.4f} | F1: {macro_f1:.4f}")
+            self.logger.info(f"[{task.upper()}] Micro  - Precision: {micro_p:.4f} | Recall: {micro_r:.4f} | F1: {micro_f1:.4f}")
+            
+            # 2. 紧凑记录单类指标 (Per-class)
+            # 为了防止日志过长，只打印 Support > 0 (真实存在的类) 或有预测值的类
+            per_class_logs = []
+            for i in range(len(per_class_p)):
+                # 过滤掉既没有真实样本，又没有被预测为该类的“空类”
+                if support[i] > 0 or per_class_p[i] > 0:
+                    per_class_logs.append(f"C{i}(S={support[i]}): P={per_class_p[i]:.4f}/R={per_class_r[i]:.4f}")
+            
+            # 将单类指标合并为一行长字符串记录（或者按固定数量换行）
+            per_class_str = " | ".join(per_class_logs)
+            self.logger.info(f"[{task.upper()}] Per-class P/R: {per_class_str}")
+            self.logger.info("-" * 50)
+
+
+
+    def log_auc_roc_metrics(self, y_true_dict, y_prob_dict, num_classes_dict, prefix="Test"):
+        """
+        鲁棒的多分类 AUC-ROC 计算模块。
+        
+        Args:
+            y_true_dict: dict, 包含真实标签的 1D Numpy 数组
+            y_prob_dict: dict, 包含经过 Softmax 的预测概率的 2D Numpy 数组 [N, C]
+            num_classes_dict: dict, 各个任务的类别总数，例如 {'class': 3, 'subclass': 24, 'supertype': 137}
+            prefix: str, 日志前缀
+        """
+        self.logger.info(f"========== {prefix} AUC-ROC Metrics ==========")
+        
+        tasks = ['class', 'subclass', 'supertype']
+        
+        for task in tasks:
+            y_true = y_true_dict[task]
+            y_prob = y_prob_dict[task]
+            num_c = num_classes_dict[task]
+            
+            # 1. 计算 Micro AUC (全局看作一个巨大的二分类任务)
+            # 将 y_true 转为 one-hot 格式，方便与概率矩阵直接计算
+            y_true_onehot = np.zeros_like(y_prob)
+            y_true_onehot[np.arange(len(y_true)), y_true] = 1
+            
+            # Micro AUC: 展平所有维度直接算
+            micro_auc = roc_auc_score(y_true_onehot.ravel(), y_prob.ravel())
+            
+            # 2. 鲁棒地计算 Macro AUC (One-vs-Rest)
+            # 针对长尾分布：只对存在正样本且存在负样本的类进行计算，防止 sklearn 报错
+            valid_auc_scores = []
+            for c in range(num_c):
+                y_true_binary = (y_true == c).astype(int)
+                
+                # 只有当该类同时存在 0 和 1 的时候（即既有正样本也有负样本），才能算 AUC
+                if len(np.unique(y_true_binary)) == 2:
+                    auc = roc_auc_score(y_true_binary, y_prob[:, c])
+                    valid_auc_scores.append(auc)
+                    
+            # 取有效类的均值作为 Macro AUC
+            macro_auc = np.mean(valid_auc_scores) if len(valid_auc_scores) > 0 else 0.0
+            
+            # 3. 记录日志
+            self.logger.info(f"[{task.upper()}] AUC-ROC - Macro: {macro_auc:.4f} | Micro: {micro_auc:.4f} | (Valid classes: {len(valid_auc_scores)}/{num_c})")
+            
+            self.logger.info("-" * 50)
+
+
+
+    def compute_loss(self, batch_data):
+        X = batch_data['X'].to(self.device)
+        # batch_id = batch_data['batch_id'].to(self.device)
+        y_c = batch_data['y_class'].to(self.device)
+        y_sc = batch_data['y_subclass'].to(self.device)
+        y_st = batch_data['y_supertype'].to(self.device)
+        confidence = batch_data['confidence'].to(self.device)
+
+        # recon_X, logits, z = self.model(X, batch_id)
+        X = self._normalize(X)  # log(1+x) 处理输入特征的数值范围
+        recon_X, logits, z = self.model(X)
+
+        logit_c, logit_sc, logit_st = logits
+
+        loss_recon = F.mse_loss(recon_X, X)
+        loss_c = F.cross_entropy(logit_c, y_c)
+        loss_sc = F.cross_entropy(logit_sc, y_sc)
+        
+        # 带有 Confidence 的样本级加权 Loss
+        loss_st_unweighted = F.cross_entropy(logit_st, y_st, reduction='none')
+        loss_st = (loss_st_unweighted * confidence).mean()
