diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..78346d1e1cebe243fce80774e9d11b941ea85552 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,62 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.avro filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp 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 +MANIFEST_CONTENTS.csv filter=lfs diff=lfs merge=lfs -text +MANIFEST_INTERNAL.csv filter=lfs diff=lfs merge=lfs -text diff --git a/FILE_NAMING_CONVENTION.txt b/FILE_NAMING_CONVENTION.txt new file mode 100644 index 0000000000000000000000000000000000000000..1da8f46a742882b2d983db3e6f43b66bd3c8156d --- /dev/null +++ b/FILE_NAMING_CONVENTION.txt @@ -0,0 +1,228 @@ +SYTO PUBLICATION DATA - FILE NAMING CONVENTION +============================================== + +Last updated: 2026-08-24 + +This file documents the naming convention used in this dataset, together with +every abbreviation and code that appears in a file or folder name. + +1. GENERAL RULES +---------------- + +- Words within a name element are joined without separators (e.g. "softlabels"); + underscores (_) separate the elements themselves. +- Directory names are lowercase with hyphens between words (tier0-results, + rrbs-recovered-reads). Result file names use the element scheme in section 3. +- Names are location-independent: a file still identifies itself after being + moved out of its folder. +- Version is a trailing _v. v1 is the first published release. + + +2. TOP-LEVEL LAYOUT +------------------- + +The deposit ships as archives, one per logical unit, plus a few loose files +that should be readable without downloading anything large: + + tier0-results/ Arhives with final result for two main experiments + + TabulaSapines mappings + consolidated ranking table + of Syto variants and external baselines. + tier1-models// Trained Syto variants, one archive per variant. + tier2-pseudobulks// One archive per pseudobulk run. + tier3-training-data/ Marker atlases used in Syto paper; target proportions + used for each pseudobulk generation; + training datasets under datasets//. + tier4-source-data/ Staged and recovered reads, plus the hg19 and + hg38 reference genomes. + MANIFEST.csv One row per archive or standalone file: what it is, size, file + count, sha256. + runs.csv One row per experiment run. + FILE_NAMING_CONVENTION.txt This file. + +Unpacking every archive restores the original tree: + + tier0-results/ Final tables underlying the publication. + tier1-models/ Trained classifiers, deconvolvers and calibrators. + tier2-pseudobulks/ Generated pseudobulk mixtures (columnar Parquet). + tier3-training-data/ Atlases, target proportions and training datasets. + tier4-source-data/ Staged and recovered reads, and reference genomes. + +Within tier1 and tier2, folders are /, where run_name is +__, e.g. dismir__softlabelpooled. + + +3. RESULT FILE NAME SCHEME (tier0-results) +------------------------------------------ + +Six elements: + + _____.csv + + Example: + SYTO_oodrrbs_dismir_softlabels_top156_v1.csv + | | | | | | + | | | | | +-- version + | | | | +--------- feature set + | | | +-------------------- label mode + | | +--------------------------- classifier + | +----------------------------------- experiment + +---------------------------------------- project + + +4. CODES AND ABBREVIATIONS +-------------------------- + +PROJECT + SYTO The framework introduced in "data-driven soft labeling scales + dna read classification to whole-body cell-type deconvolution". + +EXPERIMENT + + MAIN EXPERIMENTS: + + pseudobulk [pseudobulk] In-distribution evaluation on generated + pseudobulks. + oodrrbs [ood-rrbs] Out-of-distribution evaluation on the cfSort + RRBS cohort. + + SUPPLEMENTARY EXPERIMENTS: + + oodrrbsgss [ood-rrbs-gss] Out-of-distribution RRBS using the + GSS-sorted atlas. + poolingsensitivity [pooling-sensitivity] Sensitivity analysis over + read-pooling thresholds. + mledeconvolvers [mle-deconvolvers] Maximum-likelihood deconvolver + variants. + +CLASSIFIER + dismir Syto-extended DISMIR read-level classifier (CNN-LSTM). + methylbert Syto-refactored MethylBERT transformer classifier. + lookup Syto-introduced 1NN-Lookup-table classifier. + cancerdetector Syto-extended CancerDetector baseline classifier. + baselines Not a classifier: reference deconvolution methods + (Celfie, EpiDISH, Houseman CP, UXM) collected in one table. + +LABELMODE (how read-level training labels were assigned) + hardlabels Hard labels with a background class. + softlabels Data Driven Soft labels, with read pooling. + softnopooling Data Driven Soft labels, without read pooling. + softcanonical Soft labels produced by label smoothing (a.k.a canonical). + uniformprior CancerDetector with a uniform cell-type prior. + trainfreqprior CancerDetector with a training-frequency prior. + +FEATURESET (which probability-simplex scores were selected for deconvolution) + top156 The top 156 features (see procedure in the paper). + diagbackground Diagonal-background feature selection (see procedure in the paper). + gssatlas These runs also use the top-156 feature set but the entire pipeline is + run on Proxy-GSS-sorted atlas. + +VERSION + v1 First published release. + +REFERENCE GENOME (appears in tier3/tier4 folder names) + hg19, hg38 Human reference genome builds. + Taken from wgbs_tools; + +ATLAS NAMES (tier3-training-data/atlases) + U25, U250 Loyfer atlas variants (25 or 250 regions per cell type). + l4 The minimum length of called CpGs in read/fragment + considered when computing atlas values. + GSS_SORTED Proxy Gap Specificity Score (GSS) sorted atlas. + Each cell type is represented by the top 25 regions + starting from the highest GSS + trainonly Atlas built from the training split only. Otherwise, + atlas built from the entire dataset + + +5. ARCHIVE NAMES +---------------- + +Archives follow the same element style as result files: project first, +version last, underscores between elements, no spaces. + + __<...unit...