+
+        # total_loss = 1.0 * loss_recon + 0.5 * loss_c + 1.0 * loss_sc + 2.0 * loss_st
+        total_loss = 1.0 * loss_recon + 1.0 * loss_c + 1.0 * loss_sc + 1.0 * loss_st
+        
+        metrics = {'total': total_loss, 'recon': loss_recon.item(), 'class': loss_c.item(), 'subclass': loss_sc.item(), 'supertype': loss_st.item()}
+        logits_dict = {'class': logit_c, 'subclass': logit_sc, 'supertype': logit_st}
+        return total_loss, loss_recon, metrics, logits_dict, z
+
+    def train_epoch(self, dataloader):
+        self.model.train()
+        epoch_metrics = {'total': 0.0, 'recon': 0.0, 'class': 0.0, 'subclass': 0.0, 'supertype': 0.0}
+        
+        for batch_data in tqdm(dataloader, desc="Training"):
+            self.optimizer.zero_grad()
+            loss, _, metrics, _, _ = self.compute_loss(batch_data)
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=5.0)
+            self.optimizer.step()
+            
+            for k in epoch_metrics: epoch_metrics[k] += metrics[k]
+            
+        return {k: v / len(dataloader) for k, v in epoch_metrics.items()}
+
+    @torch.no_grad()
+    def eval_epoch(self, dataloader):
+        self.model.eval()
+        epoch_metrics = {'total': 0.0, 'recon': 0.0, 'class': 0.0, 'subclass': 0.0, 'supertype': 0.0}
+
+        for batch_data in tqdm(dataloader, desc="Evaluating"):
+            _, _, metrics, _, _ = self.compute_loss(batch_data)
+            for k in epoch_metrics: epoch_metrics[k] += metrics[k]
+
+        return {k: v / len(dataloader) for k, v in epoch_metrics.items()}
+
+    def train(self, train_loader, val_loader):
+        os.makedirs(self.args.save_dir, exist_ok=True)
+        best_model_path = os.path.join(self.args.save_dir, "best_model.pth")
+        patience_counter = 0
+
+        for epoch in range(1, self.args.max_epochs + 1):
+            start_time = time.time()
+            train_metrics = self.train_epoch(train_loader)
+            val_metrics = self.eval_epoch(val_loader)
+            self.scheduler.step()
+            
+            self.logger.info(f"Epoch [{epoch:03d}/{self.args.max_epochs:03d}] | Time: {time.time()-start_time:.1f}s | LR: {self.scheduler.get_last_lr()[0]:.2e}")
+            self.logger.info(f"  [Train] Total: {train_metrics['total']:.4f} | Recon: {train_metrics['recon']:.4f} | Class: {train_metrics['class']:.4f} | Subclass: {train_metrics['subclass']:.4f} | Supertype: {train_metrics['supertype']:.4f}")
+            self.logger.info(f"  [Val]   Total: {val_metrics['total']:.4f} | Recon: {val_metrics['recon']:.4f} | Class: {val_metrics['class']:.4f} | Subclass: {val_metrics['subclass']:.4f} | Supertype: {val_metrics['supertype']:.4f}")
+            if epoch > 1:
+                self.logger.info(f"  [Best Val] Total: {self.best_val_loss:.4f} | Recon: {best_val_metrics['recon']:.4f} | Class: {best_val_metrics['class']:.4f} | Subclass: {best_val_metrics['subclass']:.4f} | Supertype: {best_val_metrics['supertype']:.4f}")
+            if val_metrics['total'] < self.best_val_loss:
+                self.best_val_loss = val_metrics['total']
+                best_val_metrics = val_metrics
+                patience_counter = 0
+                torch.save(self.model.state_dict(), best_model_path)
+                self.logger.info(f"Best model saved!")
+            else:
+                patience_counter += 1
+                if patience_counter >= self.args.patience:
+                    self.logger.info("Early stopping triggered!")
+                    break
+
+    @torch.no_grad()
+    def test(self, test_loader):
+        self.logger.info("\nLoading best model for Test set evaluation...")
+        best_model_path = os.path.join(self.args.save_dir, "best_model.pth")
+        self.model.load_state_dict(torch.load(best_model_path, map_location=self.device))
+        self.model.eval()
+        
+        res = {'latent': [], 'spatial': [], 'batch': [], 'supertype': [], 'cps': []}
+        epoch_metrics = {'total': 0.0, 'recon': 0.0, 'class': 0.0, 'subclass': 0.0, 'supertype': 0.0}
+        # 用于收集全 Epoch 的真实标签和预测结果
+        all_y_true = {'class': [], 'subclass': [], 'supertype': []}
+        all_y_pred = {'class': [], 'subclass': [], 'supertype': []}
+        # 🌟 新增：用于收集预测概率 [N, C] 的容器
+        all_y_prob = {'class': [], 'subclass': [], 'supertype': []}
+        for batch_data in test_loader:
+            _, _, metrics, logits_dict, z = self.compute_loss(batch_data)
+            res['latent'].append(z.cpu())
+            res['spatial'].append(batch_data['spatial'].cpu())
+            res['batch'].append(batch_data['batch_id'].cpu())
+            res['supertype'].append(batch_data['y_supertype'].cpu())
+            res['cps'].append(batch_data['cps'].cpu())
+            for k in epoch_metrics: epoch_metrics[k] += metrics[k]
+
+            # 2. 提取真实的标签并转为 CPU Numpy
+            all_y_true['class'].append(batch_data['y_class'].cpu().numpy())
+            all_y_true['subclass'].append(batch_data['y_subclass'].cpu().numpy())
+            all_y_true['supertype'].append(batch_data['y_supertype'].cpu().numpy())
+            
+            # 🌟 新增：对 logits 进行 Softmax 转为概率，并收集 [Batch, Num_Classes]
+            all_y_prob['class'].append(F.softmax(logits_dict['class'], dim=-1).cpu().numpy())
+            all_y_prob['subclass'].append(F.softmax(logits_dict['subclass'], dim=-1).cpu().numpy())
+            all_y_prob['supertype'].append(F.softmax(logits_dict['supertype'], dim=-1).cpu().numpy())
+
+            # 3. 从 logits 中提取预测类别 (argmax)，并转为 CPU Numpy
+            # 假设 compute_loss 返回了 logit_c, logit_sc, logit_st 组成的 logits_dict
+            # 这里为了演示，我假设你的 logits 已经拿到了
+            logit_c = logits_dict['class']
+            logit_sc = logits_dict['subclass']
+            logit_st = logits_dict['supertype']
+        
+            all_y_pred['class'].append(logit_c.argmax(dim=-1).cpu().numpy())
+            all_y_pred['subclass'].append(logit_sc.argmax(dim=-1).cpu().numpy())
+            all_y_pred['supertype'].append(logit_st.argmax(dim=-1).cpu().numpy())
+
+        test_metrics =  {k: v / len(test_loader) for k, v in epoch_metrics.items()}            
+        self.logger.info(f"  [Test] Total: {test_metrics['total']:.4f} | Recon: {test_metrics['recon']:.4f} | Class: {test_metrics['class']:.4f} | Subclass: {test_metrics['subclass']:.4f} | Supertype: {test_metrics['supertype']:.4f}")
+        # 将 List of Numpy arrays 拼接成一个完整的大一维 Array
+        final_y_true = {k: np.concatenate(v) for k, v in all_y_true.items()}
+        final_y_pred = {k: np.concatenate(v) for k, v in all_y_pred.items()}
+        
+        # 🌟 调用我们刚才写的日志函数计算并打印 P, R, F1
+        self.log_classification_metrics(final_y_true, final_y_pred, prefix='test')
+
+        # 🌟 新增：拼接概率矩阵 (沿着样本维度 dim=0 拼接)
+        final_y_prob = {k: np.concatenate(v, axis=0) for k, v in all_y_prob.items()}
+        num_classes_dict = {
+            'class':     self.args.output_num[0],
+            'subclass':  self.args.output_num[1],
+            'supertype': self.args.output_num[2],
+        }
+        self.log_auc_roc_metrics(final_y_true, final_y_prob, num_classes_dict, prefix='Test')
+
+
+        res = {k: torch.cat(v, dim=0).numpy() for k, v in res.items()}
+        out_path = os.path.join(self.args.save_dir, 'test_features.npz')
+        np.savez_compressed(out_path, **res)
+        self.logger.info(f"Features saved to: {out_path}")
+
+
+        return res

external/SEA-AD/MJM/src/engines/trainer_2a.py

@@ -0,0 +1,186 @@
+"""
+Trainer for mjm_2a: Laminar Depth Injection.
+Extends TrainerEngine logic; does NOT modify trainer.py.
+"""
+import os
+import time
+import torch
+import torch.nn.functional as F
+import numpy as np
+from tqdm import tqdm
+from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
+
+from src.engines.trainer import TrainerEngine
+
+
+class TrainerEngine_2a(TrainerEngine):
+    """
+    Adds depth imputation input and auxiliary depth regression loss.