>_.zip + +The middle elements identify the unit and vary by tier: + + tier0 one archive per result group + SYTO_tier0_results_baselines_v1.zip + SYTO_tier0_results_cfsortooddeconvolutionresults_v1.zip + + tier1 one archive per trained model (experiment + classifier + label mode) + SYTO_tier1_models_oodrrbs_dismir_softlabelpooled_v1.zip + + tier2 one archive per pseudobulk run, same elements as tier1 (pseudobulk is + build on the classifiers outputs and the mapping is one-to-one) + SYTO_tier2_pseudobulks_pseudobulk_methylbert_softlabelpooled_v1.zip + + tier3 one archive for atlases, one for target proportions, one per dataset + SYTO_tier3_trainingdata_atlases_v1.zip + SYTO_tier3_trainingdata_hg38_oodalldatalabels_v1.zip + + tier4 one archive per source dataset, and one per reference genome + SYTO_tier4_sourcedata_stagedu250_hg38_v1.zip + SYTO_tier4_sourcedata_referencegenomes_hg38_v1.zip + + +USING THE ARCHIVES +------------------ + +Members are stored with their path relative to the DEPOSIT ROOT. +Always unpack from the root, whichever folder an archive came from: + + cd /path/to/syto-publishable-data + find . -name 'SYTO_*.zip' -exec unzip -o -q {} ';' # everything, or + unzip tier1-models/ood-rrbs/SYTO_tier1models_oodrrbs_dismir_softlabelpooled_v1.zip + +Unpacking an archive from inside its own folder would nest the tree a second +time (tier1-models/ood-rrbs/tier1-models/...) and the configs would not +resolve. + +The reference-genome archives contain relative symlinks (genome.fa.gz -> +hg38.fa.gz) matching the wgbs_tools layout. Unpack them with a tool that +preserves symlinks; unzip on Linux and macOS does. + +6. NOTES ON CONTENT NOT ENCODED IN NAMES +---------------------------------------- + +- Missing-label strategy: every cfSort deconvolution result in tier0 was + produced with the prior blending strategy (see the paper). +- Deconvolver and calibrator are NOT in the file name. They vary within a + file and appear as the "Deconvolver" and "Calibrator" columns instead. +- All experiments were originally recorded using our local mlflow registry. + For the purposes of sharing we have stripped the data from the mlflow + related files, however, each element in tier1-models contains recorded + metrics, parameters and tags from classifier fitting. Hence, it should + be possible to create your own syntetic meta.yaml files to access + these metadata via mlflow. + +7. CONFIG PATHS +--------------- + +The published YAML configs use paths relative to that same root, so they +resolve once the archives holding the files they reference are unpacked. A +config will not find its inputs while they are still inside a zip. +The configs were automatically redacted for this data packaging purposes, +meaning that original configs were referring to different paths in either +logical filesystem or HPC. We didn't re-run all experiments after re-packaging +configs. Therefore, shared configs must be treated as the reference points, +meaning that the parameters are as in original experiments. Still, users +may need to correct pointers to the paths depending on how they unpack +the data. + +Two placeholders mark inputs this release does not redistribute: + + ${LOYFER_RECOVERED_READS} Loyfer recovered read tables: outputs of + https://github.com/CompEpigen/wgbs_atlas_simulation + ${SYTO_MLFLOW} An MLflow store, for the intermediate + *_predicted.pkl inputs that are not published. + Can also be any other intermediate store. + +The *_predicted.pkl were the original data splits enriched with the outputs +of the trained classifiers that were used as inputs to pseudobulks generation. +The pseudobulk pipeline is available to run in predictions_only mode, which +allows producing such files. This is handy when, for example, we need +to use a GPU-heavy classifier (like MethylBERT) for predictions, and we want +to run this stage on a more expensive GPU node, deferring pseudobulk generation +to a CPU-only node. While we did not publish those files, we have included two-stage +configs where relevant so users can reproduce pseudobulks. diff --git a/MANIFEST.csv b/MANIFEST.csv new file mode 100644 index 0000000000000000000000000000000000000000..381c64de5ca0cf63089d323718d6c0708ffef99a --- /dev/null +++ b/MANIFEST.csv @@ -0,0 +1,88 @@ +item,tier,description,bytes,files,sha256 +tier0-results/SYTO_tier0_results_baselines_v1.zip,tier0,Final result tables: baselines,381705958,37,1d6f5bc7bebeda1af11016f3c579627afde81b7efdcc6c61bcd094d963f6aedb +tier0-results/SYTO_tier0_results_cfsortooddeconvolutionresults_v1.zip,tier0,Final result tables: cfsort_ood_deconvolution_results,72581474,22,8c260e3e79419b20ce4e64a9ea434145f34e8a38f4e31f67417f9d0eae3aa9e0 +tier0-results/SYTO_tier0_results_mappings_v1.zip,tier0,Final result tables: mappings,10872,4,89f31478a4fcdd5167c66cf4b93a3c0d6dda232718675e71cff012a9ca1b690b +tier1-models/mle-deconvolvers/SYTO_tier1_models_mledeconvolvers_methylbert_hardlabelwithbackground2labels_v1.zip,tier1,Trained model: mle-deconvolvers / methylbert__hard_label_with_background__2labels,665930255,265,c2476d9529842370e0676881e557d377531aa8d0ad3950ca28a77f1b55d51b28 +tier1-models/mle-deconvolvers/SYTO_tier1_models_mledeconvolvers_methylbert_hardlabelwithbackground40labels_v1.zip,tier1,Trained model: mle-deconvolvers / methylbert__hard_label_with_background__40labels,665865023,265,ef631e30b67db8a621098c961840d6e67fa5c49286a1d2e0bca61ad02b9fb8c8 +tier1-models/ood-rrbs-gss/SYTO_tier1_models_oodrrbsgss_dismir_hardlabelwithbackground_v1.zip,tier1,Trained model: ood-rrbs-gss / dismir__hard_label_with_background,363485581,1091,d332f33056de1b33932e507f3ea7332e12d15609c484327a6b2312fca6c47268 +tier1-models/ood-rrbs-gss/SYTO_tier1_models_oodrrbsgss_dismir_softlabelnopooling_v1.zip,tier1,Trained model: ood-rrbs-gss / dismir__soft_label_no_pooling,353070174,1091,5eec484facaad6b0445a1572aaab5e5681957f6ba402d9c245f7c77439324ccd +tier1-models/ood-rrbs-gss/SYTO_tier1_models_oodrrbsgss_dismir_softlabelpooled_v1.zip,tier1,Trained model: ood-rrbs-gss / dismir__soft_label_pooled,353393718,1091,799dbde8903b2593be346062e44e16c451046b5e3623c6159fea749231f14a73 +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_cancerdetector_trainfreq_v1.zip,tier1,Trained model: ood-rrbs / cancer_detector__train_freq,362169897,1080,2898a4f59932eff7a38cc9a5e8fbbb05367206a5688ece2125909d827f841931 +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_cancerdetector_uniform_v1.zip,tier1,Trained model: ood-rrbs / cancer_detector__uniform,360648042,1080,579c2e5afc7d5b9ebe4369598950543f7827dfe72f92b9b83d09596e6d775715 +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_hardlabelsbackgroundandsmoothing_v1.zip,tier1,Trained model: ood-rrbs / dismir__hard_labels_background_and_smoothing,602669243,2156,ca24a5611e132669f4907226234f316f5a8b2f13296154fcee8190ef96909e4e +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_hardlabelwithbackground_v1.zip,tier1,Trained model: ood-rrbs / dismir__hard_label_with_background,611597224,2156,7bc312decf10861644e59a405e433395bf439c9ee3ce2f4a58d59fae38fd49ca +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_softlabelnopooling_v1.zip,tier1,Trained model: ood-rrbs / dismir__soft_label_no_pooling,352901063,1091,08a9934de4bd852a5d6d8b1208c7abfb04d90bbae2d6a0edaf3d5fe853bebd2a +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_softlabelpooled_v1.zip,tier1,Trained model: ood-rrbs / dismir__soft_label_pooled,359334740,2133,c2c39463df96fa565f9e723962383e2ef391b8f90885283d000140b75c579cad +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_lookup_hardlabelwithbackground_v1.zip,tier1,Trained model: ood-rrbs / lookup__hard_label_with_background,612796679,2148,f370e50d1027dc3b52fb9f4e2c92b4e1cabd7c77964faff9fb5b84059616afa6 +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_lookup_softlabelnopooling_v1.zip,tier1,Trained model: ood-rrbs / lookup__soft_label_no_pooling,361312445,1083,4ca2dcdcfa6c53faddf2c8b04ef0707c1f328e704454ec03cf4f3eb090bcf1fa +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_lookup_softlabelpooled_v1.zip,tier1,Trained model: ood-rrbs / lookup__soft_label_pooled,453917777,1083,cf04adadfc7ca74feaa3306ac59297f744721fbb401e187f0097b9ea212b9d44 +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_methylbert_hardlabelsbackgroundandsmoothing_v1.zip,tier1,Trained model: ood-rrbs / methylbert__hard_labels_background_and_smoothing,1303272595,2391,34d2b230057c7b4fb2f0e6e624cc7e7707103d81fe3a9871209eefd7cd7fc9e2 +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_methylbert_hardlabelwithbackground_v1.zip,tier1,Trained model: ood-rrbs / methylbert__hard_label_with_background,1319976544,2391,cdfdfe13bd633068bd6634361e14bda10e25275c97260d579cf1af5257053f0b +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_methylbert_softlabelnopooling_v1.zip,tier1,Trained model: ood-rrbs / methylbert__soft_label_no_pooling,1028690663,1326,a30ecd130261919dcaec69b919aa1a61e269f3b7364ebf9b4f430bb0fee049d3 +tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_methylbert_softlabelpooled_v1.zip,tier1,Trained model: ood-rrbs / methylbert__soft_label_pooled,1026257958,1326,0929195bef7f4e725a8b13e608df06fb0d9b86ab599e6cf40de396f4be18df29 +tier1-models/pooling-sensitivity/SYTO_tier1_models_poolingsensitivity_dismir_softlabelpooledminreads15_v1.zip,tier1,Trained model: pooling-sensitivity / dismir__soft_label_pooled_minreads_15,470839936,51,8dbfdfecab11d9cefca43b6c98b5a8b7c265079b43a6c3023c9051f0bd0d74fa +tier1-models/pooling-sensitivity/SYTO_tier1_models_poolingsensitivity_dismir_softlabelpooledminreads45_v1.zip,tier1,Trained model: pooling-sensitivity / dismir__soft_label_pooled_minreads_45,471222034,51,fac3d682b16b7c68a85d7b60dce13d3c7041ea6934a74347b2daee20c79d4aa6 +tier1-models/pooling-sensitivity/SYTO_tier1_models_poolingsensitivity_dismir_softlabelpooledtr02_v1.zip,tier1,Trained model: pooling-sensitivity / dismir__soft_label_pooled_tr0-2,467369250,51,743a317b77076a4acfc8894b58f171f6bca33169a4202c5bcf60a2afbc283c2f +tier1-models/pooling-sensitivity/SYTO_tier1_models_poolingsensitivity_dismir_softlabelpooledtr06_v1.zip,tier1,Trained model: pooling-sensitivity / dismir__soft_label_pooled_tr0-6,472111813,51,2f9a69cb4a4d31fd87423bad55282810b37e5af9a7f43ace339a520653e13b5e +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_cancerdetector_trainfreq_v1.zip,tier1,Trained model: pseudobulk / cancer_detector__train_freq,467725310,40,c7c846390d55532a84c82be16b2a5053fabfe8f8e3ef29c3c198678842fb1f46 +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_cancerdetector_uniform_v1.zip,tier1,Trained model: pseudobulk / cancer_detector__uniform,466179865,40,677de88ac2dc1b9f8af79aea22fa20ee3f3ac091c398f0f0e15789ab05126423 +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_dismir_hardlabelsbackgroundandsmoothing_v1.zip,tier1,Trained model: pseudobulk / dismir__hard_labels_background_and_smoothing,772247453,75,be3dca5b671599fb4944e07d672169b21242d7962fc9b6d2e091d613611a871e +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_dismir_hardlabelwithbackground_v1.zip,tier1,Trained model: pseudobulk / dismir__hard_label_with_background,789477336,75,c7444e5a57350d03ed5740875bbe598afde2744f0057b4d63ec183d6e2c02318 +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_dismir_softlabelnopooling_v1.zip,tier1,Trained model: pseudobulk / dismir__soft_label_no_pooling,460267665,50,c6ad4824a18394838d5992c5a00ff7adfb7acdd178d5411e3f56028adf6883cb +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_dismir_softlabelpooled_v1.zip,tier1,Trained model: pseudobulk / dismir__soft_label_pooled,941379246,62,c9f2c402e85cc323e0b9caad21bf6df4624aac8c02d4dc5e23fb0f9495f756b7 +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_lookup_hardlabelwithbackground_v1.zip,tier1,Trained model: pseudobulk / lookup__hard_label_with_background,728050127,67,83c9ab9432ed965cb77e29f87ade28ccd00b2752432d86f83ab6131e0172a979 +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_lookup_softlabelnopooling_v1.zip,tier1,Trained model: pseudobulk / lookup__soft_label_no_pooling,474549752,42,934a86bbfd97fce7631157bf24940a5d7466ab1d4e963bf136c6d6bccc26cdbf +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_lookup_softlabelpooled_v1.zip,tier1,Trained model: pseudobulk / lookup__soft_label_pooled,553777225,42,460006a4159befaab1302a0618031f0fcd4c288b5d078ce92d2c3a5b26d3ec32 +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_methylbert_hardlabelsbackgroundandsmoothing_v1.zip,tier1,Trained model: pseudobulk / methylbert__hard_labels_background_and_smoothing,1489088874,314,4e2ed4cd2739dca38ee16440f42d8690eb20c909e77df9fc938069ae42186ddb +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_methylbert_hardlabelwithbackground_v1.zip,tier1,Trained model: pseudobulk / methylbert__hard_label_with_background,1499403107,311,721ab88e76b0c27ade7b9b88b6789b42510328f38e80df7422b95f3fcb3b77bd +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_methylbert_softlabelnopooling_v1.zip,tier1,Trained model: pseudobulk / methylbert__soft_label_no_pooling,1147747261,289,47b75a1988243340591c63a694362bd18b718b97b8b9d5730babb338bd913cf5 +tier1-models/pseudobulk/SYTO_tier1_models_pseudobulk_methylbert_softlabelpooled_v1.zip,tier1,Trained model: pseudobulk / methylbert__soft_label_pooled,1123597897,286,a6e042db8fd931f7776433347fe12078c848f4dd6c9308ea96fc6620c3934b7d +tier2-pseudobulks/ood-rrbs-gss/SYTO_tier2_pseudobulks_oodrrbsgss_dismir_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: ood-rrbs-gss / dismir__hard_label_with_background,3944107181,15,46ad82ac6dccc5c88ff655cc8545f5fd9bbcd20a67c0344787502857b9f1397d +tier2-pseudobulks/ood-rrbs-gss/SYTO_tier2_pseudobulks_oodrrbsgss_dismir_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: ood-rrbs-gss / dismir__soft_label_no_pooling,3570055512,15,2aac9e78ccc287c27c38320ec39c661dba1bc09fead268ef5296cef1251b319a +tier2-pseudobulks/ood-rrbs-gss/SYTO_tier2_pseudobulks_oodrrbsgss_dismir_softlabelpooled_v1.zip,tier2,Generated pseudobulks: ood-rrbs-gss / dismir__soft_label_pooled,3519805299,15,1a36b3c29b1d054c35b222438f0275feddc9b8a343443b9092a96c4374563916 +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_cancerdetector_trainfreq_v1.zip,tier2,Generated pseudobulks: ood-rrbs / cancer_detector__train_freq,3374107603,14,59825b139e257b9a1aadf2da4ee5d7c63060d64166e3219f73637eaf14fd520b +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_cancerdetector_uniform_v1.zip,tier2,Generated pseudobulks: ood-rrbs / cancer_detector__uniform,3380117438,14,6dfe875a9d157102b03967766bdf96a02bf9d2b0bdcf25cc30b0ad4b321faed6 +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_dismir_hardlabelsbackgroundandsmoothing_v1.zip,tier2,Generated pseudobulks: ood-rrbs / dismir__hard_labels_background_and_smoothing,4045149674,15,825a521d714d42ff4a33a37c4b5a7e90903311c26e99729fba93b6a99eac7733 +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_dismir_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: ood-rrbs / dismir__hard_label_with_background,4430893921,15,eef85da56aa79ae5f699e21faeeae3a301c439a5e08f506c0cdc5973a35393e9 +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_dismir_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: ood-rrbs / dismir__soft_label_no_pooling,4230845535,15,0c895c20a91255338c9236db003d3b0c0e1ea65f1fc5f7d63e011e74e159b22e +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_dismir_softlabelpooled_v1.zip,tier2,Generated pseudobulks: ood-rrbs / dismir__soft_label_pooled,3815064441,15,15f57c558acd3244e2f820595e66decb078b8bc598ab93208f5ecc6d21e4707e +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_lookup_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: ood-rrbs / lookup__hard_label_with_background,417511194,14,7fc3b0f77796004dbfca8ca79f3073ea0845728bb0570598a80d3be9e8a7253e +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_lookup_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: ood-rrbs / lookup__soft_label_no_pooling,3355329242,14,ba6e99e93a7c2ef1d73d38f0cc79989eb5380b9db1a92c377aca926da243eccf +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_lookup_softlabelpooled_v1.zip,tier2,Generated pseudobulks: ood-rrbs / lookup__soft_label_pooled,3459660850,14,1f53ee0064bcc7cd8a6fc7c7c7065b78a2b7b8fad63ffc023fcffcfb1f900baf +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_methylbert_hardlabelsbackgroundandsmoothing_v1.zip,tier2,Generated pseudobulks: ood-rrbs / methylbert__hard_labels_background_and_smoothing,3889871590,15,026817de49270afbc92af731313a6afe731f315cfebfafb8a7b120ea70a65f51 +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_methylbert_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: ood-rrbs / methylbert__hard_label_with_background,3983578715,15,9562e66955ca0b5560557905ea22e3e1e562e141b3fec62ec525f319377e8944 +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_methylbert_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: ood-rrbs / methylbert__soft_label_no_pooling,3805019954,15,89da0525082b73d568814139a60b8be609f7c0093b4e286886926cb4dbb54f57 +tier2-pseudobulks/ood-rrbs/SYTO_tier2_pseudobulks_oodrrbs_methylbert_softlabelpooled_v1.zip,tier2,Generated pseudobulks: ood-rrbs / methylbert__soft_label_pooled,3770840907,15,6710154f5361cad6658e7af80aefaf6d617c79861ded2efc32fcbbf133a4b3b0 +tier2-pseudobulks/pooling-sensitivity/SYTO_tier2_pseudobulks_poolingsensitivity_dismir_softlabelpooledminreads15_v1.zip,tier2,Generated pseudobulks: pooling-sensitivity / dismir__soft_label_pooled_minreads_15,5244756628,20,80400bb8540b3784fe95c9bc131fd3cb4d99df1734cdfa49b572ac415456914a +tier2-pseudobulks/pooling-sensitivity/SYTO_tier2_pseudobulks_poolingsensitivity_dismir_softlabelpooledminreads45_v1.zip,tier2,Generated pseudobulks: pooling-sensitivity / dismir__soft_label_pooled_minreads_45,5157046700,20,763e4b943295012aad0c51437a1072440257ac17dc94a68bb5978332e49e7a34 +tier2-pseudobulks/pooling-sensitivity/SYTO_tier2_pseudobulks_poolingsensitivity_dismir_softlabelpooledtr02_v1.zip,tier2,Generated pseudobulks: pooling-sensitivity / dismir__soft_label_pooled_tr0-2,5166704118,20,3ed688be92090a3323f590cdac21744e2d10b31c52a935ce3075373d73dfd5e5 +tier2-pseudobulks/pooling-sensitivity/SYTO_tier2_pseudobulks_poolingsensitivity_dismir_softlabelpooledtr06_v1.zip,tier2,Generated pseudobulks: pooling-sensitivity / dismir__soft_label_pooled_tr0-6,5118756155,20,f9eb8858497cf21ebc50dbea41183e9454c9982c81dd9ba6c267323396a0f26f +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_cancerdetector_trainfreq_v1.zip,tier2,Generated pseudobulks: pseudobulk / cancer_detector__train_freq,4923202430,19,44169c7554cf7e2945a77b775db48ad7f6f6dc5575871a756e6adb1bf4ef8138 +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_cancerdetector_uniform_v1.zip,tier2,Generated pseudobulks: pseudobulk / cancer_detector__uniform,4932406335,19,2521b7dc7b16569713c576d36edffbbc5c1b8d5241754a6178e553c28bfc75f0 +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_dismir_hardlabelsbackgroundandsmoothing_v1.zip,tier2,Generated pseudobulks: pseudobulk / dismir__hard_labels_background_and_smoothing,5345266035,20,da496fd0886cbc88727527b7b2ef7a337246db919e3bcee176dbaa11a3ad88fe +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_dismir_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: pseudobulk / dismir__hard_label_with_background,5622343855,20,537063c9fa607454a3148922df25c3a56c6335678947266319f3f56d1c37519c +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_dismir_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: pseudobulk / dismir__soft_label_no_pooling,5383182614,20,71f2458cfc971744da4312345d82edd3470053ca04f4d06e3488dbc908a9f3cd +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_dismir_softlabelpooled_v1.zip,tier2,Generated pseudobulks: pseudobulk / dismir__soft_label_pooled,5077217404,19,37b788f2fc1f83fb201ed3006cf34c3ce7004f0672e23301fd07353491152751 +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_lookup_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: pseudobulk / lookup__hard_label_with_background,611084614,19,b9aafdc62bc64cc6d6841221029605fec168f4d0030cdb85012338afef735660 +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_lookup_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: pseudobulk / lookup__soft_label_no_pooling,4959426613,19,fd568db7b25e3ce88cbd1dd941504425c9c4e0c633fe77093bf984857c1f1991 +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_lookup_softlabelpooled_v1.zip,tier2,Generated pseudobulks: pseudobulk / lookup__soft_label_pooled,5065703160,19,f5fa53762ab1badfd2b4d879becd452296f5c57f06cc749a9f1e84f6ffa0b57c +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_methylbert_hardlabelsbackgroundandsmoothing_v1.zip,tier2,Generated pseudobulks: pseudobulk / methylbert__hard_labels_background_and_smoothing,5566090386,20,fe5e590c55c65caacf6d75cc490669875071cd59263596b585373c99da8e2de3 +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_methylbert_hardlabelwithbackground_v1.zip,tier2,Generated pseudobulks: pseudobulk / methylbert__hard_label_with_background,5655587087,20,e29d13082ed0e58bded6ea8a6783af40dd793feb4438fe91962fad916d7ffd3c +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_methylbert_softlabelnopooling_v1.zip,tier2,Generated pseudobulks: pseudobulk / methylbert__soft_label_no_pooling,5414865692,20,b47504f88281c26ba9fb01c104c36d1c9061f68b3bc996bda6033cb33b51d0a7 +tier2-pseudobulks/pseudobulk/SYTO_tier2_pseudobulks_pseudobulk_methylbert_softlabelpooled_v1.zip,tier2,Generated pseudobulks: pseudobulk / methylbert__soft_label_pooled,5306983642,20,eec47aa85d1e59cede089d9594bc399c1ff80421f95f741ef4a41d9c64abc821 +tier3-training-data/SYTO_tier3_trainingdata_atlases_v1.zip,tier3,Training inputs: atlases,4897139,10,e1e132fd87ba58f341f9154a27a077a3dca15a0665664fc7da60f65f7ad41459 +tier3-training-data/SYTO_tier3_trainingdata_targetproportions_v1.zip,tier3,Training inputs: target proportions,16675054,3,6455a3e2316a0f99a512ac20223ade5cc71782381e89dfb197e79bc491911cc1 +tier3-training-data/datasets/hg19/SYTO_tier3_trainingdata_hg19_oodalldatalabels_v1.zip,tier3,Training dataset: hg19 / ood-alldata-labels,2174012872,129,a457ea5c8dd6b2b538943dfdec85184a153a466f8ef852284e8b357cfc51f3e5 +tier3-training-data/datasets/hg19/SYTO_tier3_trainingdata_hg19_pseudobulktrainlabels_v1.zip,tier3,Training dataset: hg19 / pseudobulk-train-labels,1955902581,129,3de3b5898a843a10ba032a65484fd8b956690f9869c9d18ffcfaa958af360c46 +tier3-training-data/datasets/hg38/SYTO_tier3_trainingdata_hg38_oodalldatalabels_v1.zip,tier3,Training dataset: hg38 / ood-alldata-labels,2373573227,129,7b26c7648e5714aa70987affe238b525ab21f07b23728c030eb103745e2e0a2b +tier3-training-data/datasets/hg38/SYTO_tier3_trainingdata_hg38_oodgssalldatalabels_v1.zip,tier3,Training dataset: hg38 / ood-gss-alldata-labels,2131405884,129,312c72ee5c68ac4ab5a3a40a2b484b2d3b9e6a5e28c066e647b2a6660d0cb0e5 +tier3-training-data/datasets/hg38/SYTO_tier3_trainingdata_hg38_poolingsensitivitylabels_v1.zip,tier3,Training dataset: hg38 / pooling-sensitivity-labels,1825093363,129,c9b5e37197a2b502b588b5a4d8abdbdc084418699723014643e0a6db7d8264c9 +tier3-training-data/datasets/hg38/SYTO_tier3_trainingdata_hg38_pseudobulktrainlabels_v1.zip,tier3,Training dataset: hg38 / pseudobulk-train-labels,2124803297,129,5795a02eea819ee0391900a4b120ba46b7028c8827899958d8ba9d057a9e5038 +tier4-source-data/reference-genomes/SYTO_tier4_sourcedata_referencegenomes_hg19_v1.zip,tier4,Source data: reference-genomes / hg19,1115262363,15,4cf364ddf4542de4478aab0e74f9c3f380f1e5308328f9d3cb9f82cc8df71569 +tier4-source-data/reference-genomes/SYTO_tier4_sourcedata_referencegenomes_hg38_v1.zip,tier4,Source data: reference-genomes / hg38,1153763877,13,828f1f72800c97bf49a858cea9ce7946a5cf295a73484fd58a550f9a31f8e977 +tier4-source-data/rrbs-recovered-reads/SYTO_tier4_sourcedata_rrbsrecoveredreads_U250l4hg19_v1.zip,tier4,Source data: rrbs-recovered-reads / U250.l4.hg19,1184122671,521,f402f60fd129bd3d4db9861a8b41e33400912d1ce98e537508c050f8dafe58b4 +tier4-source-data/staged-u250/SYTO_tier4_sourcedata_stagedu250_hg19_v1.zip,tier4,Source data: staged-u250 / hg19,4099519881,26449,ac5960d8eaecbe946811ac19a43268cb43b2236c83a6c8b22cb9088c9e46df35 +tier4-source-data/staged-u250/SYTO_tier4_sourcedata_stagedu250_hg38_v1.zip,tier4,Source data: staged-u250 / hg38,4235769475,26449,1b8fbfee12e2c910a5b8406890442cd45dcecea12ff4f0c098e2adf9dec245b3 +FILE_NAMING_CONVENTION.txt,,"Naming convention, abbreviations and how to unpack the archives",10408,1,d96f337e133a1858b6b98b57ca899d38388bc100d8f1825111929877359362cf +runs.csv,,One row per experiment run,13911,1,b8a7f92c74fcb8b358d384febfde8f49a362d9b5a06958c47e060adc2fb8b3a9 +tier0-results/SYTO_tier0_results_jointexperimentsranking.tsv,,Loose file,88806,1,48c8dbd3e4413d9a0975bad3671dce537df77417791143654356649a59e95e62 diff --git a/MANIFEST_CONTENTS.csv b/MANIFEST_CONTENTS.csv new file mode 100644 index 0000000000000000000000000000000000000000..f1d8332a900fca5d2cc811e6090dfacf5ee1cf44 --- /dev/null +++ b/MANIFEST_CONTENTS.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f23c7854200e9a436e7f4187e324fe13c90032e335c01ea592a9fcf7d5e79bf +size 17896232 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a121f74f91896f277265cf509cccfa6c358a39f4 --- /dev/null +++ b/README.md @@ -0,0 +1,170 @@ +--- +license: cc-by-4.0 +pretty_name: "SYTO v1.0 — data-driven soft labeling for whole-body cell-type deconvolution" +viewer: false +tags: + - biology + - genomics + - epigenomics + - dna-methylation + - cell-type-deconvolution + - read-classification +--- + +# SYTO v1.0 publication data + +Data underlying *"Data-driven soft labeling scales DNA read classification to +whole-body cell-type deconvolution"*. SYTO is a framework for read-level DNA +methylation classification and whole-body cell-type deconvolution. This deposit +contains the trained models, generated pseudobulk mixtures, training data, +source reads and final result tables behind every figure and table in the paper. + +> **This repository is a mirror.** +> The canonical, citable version of record is TBD. +> This mirror exists to make the data easier +> to fetch programmatically; contents are identical to the planed citable deposit. + +- **Size:** 190.4 GB across 89 files (84 `.zip` archives + 4 acompanying metadata files + 1 csv file with Syto variants and baselines ranking) +- **Unpacked:** 82,002 files +- **Largest file:** 5.66 GB + +## Contents + +The deposit is organised in five tiers of decreasing necessity, so you can take +only the depth you need. + +| Tier | Contents | Size | +|---|---|---| +| `tier0-results/` | Final result tables underlying important published figures and tables | 0.5 GB | +| `tier1-models/` | Trained classifiers, deconvolvers and calibrators | 24.0 GB | +| `tier2-pseudobulks/` | Generated pseudobulk mixtures (columnar Parquet) | 141.5 GB | +| `tier3-training-data/` | Marker atlases, target proportions, training datasets | 12.6 GB | +| `tier4-source-data/` | Staged and recovered reads, hg19/hg38 reference genomes | 11.8 GB | + +**`tier0-results/` alone (0.5 GB) is enough to inspect the final results of the paper.** +Tiers 1–4 exist so the analysis can be independently re-run and verified. + +Metadata files are readable without downloading anything large: + +| File | Purpose | +|---|---| +| `MANIFEST.csv` | One row per archive: description, size, file count, SHA-256 | +| `MANIFEST_CONTENTS.csv` | One row per file *inside* the archives — inspect an archive's contents without downloading it | +| `runs.csv` | One row per experiment run, linking tier1 ↔ tier2 ↔ tier3 paths | +| `FILE_NAMING_CONVENTION.txt` | Full naming scheme and every abbreviation used | + +## Downloading + +Archives are stored as-is; there is no dataset viewer. Fetch selectively. + +Inspect what exists first — both manifests are small: + +```python +import pandas as pd +pd.read_csv("hf://datasets/CompEpigen/syto.1.0/MANIFEST.csv") +pd.read_csv("hf://datasets/CompEpigen/syto.1.0/MANIFEST_CONTENTS.csv") +``` + +Just the results (0.5 GB): + +```bash +hf download CompEpigen/syto.1.0 --repo-type=dataset \ + --include "tier0-results/*" "*.csv" "*.txt" --local-dir ./syto-data +``` + +One specific run: + +```bash +hf download CompEpigen/syto.1.0 --repo-type=dataset \ + --include "tier1-models/ood-rrbs/SYTO_tier1_models_oodrrbs_dismir_softlabelpooled_v1.zip" \ + --local-dir ./syto-data +``` + +Everything (190 GB): + +```bash +hf download CompEpigen/syto.1.0 --repo-type=dataset --local-dir ./syto-data +``` + +## Unpacking + +**Always unpack from the deposit root.** Archive members are stored with paths +relative to the root, so unpacking an archive from inside its own folder nests +the tree a second time (`tier1-models/ood-rrbs/tier1-models/...