+    depth_imputer: a callable model(X) -> depth_pred [B,1]
+    lambda_depth: weight for auxiliary depth MSE loss
+    """
+    def __init__(self, model, optimizer, scheduler, device, logger, args,
+                 depth_imputer=None, lambda_depth=0.1):
+        super().__init__(model, optimizer, scheduler, device, logger, args)
+        self.depth_imputer = depth_imputer
+        self.lambda_depth  = lambda_depth
+
+    def compute_loss(self, batch_data):
+        X          = batch_data['X'].to(self.device)
+        y_c        = batch_data['y_class'].to(self.device)
+        y_sc       = batch_data['y_subclass'].to(self.device)
+        y_st       = batch_data['y_supertype'].to(self.device)
+        confidence = batch_data['confidence'].to(self.device)
+        depth_true = batch_data['depth_norm'].to(self.device).unsqueeze(-1)   # [B,1]
+        has_depth  = batch_data['has_depth'].to(self.device)                   # [B]
+
+        X_norm = self._normalize(X)  # log1p
+
+        # Impute depth for all cells
+        with torch.no_grad():
+            depth_imputed = self.depth_imputer(X_norm)  # [B,1]
+
+        recon_X, logits, z, depth_pred = self.model(X_norm, depth_imputed)
+        logit_c, logit_sc, logit_st = logits
+
+        loss_recon = F.mse_loss(recon_X, X_norm)
+        loss_c     = F.cross_entropy(logit_c, y_c)
+        loss_sc    = F.cross_entropy(logit_sc, y_sc)
+        loss_st_uw = F.cross_entropy(logit_st, y_st, reduction='none')
+        loss_st    = (loss_st_uw * confidence).mean()
+
+        # Auxiliary depth loss (only on cells that have real depth labels)
+        if has_depth.sum() > 0:
+            loss_depth = F.mse_loss(
+                depth_pred[has_depth.bool()],
+                depth_true[has_depth.bool()]
+            )
+        else:
+            loss_depth = torch.tensor(0.0, device=self.device)
+
+        total_loss = (1.0 * loss_recon + 1.0 * loss_c +
+                      1.0 * loss_sc   + 1.0 * loss_st +
+                      self.lambda_depth * loss_depth)
+
+        metrics = {
+            'total':    total_loss,
+            'recon':    loss_recon.item(),
+            'class':    loss_c.item(),
+            'subclass': loss_sc.item(),
+            'supertype':loss_st.item(),
+            'depth':    loss_depth.item(),
+        }
+        logits_dict = {'class': logit_c, 'subclass': logit_sc, 'supertype': logit_st}
+        return total_loss, loss_recon, metrics, logits_dict, z
+
+    def train_epoch(self, dataloader):
+        self.model.train()
+        keys = ['total', 'recon', 'class', 'subclass', 'supertype', 'depth']
+        epoch_metrics = {k: 0.0 for k in keys}
+        for batch_data in tqdm(dataloader, desc="Training"):
+            self.optimizer.zero_grad()
+            loss, _, metrics, _, _ = self.compute_loss(batch_data)
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=5.0)
+            self.optimizer.step()
+            for k in epoch_metrics:
+                epoch_metrics[k] += metrics[k]
+        return {k: v / len(dataloader) for k, v in epoch_metrics.items()}
+
+    @torch.no_grad()
+    def eval_epoch(self, dataloader):
+        self.model.eval()
+        keys = ['total', 'recon', 'class', 'subclass', 'supertype', 'depth']
+        epoch_metrics = {k: 0.0 for k in keys}
+        for batch_data in tqdm(dataloader, desc="Evaluating"):
+            _, _, metrics, _, _ = self.compute_loss(batch_data)
+            for k in epoch_metrics:
+                epoch_metrics[k] += metrics[k]
+        return {k: v / len(dataloader) for k, v in epoch_metrics.items()}
+
+    def train(self, train_loader, val_loader):
+        os.makedirs(self.args.save_dir, exist_ok=True)
+        best_model_path = os.path.join(self.args.save_dir, 'best_model.pth')
+        patience_counter = 0
+        best_val_metrics = None
+
+        for epoch in range(1, self.args.max_epochs + 1):
+            t0 = time.time()
+            train_metrics = self.train_epoch(train_loader)
+            val_metrics   = self.eval_epoch(val_loader)
+            self.scheduler.step()
+
+            self.logger.info(
+                f"Epoch [{epoch:03d}/{self.args.max_epochs}] "
+                f"Time:{time.time()-t0:.1f}s LR:{self.scheduler.get_last_lr()[0]:.2e}"
+            )
+            self.logger.info(
+                f"  [Train] total:{train_metrics['total']:.4f} "
+                f"recon:{train_metrics['recon']:.4f} cls:{train_metrics['class']:.4f} "
+                f"sub:{train_metrics['subclass']:.4f} sup:{train_metrics['supertype']:.4f} "
+                f"depth:{train_metrics['depth']:.4f}"
+            )
+            self.logger.info(
+                f"  [Val]   total:{val_metrics['total']:.4f} "
+                f"recon:{val_metrics['recon']:.4f} cls:{val_metrics['class']:.4f} "
+                f"sub:{val_metrics['subclass']:.4f} sup:{val_metrics['supertype']:.4f} "
+                f"depth:{val_metrics['depth']:.4f}"
+            )
+
+            if val_metrics['total'] < self.best_val_loss:
+                self.best_val_loss = val_metrics['total']
+                best_val_metrics   = val_metrics
+                patience_counter   = 0
+                torch.save(self.model.state_dict(), best_model_path)
+                self.logger.info('Best model saved!')
+            else:
+                patience_counter += 1
+                if patience_counter >= self.args.patience:
+                    self.logger.info('Early stopping triggered!')
+                    break
+
+    @torch.no_grad()
+    def test(self, test_loader):
+        self.logger.info('Loading best model for test...')
+        best_model_path = os.path.join(self.args.save_dir, 'best_model.pth')
+        self.model.load_state_dict(torch.load(best_model_path, map_location=self.device))
+        self.model.eval()
+
+        all_y_true = {'class': [], 'subclass': [], 'supertype': []}
+        all_y_pred = {'class': [], 'subclass': [], 'supertype': []}
+        all_y_prob = {'class': [], 'subclass': [], 'supertype': []}
+        res = {'latent': [], 'spatial': [], 'batch': [], 'supertype': [], 'cps': []}
+        epoch_metrics = {'total': 0.0, 'recon': 0.0, 'class': 0.0,
+                         'subclass': 0.0, 'supertype': 0.0, 'depth': 0.0}
+
+        for batch_data in test_loader:
+            _, _, metrics, logits_dict, z = self.compute_loss(batch_data)
+            for k in epoch_metrics:
+                epoch_metrics[k] += metrics[k]
+            res['latent'].append(z.cpu())
+            res['spatial'].append(batch_data['spatial'].cpu())
+            res['batch'].append(batch_data['batch_id'].cpu())
+            res['supertype'].append(batch_data['y_supertype'].cpu())
+            res['cps'].append(batch_data['cps'].cpu())
+            for t in ['class', 'subclass', 'supertype']:
+                all_y_true[t].append(batch_data[f'y_{t}'].cpu().numpy())
+                all_y_pred[t].append(logits_dict[t].argmax(-1).cpu().numpy())
+                all_y_prob[t].append(F.softmax(logits_dict[t], -1).cpu().numpy())
+
+        test_metrics = {k: v / len(test_loader) for k, v in epoch_metrics.items()}
+        self.logger.info(f"  [Test] {test_metrics}")
+        final_y_true = {k: np.concatenate(v) for k, v in all_y_true.items()}
+        final_y_pred = {k: np.concatenate(v) for k, v in all_y_pred.items()}
+        final_y_prob = {k: np.concatenate(v, axis=0) for k, v in all_y_prob.items()}
+        self.log_classification_metrics(final_y_true, final_y_pred, prefix='Test')
+        num_classes_dict = {
+            'class':     self.args.output_num[0],
+            'subclass':  self.args.output_num[1],
+            'supertype': self.args.output_num[2],
+        }
+        self.log_auc_roc_metrics(final_y_true, final_y_prob, num_classes_dict, prefix='Test')
+        res = {k: torch.cat(v, 0).numpy() for k, v in res.items()}
+        out_path = os.path.join(self.args.save_dir, 'test_features.npz')
+        np.savez_compressed(out_path, **res)
+        self.logger.info(f'Features saved: {out_path}')
+        return res

external/SEA-AD/MJM/src/engines/trainer_2b.py

@@ -0,0 +1,186 @@
+"""
+Trainer for mjm_2b: Morpho-Spatial Fusion.
+"""
+import os
+import time
+import torch
+import torch.nn.functional as F
+import numpy as np
+from tqdm import tqdm
+
+from src.engines.trainer import TrainerEngine
+
+
+class TrainerEngine_2b(TrainerEngine):
+    """
+    Adds cell_volume_norm + spatial_tiled inputs and auxiliary volume loss.