`) + +```bash +cd ./syto +find . -name 'SYTO_*.zip' -exec unzip -o -q {} ';' +``` + +The reference-genome archives contain relative symlinks +(`genome.fa.gz -> hg38.fa.gz`) matching the `wgbs_tools` layout. Unpack them +with a tool that preserves symlinks — `unzip` on Linux and macOS does. + +## Verifying + +`MANIFEST.csv` carries a SHA-256 for every archive: + +```bash +python - <<'PY' +import csv, hashlib, pathlib +for r in csv.DictReader(open("MANIFEST.csv")): + p = pathlib.Path(r["item"]) + if not p.exists(): + continue + h = hashlib.sha256() + with p.open("rb") as f: + for chunk in iter(lambda: f.read(1 << 20), b""): + h.update(chunk) + print("OK " if h.hexdigest() == r["sha256"] else "BAD", p) +PY +``` + +## Naming convention + +See `FILE_NAMING_CONVENTION.txt` for the complete scheme, including feature +sets, atlas names and reference-genome codes. + +## What is not included + +Redundant, regenerable and training-only artefacts were removed before deposit: +duplicated inputs, per-calibrator predictions +regenerable from the calibrator plus features, optimizer states, intermediates +superseded by the deposited aggregate tables, and diagnostic plots not part of +the published results. + +Two inputs are referenced by the published configs but not redistributed here, +marked by placeholders: + +- `${LOYFER_RECOVERED_READS}` — Loyfer recovered read tables; outputs of + [CompEpigen/wgbs_atlas_simulation](https://github.com/CompEpigen/wgbs_atlas_simulation) +- `${SYTO_MLFLOW}` — an intermediate store for the `*_predicted.pkl` files + (data splits enriched with trained-classifier outputs, used as pseudobulk + inputs). The pseudobulk pipeline runs in `predictions_only` mode to produce + them; two-stage configs are included where relevant. + +Experiments were originally recorded in a local MLflow registry. MLflow-specific +files were stripped for release, but each `tier1-models` element retains its +recorded metrics, parameters and tags. + +**A note on configs:** published YAML configs use paths relative to the deposit +root and were automatically redacted from their original HPC paths. Experiments +were not re-run after repackaging, so treat the configs as authoritative for +*parameters* while expecting to adjust *paths* to match your unpacking layout. + +## Citation + +```bibtex +@article{rizdvanetskyi2026data, + title={Data-Driven Soft Labeling Scales DNA Read Classification to Whole-Body Cell-Type Deconvolution}, + author={Rizdvanetskyi, Dmytro and Roos, Nathan and Lutsik, Pavlo}, + journal={arXiv preprint arXiv:2607.04987}, + year={2026} +} +``` + +Thee dataset DOI is TBD. + +## License + +Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). diff --git a/runs.csv b/runs.csv new file mode 100644 index 0000000000000000000000000000000000000000..b161b45e0f3ed624f22b0b47dbd98684e870ed79 --- /dev/null +++ b/runs.csv @@ -0,0 +1,36 @@ 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0000000000000000000000000000000000000000..8a7ad3e34a269f5fd1a42d28474d3d4a6e4a9d85 --- /dev/null +++ b/tier0-results/SYTO_tier0_results_jointexperimentsranking.tsv @@ -0,0 +1,377 @@ +Classifier Labeling Scheme Prior Feature Scheme Deconvolver Calibrator method method_key pseudobulk_r2 tcs rank_pseudobulk rank_tcs avg_rank +methylbert soft_labels_with_pooling top156 psls linear_simplex_projection methylbert / soft_labels_with_pooling / top156 / psls / linear_simplex_projection methylbert|soft_labels_with_pooling||top156|psls|linear_simplex_projection 0.9889460568421344 0.7123821700160654 6 27 17 +methylbert soft_labels_with_pooling top156 swn linear_simplex_projection methylbert / soft_labels_with_pooling / top156 / swn / linear_simplex_projection methylbert|soft_labels_with_pooling||top156|swn|linear_simplex_projection 0.9849812605861028 0.7249655177910103 35 10 23 +methylbert soft_labels_with_pooling top156 swn linear_clip_normalize methylbert / soft_labels_with_pooling / top156 / swn / linear_clip_normalize methylbert|soft_labels_with_pooling||top156|swn|linear_clip_normalize 0.9859499421023464 0.7129535363514738 26 23 25 +methylbert soft_labels_with_pooling top156 swn vector_scaling methylbert / soft_labels_with_pooling / top156 / swn / vector_scaling methylbert|soft_labels_with_pooling||top156|swn|vector_scaling 0.9837954924267952 0.7247388694619842 51 11 31 +methylbert soft_labels_with_pooling top156 nnls linear_simplex_projection methylbert / soft_labels_with_pooling / top156 / nnls / linear_simplex_projection methylbert|soft_labels_with_pooling||top156|nnls|linear_simplex_projection 0.9893797481101504 0.6960109317498391 1 64 33 +dismir hard_labels top156 nnls linear_simplex_projection dismir / hard_labels / top156 / nnls / linear_simplex_projection dismir|hard_labels||top156|nnls|linear_simplex_projection 0.9868485685376084 0.6986625865471543 17 59 38 +dismir soft_labels_without_pooling top156 swn linear_clip_normalize dismir / soft_labels_without_pooling / top156 / swn / linear_clip_normalize dismir|soft_labels_without_pooling||top156|swn|linear_clip_normalize 0.9841781135562466 0.7106805385291166 45 32 39 +methylbert soft_labels_with_pooling top156 swn uncalibrated methylbert / soft_labels_with_pooling / top156 / swn / uncalibrated methylbert|soft_labels_with_pooling||top156|swn|uncalibrated 0.984948289629694 0.7070784457064805 36 42 39 +methylbert soft_labels_with_pooling top156 mlp linear_simplex_projection methylbert / soft_labels_with_pooling / top156 / mlp / linear_simplex_projection methylbert|soft_labels_with_pooling||top156|mlp|linear_simplex_projection 0.9852368929266168 0.6990076999905004 32 58 45 +dismir soft_labels_without_pooling top156 swn linear_simplex_projection dismir / soft_labels_without_pooling / top156 / swn / linear_simplex_projection dismir|soft_labels_without_pooling||top156|swn|linear_simplex_projection 0.9836535123455566 0.710351101787718 57 34 46 +dismir soft_labels_without_pooling top156 nnls linear_simplex_projection dismir / soft_labels_without_pooling / top156 / nnls / linear_simplex_projection dismir|soft_labels_without_pooling||top156|nnls|linear_simplex_projection 0.9889154605791995 0.6878419654377618 7 87 47 +dismir soft_labels_without_pooling top156 psls linear_simplex_projection dismir / soft_labels_without_pooling / top156 / psls / linear_simplex_projection dismir|soft_labels_without_pooling||top156|psls|linear_simplex_projection 0.9887841368623989 0.6849057868330028 8 93 51 +dismir soft_labels_without_pooling top156 swn vector_scaling dismir / soft_labels_without_pooling / top156 / swn / vector_scaling dismir|soft_labels_without_pooling||top156|swn|vector_scaling 0.982872426127438 0.7126930106627609 76 25 51 +dismir soft_labels_without_pooling top156 swn uncalibrated dismir / soft_labels_without_pooling / top156 / swn / uncalibrated dismir|soft_labels_without_pooling||top156|swn|uncalibrated 0.982377574272848 0.7132983962140551 84 22 53 +cancerdetector train_freq_prior top156 mlp linear_simplex_projection