+    lambda_vol: weight for auxiliary volume MSE loss
+    """
+    def __init__(self, model, optimizer, scheduler, device, logger, args,
+                 lambda_vol=0.1):
+        super().__init__(model, optimizer, scheduler, device, logger, args)
+        self.lambda_vol = lambda_vol
+
+    def _norm_spatial_tiled(self, st):
+        """Per-batch min-max normalize tiled spatial coords to [0,1]."""
+        mn = st.min(dim=0, keepdim=True).values
+        mx = st.max(dim=0, keepdim=True).values
+        return (st - mn) / (mx - mn + 1e-6)
+
+    def compute_loss(self, batch_data):
+        X               = batch_data['X'].to(self.device)
+        y_c             = batch_data['y_class'].to(self.device)
+        y_sc            = batch_data['y_subclass'].to(self.device)
+        y_st            = batch_data['y_supertype'].to(self.device)
+        confidence      = batch_data['confidence'].to(self.device)
+        vol_norm        = batch_data['cell_volume_norm'].to(self.device).unsqueeze(-1)  # [B,1]
+        has_volume      = batch_data['has_volume'].to(self.device)                       # [B]
+        spatial_tiled   = batch_data['spatial_tiled'].to(self.device)                   # [B,2]
+
+        X_norm = self._normalize(X)
+        st_norm = self._norm_spatial_tiled(spatial_tiled)  # [B,2]
+
+        recon_X, logits, z, vol_pred = self.model(X_norm, vol_norm, st_norm)
+        logit_c, logit_sc, logit_st = logits
+
+        loss_recon = F.mse_loss(recon_X, X_norm)
+        loss_c     = F.cross_entropy(logit_c, y_c)
+        loss_sc    = F.cross_entropy(logit_sc, y_sc)
+        loss_st_uw = F.cross_entropy(logit_st, y_st, reduction='none')
+        loss_st    = (loss_st_uw * confidence).mean()
+
+        if has_volume.sum() > 0:
+            loss_vol = F.mse_loss(
+                vol_pred[has_volume.bool()],
+                vol_norm[has_volume.bool()]
+            )
+        else:
+            loss_vol = torch.tensor(0.0, device=self.device)
+
+        total_loss = (1.0 * loss_recon + 1.0 * loss_c +
+                      1.0 * loss_sc   + 1.0 * loss_st +
+                      self.lambda_vol * loss_vol)
+
+        metrics = {
+            'total':    total_loss,
+            'recon':    loss_recon.item(),
+            'class':    loss_c.item(),
+            'subclass': loss_sc.item(),
+            'supertype':loss_st.item(),
+            'volume':   loss_vol.item(),
+        }
+        logits_dict = {'class': logit_c, 'subclass': logit_sc, 'supertype': logit_st}
+        return total_loss, loss_recon, metrics, logits_dict, z
+
+    def train_epoch(self, dataloader):
+        self.model.train()
+        keys = ['total', 'recon', 'class', 'subclass', 'supertype', 'volume']
+        epoch_metrics = {k: 0.0 for k in keys}
+        for batch_data in tqdm(dataloader, desc="Training"):
+            self.optimizer.zero_grad()
+            loss, _, metrics, _, _ = self.compute_loss(batch_data)
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=5.0)
+            self.optimizer.step()
+            for k in epoch_metrics:
+                epoch_metrics[k] += metrics[k]
+        return {k: v / len(dataloader) for k, v in epoch_metrics.items()}
+
+    def eval_epoch(self, dataloader):
+        self.model.eval()
+        keys = ['total', 'recon', 'class', 'subclass', 'supertype', 'volume']
+        epoch_metrics = {k: 0.0 for k in keys}
+        for batch_data in tqdm(dataloader, desc="Evaluating"):
+            self.optimizer.zero_grad()
+            _, loss_recon, metrics, _, _ = self.compute_loss(batch_data)
+            loss_recon.backward()
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=5.0)
+            self.optimizer.step()
+            for k in epoch_metrics:
+                epoch_metrics[k] += metrics[k]
+        return {k: v / len(dataloader) for k, v in epoch_metrics.items()}
+
+    def train(self, train_loader, val_loader):
+        os.makedirs(self.args.save_dir, exist_ok=True)
+        best_model_path = os.path.join(self.args.save_dir, 'best_model.pth')
+        patience_counter = 0
+
+        for epoch in range(1, self.args.max_epochs + 1):
+            t0 = time.time()
+            train_metrics = self.train_epoch(train_loader)
+            val_metrics   = self.eval_epoch(val_loader)
+            self.scheduler.step()
+
+            self.logger.info(
+                f"Epoch [{epoch:03d}/{self.args.max_epochs}] "
+                f"Time:{time.time()-t0:.1f}s LR:{self.scheduler.get_last_lr()[0]:.2e}"
+            )
+            self.logger.info(
+                f"  [Train] total:{train_metrics['total']:.4f} "
+                f"recon:{train_metrics['recon']:.4f} cls:{train_metrics['class']:.4f} "
+                f"sub:{train_metrics['subclass']:.4f} sup:{train_metrics['supertype']:.4f} "
+                f"vol:{train_metrics['volume']:.4f}"
+            )
+            self.logger.info(
+                f"  [Val]   total:{val_metrics['total']:.4f} "
+                f"recon:{val_metrics['recon']:.4f} cls:{val_metrics['class']:.4f} "
+                f"sub:{val_metrics['subclass']:.4f} sup:{val_metrics['supertype']:.4f} "
+                f"vol:{val_metrics['volume']:.4f}"
+            )
+
+            if val_metrics['total'] < self.best_val_loss:
+                self.best_val_loss = val_metrics['total']
+                patience_counter   = 0
+                torch.save(self.model.state_dict(), best_model_path)
+                self.logger.info('Best model saved!')
+            else:
+                patience_counter += 1
+                if patience_counter >= self.args.patience:
+                    self.logger.info('Early stopping triggered!')
+                    break
+
+    @torch.no_grad()
+    def test(self, test_loader):
+        self.logger.info('Loading best model for test...')
+        best_model_path = os.path.join(self.args.save_dir, 'best_model.pth')
+        self.model.load_state_dict(torch.load(best_model_path, map_location=self.device))
+        self.model.eval()
+
+        all_y_true = {'class': [], 'subclass': [], 'supertype': []}
+        all_y_pred = {'class': [], 'subclass': [], 'supertype': []}
+        all_y_prob = {'class': [], 'subclass': [], 'supertype': []}
+        res = {'latent': [], 'spatial': [], 'batch': [], 'supertype': [], 'cps': []}
+        epoch_metrics = {'total': 0.0, 'recon': 0.0, 'class': 0.0,
+                         'subclass': 0.0, 'supertype': 0.0, 'volume': 0.0}
+
+        for batch_data in test_loader:
+            _, _, metrics, logits_dict, z = self.compute_loss(batch_data)
+            for k in epoch_metrics:
+                epoch_metrics[k] += metrics[k]
+            res['latent'].append(z.cpu())
+            res['spatial'].append(batch_data['spatial'].cpu())
+            res['batch'].append(batch_data['batch_id'].cpu())
+            res['supertype'].append(batch_data['y_supertype'].cpu())
+            res['cps'].append(batch_data['cps'].cpu())
+            for t in ['class', 'subclass', 'supertype']:
+                all_y_true[t].append(batch_data[f'y_{t}'].cpu().numpy())
+                all_y_pred[t].append(logits_dict[t].argmax(-1).cpu().numpy())
+                all_y_prob[t].append(F.softmax(logits_dict[t], -1).cpu().numpy())
+
+        test_metrics = {k: v / len(test_loader) for k, v in epoch_metrics.items()}
+        self.logger.info(f"  [Test] {test_metrics}")
+        final_y_true = {k: np.concatenate(v) for k, v in all_y_true.items()}
+        final_y_pred = {k: np.concatenate(v) for k, v in all_y_pred.items()}
+        final_y_prob = {k: np.concatenate(v, axis=0) for k, v in all_y_prob.items()}
+        self.log_classification_metrics(final_y_true, final_y_pred, prefix='Test')
+        num_classes_dict = {
+            'class':     self.args.output_num[0],
+            'subclass':  self.args.output_num[1],
+            'supertype': self.args.output_num[2],
+        }
+        self.log_auc_roc_metrics(final_y_true, final_y_prob, num_classes_dict, prefix='Test')
+        res = {k: torch.cat(v, 0).numpy() for k, v in res.items()}
+        out_path = os.path.join(self.args.save_dir, 'test_features.npz')
+        np.savez_compressed(out_path, **res)
+        self.logger.info(f'Features saved: {out_path}')
+        return res

external/SEA-AD/MJM/src/utils/dataloader_v2.py

@@ -0,0 +1,106 @@
+"""
+Dataloader for global_data_v2.npz.