cancerdetector / train_freq_prior / top156 / mlp / linear_simplex_projection cancerdetector||train_freq_prior|top156|mlp|linear_simplex_projection 0.9828124201171644 0.7121625413638448 79 29 54 +cancerdetector train_freq_prior top156 swn vector_scaling cancerdetector / train_freq_prior / top156 / swn / vector_scaling cancerdetector||train_freq_prior|top156|swn|vector_scaling 0.9832055782196714 0.705622812597497 64 45 55 +cancerdetector train_freq_prior top156 swn linear_simplex_projection cancerdetector / train_freq_prior / top156 / swn / linear_simplex_projection cancerdetector||train_freq_prior|top156|swn|linear_simplex_projection 0.983689885239182 0.701142929506541 56 54 55 +cancerdetector train_freq_prior top156 mlp vector_scaling cancerdetector / train_freq_prior / top156 / mlp / vector_scaling cancerdetector||train_freq_prior|top156|mlp|vector_scaling 0.9828818738250352 0.7102737142639475 75 35 55 +methylbert soft_labels_with_pooling top156 psls linear_clip_normalize methylbert / soft_labels_with_pooling / top156 / psls / linear_clip_normalize methylbert|soft_labels_with_pooling||top156|psls|linear_clip_normalize 0.986105276492809 0.687050291341878 25 89 57 +methylbert soft_labels_with_pooling top156 mlp vector_scaling methylbert / soft_labels_with_pooling / top156 / mlp / vector_scaling methylbert|soft_labels_with_pooling||top156|mlp|vector_scaling 0.9846232754470337 0.6935521795428772 40 74 57 +cancerdetector train_freq_prior top156 nnls linear_simplex_projection cancerdetector / train_freq_prior / top156 / nnls / linear_simplex_projection cancerdetector||train_freq_prior|top156|nnls|linear_simplex_projection 0.987350519490976 0.680367774601671 10 106 58 +dismir soft_labels_without_pooling top156 nnls linear_clip_normalize dismir / soft_labels_without_pooling / top156 / nnls / linear_clip_normalize dismir|soft_labels_without_pooling||top156|nnls|linear_clip_normalize 0.9858092080687354 0.6844412666035502 27 95 61 +dismir hard_labels top156 nnls linear_clip_normalize dismir / hard_labels / top156 / nnls / linear_clip_normalize dismir|hard_labels||top156|nnls|linear_clip_normalize 0.9835807218534274 0.6967781482933035 60 62 61 +methylbert soft_labels_with_pooling top156 nnls vector_scaling methylbert / soft_labels_with_pooling / top156 / nnls / vector_scaling methylbert|soft_labels_with_pooling||top156|nnls|vector_scaling 0.9818302664893322 0.711851707357511 93 31 62 +methylbert soft_labels_without_pooling top156 psls linear_simplex_projection methylbert / soft_labels_without_pooling / top156 / psls / linear_simplex_projection methylbert|soft_labels_without_pooling||top156|psls|linear_simplex_projection 0.986159372696786 0.6808805615882352 24 105 65 +dismir soft_labels_without_pooling top156 psls linear_clip_normalize dismir / soft_labels_without_pooling / top156 / psls / linear_clip_normalize dismir|soft_labels_without_pooling||top156|psls|linear_clip_normalize 0.9856631476970092 0.6815175252333353 29 102 66 +dismir soft_labels_without_pooling top156 mlp vector_scaling dismir / soft_labels_without_pooling / top156 / mlp / vector_scaling dismir|soft_labels_without_pooling||top156|mlp|vector_scaling 0.9831470412789792 0.6960772846712647 68 63 66 +methylbert soft_labels_with_pooling top156 psls vector_scaling methylbert / soft_labels_with_pooling / top156 / psls / vector_scaling methylbert|soft_labels_with_pooling||top156|psls|vector_scaling 0.9807113683565536 0.7162803067930814 112 19 66 +methylbert soft_labels_with_pooling top156 mlp linear_clip_normalize methylbert / soft_labels_with_pooling / top156 / mlp / linear_clip_normalize methylbert|soft_labels_with_pooling||top156|mlp|linear_clip_normalize 0.9843897694680924 0.686273511572954 42 91 67 +dismir soft_labels_without_pooling top156 nnls vector_scaling dismir / soft_labels_without_pooling / top156 / nnls / vector_scaling dismir|soft_labels_without_pooling||top156|nnls|vector_scaling 0.9832634395494776 0.6926011955593835 62 77 70 +cancerdetector train_freq_prior top156 psls linear_simplex_projection cancerdetector / train_freq_prior / top156 / psls / linear_simplex_projection cancerdetector||train_freq_prior|top156|psls|linear_simplex_projection 0.9890178950463075 0.6703633513070539 5 135 70 +dismir soft_labels_with_pooling top156 swn linear_clip_normalize dismir / soft_labels_with_pooling / top156 / swn / linear_clip_normalize dismir|soft_labels_with_pooling||top156|swn|linear_clip_normalize 0.9852290863402268 0.6793922260344323 33 108 71 +dismir hard_labels top156 swn linear_clip_normalize dismir / hard_labels / top156 / swn / linear_clip_normalize dismir|hard_labels||top156|swn|linear_clip_normalize 0.9811902220699448 0.7097373891726142 106 36 71 +cancerdetector uniform_prior top156 xgb linear_simplex_projection cancerdetector / uniform_prior / top156 / xgb / linear_simplex_projection cancerdetector||uniform_prior|top156|xgb|linear_simplex_projection 0.9798343417460448 0.7266449019994848 134 8 71 +methylbert soft_labels_with_pooling top156 nnls linear_clip_normalize methylbert / soft_labels_with_pooling / top156 / nnls / linear_clip_normalize methylbert|soft_labels_with_pooling||top156|nnls|linear_clip_normalize 0.9869778774295486 0.6732129714840747 15 128 72 +dismir soft_labels_with_pooling top156 swn linear_simplex_projection dismir / soft_labels_with_pooling / top156 / swn / linear_simplex_projection dismir|soft_labels_with_pooling||top156|swn|linear_simplex_projection 0.9850257506909972 0.6790194425399453 34 109 72 +cancerdetector uniform_prior top156 swn vector_scaling cancerdetector / uniform_prior / top156 / swn / vector_scaling cancerdetector||uniform_prior|top156|swn|vector_scaling 0.9833298389001331 0.6893681069051981 61 83 72 +cancerdetector train_freq_prior top156 xgb linear_simplex_projection cancerdetector / train_freq_prior / top156 / xgb / linear_simplex_projection cancerdetector||train_freq_prior|top156|xgb|linear_simplex_projection 0.979632501947134 0.7434190065379813 141 6 74 +cancerdetector train_freq_prior top156 mlp linear_clip_normalize cancerdetector / train_freq_prior / top156 / mlp / linear_clip_normalize cancerdetector||train_freq_prior|top156|mlp|linear_clip_normalize 0.981660000507572 0.701797398542555 95 53 74 +dismir soft_labels_with_pooling top156 swn vector_scaling dismir / soft_labels_with_pooling / top156 / swn / vector_scaling dismir|soft_labels_with_pooling||top156|swn|vector_scaling 0.9837692930958752 0.6825836810657926 52 100 76 +cancerdetector train_freq_prior top156 nnls vector_scaling cancerdetector / train_freq_prior / top156 / nnls / vector_scaling cancerdetector||train_freq_prior|top156|nnls|vector_scaling 0.9810469193165378 0.7045942606679875 107 46 77 +cancerdetector train_freq_prior top156 swn linear_clip_normalize cancerdetector / train_freq_prior / top156 / swn / linear_clip_normalize cancerdetector||train_freq_prior|top156|swn|linear_clip_normalize 0.9829437743455788 0.6891716163047088 