+Returns all fields needed by mjm_2a/b/c trainers.
+"""
+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader
+
+
+class SpatialSEAAD_V2_Dataset(Dataset):
+    def __init__(self, X, y_class, y_subclass, y_supertype, batch_donor,
+                 spatial, spatial_tiled, cps, confidence,
+                 depth_norm, has_depth,
+                 cell_volume_norm, has_volume,
+                 meta_info):
+        self.X               = torch.from_numpy(X).float()
+        self.y_class         = torch.from_numpy(y_class).long()
+        self.y_subclass      = torch.from_numpy(y_subclass).long()
+        self.y_supertype     = torch.from_numpy(y_supertype).long()
+        self.batch_donor     = torch.from_numpy(batch_donor).long()
+        self.spatial         = torch.from_numpy(spatial).float()
+        self.spatial_tiled   = torch.from_numpy(spatial_tiled).float()
+        self.cps             = torch.from_numpy(cps).float()
+        self.confidence      = torch.from_numpy(confidence).float()
+        self.depth_norm      = torch.from_numpy(depth_norm).float()
+        self.has_depth       = torch.from_numpy(has_depth.astype(np.float32)).float()
+        self.cell_volume_norm = torch.from_numpy(cell_volume_norm).float()
+        self.has_volume      = torch.from_numpy(has_volume.astype(np.float32)).float()
+
+        meta = meta_info.item()
+        self.num_c  = meta.get('num_class')
+        self.num_sc = meta.get('num_subclass')
+        self.num_st = meta.get('num_supertype')
+
+    def __len__(self):
+        return self.X.shape[0]
+
+    def __getitem__(self, idx):
+        return {
+            'X':               self.X[idx],
+            'spatial':         self.spatial[idx],
+            'spatial_tiled':   self.spatial_tiled[idx],
+            'batch_id':        self.batch_donor[idx],
+            'y_class':         self.y_class[idx],
+            'y_subclass':      self.y_subclass[idx],
+            'y_supertype':     self.y_supertype[idx],
+            'cps':             self.cps[idx],
+            'confidence':      self.confidence[idx],
+            'depth_norm':      self.depth_norm[idx],
+            'has_depth':       self.has_depth[idx],
+            'cell_volume_norm': self.cell_volume_norm[idx],
+            'has_volume':      self.has_volume[idx],
+        }
+
+
+def build_dataloaders_v2(npz_path, batch_size=512, seed=42, num_workers=8):
+    data = np.load(npz_path, allow_pickle=True)
+
+    X_all           = data['X']
+    y_c_all         = data['y_class']
+    y_sc_all        = data['y_subclass']
+    y_st_all        = data['y_supertype']
+    batch_all       = data['batch_donor']
+    spatial_all     = data['spatial']
+    spatial_tiled_all = data['spatial_tiled']
+    cps_all         = data['cps']
+    conf_all        = data['y_supertype_confidence']
+    depth_all       = data['depth_norm']
+    has_depth_all   = data['has_depth']
+    vol_all         = data['cell_volume_norm']
+    has_vol_all     = data['has_volume']
+    meta_info       = data['meta']
+
+    # per-donor split (same logic as v1)
+    unique_donors = np.unique(batch_all)
+    np.random.seed(seed)
+    np.random.shuffle(unique_donors)
+    test_donors  = unique_donors[:6]
+    val_donors   = unique_donors[6:9]
+    train_donors = unique_donors[9:]
+
+    def _slice(mask):
+        return SpatialSEAAD_V2_Dataset(
+            X_all[mask], y_c_all[mask], y_sc_all[mask], y_st_all[mask],
+            batch_all[mask], spatial_all[mask], spatial_tiled_all[mask],
+            cps_all[mask], conf_all[mask],
+            depth_all[mask], has_depth_all[mask],
+            vol_all[mask], has_vol_all[mask],
+            meta_info,
+        )
+
+    mask_train = np.isin(batch_all, train_donors)
+    mask_val   = np.isin(batch_all, val_donors)
+    mask_test  = np.isin(batch_all, test_donors)
+
+    train_ds = _slice(mask_train)
+    val_ds   = _slice(mask_val)
+    test_ds  = _slice(mask_test)
+
+    train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=True,
+                              drop_last=True, num_workers=num_workers)
+    val_loader   = DataLoader(val_ds,   batch_size=batch_size, shuffle=True,
+                              num_workers=num_workers)
+    test_loader  = DataLoader(test_ds,  batch_size=batch_size, shuffle=False,
+                              num_workers=num_workers)
+    return train_loader, val_loader, test_loader