72 84 78 +cancerdetector uniform_prior top156 nnls vector_scaling cancerdetector / uniform_prior / top156 / nnls / vector_scaling cancerdetector||uniform_prior|top156|nnls|vector_scaling 0.9814413717183622 0.6977102491006516 97 60 79 +cancerdetector uniform_prior top156 nnls linear_simplex_projection cancerdetector / uniform_prior / top156 / nnls / linear_simplex_projection cancerdetector||uniform_prior|top156|nnls|linear_simplex_projection 0.9863738891374152 0.6703546347969845 22 136 79 +methylbert soft_labels_without_pooling top156 nnls linear_simplex_projection methylbert / soft_labels_without_pooling / top156 / nnls / linear_simplex_projection methylbert|soft_labels_without_pooling||top156|nnls|linear_simplex_projection 0.9847556186856392 0.6753600813966922 38 122 80 +cancerdetector uniform_prior top156 swn linear_simplex_projection cancerdetector / uniform_prior / top156 / swn / linear_simplex_projection cancerdetector||uniform_prior|top156|swn|linear_simplex_projection 0.9835916154749578 0.6812156243816571 59 103 81 +dismir hard_labels top156 swn uncalibrated dismir / hard_labels / top156 / swn / uncalibrated dismir|hard_labels||top156|swn|uncalibrated 0.9797723909074189 0.7122726267562383 137 28 83 +dismir soft_labels_with_pooling top156 swn uncalibrated dismir / soft_labels_with_pooling / top156 / swn / uncalibrated dismir|soft_labels_with_pooling||top156|swn|uncalibrated 0.9831518896307424 0.6817703481230568 67 101 84 +dismir hard_labels top156 swn linear_simplex_projection dismir / hard_labels / top156 / swn / linear_simplex_projection dismir|hard_labels||top156|swn|linear_simplex_projection 0.9799322848818434 0.7095915379304734 130 38 84 +dismir soft_labels_without_pooling top156 psls vector_scaling dismir / soft_labels_without_pooling / top156 / psls / vector_scaling dismir|soft_labels_without_pooling||top156|psls|vector_scaling 0.9822944495832155 0.6870671558652415 85 88 87 +dismir hard_labels top156 nnls vector_scaling dismir / hard_labels / top156 / nnls / vector_scaling dismir|hard_labels||top156|nnls|vector_scaling 0.9788966544936244 0.7172651565577153 155 18 87 +cancerdetector uniform_prior top156 psls vector_scaling cancerdetector / uniform_prior / top156 / psls / vector_scaling cancerdetector||uniform_prior|top156|psls|vector_scaling 0.984580310494928 0.6707232403506403 41 134 88 +dismir hard_labels diagbckg psls linear_simplex_projection dismir / hard_labels / diagbckg / psls / linear_simplex_projection dismir|hard_labels||diagbckg|psls|linear_simplex_projection 0.9869943425098922 0.6649849528307514 13 164 89 +cancerdetector train_freq_prior top156 psls vector_scaling cancerdetector / train_freq_prior / top156 / psls / vector_scaling cancerdetector||train_freq_prior|top156|psls|vector_scaling 0.9836001700093824 0.6756306051719915 58 119 89 +dismir hard_labels top156 psls linear_simplex_projection dismir / hard_labels / top156 / psls / linear_simplex_projection dismir|hard_labels||top156|psls|linear_simplex_projection 0.9869892145877386 0.6649751037272349 14 165 90 +dismir soft_labels_without_pooling top156 mlp linear_simplex_projection dismir / soft_labels_without_pooling / top156 / mlp / linear_simplex_projection dismir|soft_labels_without_pooling||top156|mlp|linear_simplex_projection 0.9812424391721184 0.6925236185034693 103 78 91 +lookup soft_labels_with_pooling top156 psls linear_simplex_projection lookup / soft_labels_with_pooling / top156 / psls / linear_simplex_projection lookup|soft_labels_with_pooling||top156|psls|linear_simplex_projection 0.9826997913401808 0.681118549776542 80 104 92 +methylbert soft_labels_without_pooling top156 swn linear_simplex_projection methylbert / soft_labels_without_pooling / top156 / swn / linear_simplex_projection methylbert|soft_labels_without_pooling||top156|swn|linear_simplex_projection 0.9820231859008456 0.6857475295076899 92 92 92 +dismir soft_labels_with_pooling top156 psls linear_simplex_projection dismir / soft_labels_with_pooling / top156 / psls / linear_simplex_projection dismir|soft_labels_with_pooling||top156|psls|linear_simplex_projection 0.9891630590482507 0.6564182801828252 3 189 96 +dismir hard_labels diagbckg nnls linear_simplex_projection dismir / hard_labels / diagbckg / nnls / linear_simplex_projection dismir|hard_labels||diagbckg|nnls|linear_simplex_projection 0.987257017059692 0.6583739473098351 11 181 96 +methylbert soft_labels_without_pooling top156 swn vector_scaling methylbert / soft_labels_without_pooling / top156 / swn / vector_scaling methylbert|soft_labels_without_pooling||top156|swn|vector_scaling 0.981658490076706 0.6844006665632985 96 96 96 +dismir soft_labels_without_pooling top156 nnls uncalibrated dismir / soft_labels_without_pooling / top156 / nnls / uncalibrated dismir|soft_labels_without_pooling||top156|nnls|uncalibrated 0.9813013973861524 0.6864645017775176 102 90 96 +cancerdetector uniform_prior top156 psls linear_simplex_projection cancerdetector / uniform_prior / top156 / psls / linear_simplex_projection cancerdetector||uniform_prior|top156|psls|linear_simplex_projection 0.9890876453238968 0.6563678551537846 4 190 97 +dismir hard_labels diagbckg swn linear_clip_normalize dismir / hard_labels / diagbckg / swn / linear_clip_normalize dismir|hard_labels||diagbckg|swn|linear_clip_normalize 0.9804430237147964 0.6927832050857812 119 76 98 +dismir hard_labels top156 swn vector_scaling dismir / hard_labels / top156 / swn / vector_scaling dismir|hard_labels||top156|swn|vector_scaling 0.9779629343288112 0.7125569227677906 169 26 98 +Celfie linear_simplex_projection Celfie / linear_simplex_projection celfie|||||linear_simplex_projection 0.9803476202961432 0.6936058209151715 124 73 99 +cancerdetector uniform_prior top156 xgb vector_scaling cancerdetector / uniform_prior / top156 / xgb / vector_scaling cancerdetector||uniform_prior|top156|xgb|vector_scaling 0.9768027073176336 0.7343327997852893 191 7 99 +methylbert soft_labels_without_pooling top156 psls vector_scaling methylbert / soft_labels_without_pooling / top156 / psls / vector_scaling methylbert|soft_labels_without_pooling||top156|psls|vector_scaling 0.9799297714803068 0.6950720073858603 131 69 100 +dismir soft_labels_without_pooling top156 xgb linear_simplex_projection dismir / soft_labels_without_pooling / top156 / xgb / linear_simplex_projection dismir|soft_labels_without_pooling||top156|xgb|linear_simplex_projection 0.9770935328615714 0.7215117715586513 187 14 101 +methylbert soft_labels_with_pooling top156 mlp uncalibrated methylbert / soft_labels_with_pooling / top156 / mlp / uncalibrated methylbert|soft_labels_with_pooling||top156|mlp|uncalibrated 0.982221558205212 0.6770168640297656 88 114 101 +lookup soft_labels_with_pooling top156 nnls linear_simplex_projection lookup / soft_labels_with_pooling / top156 / nnls / linear_simplex_projection lookup|soft_labels_with_pooling||top156|nnls|linear_simplex_projection 0.981216342704616 0.6842811698151092 104 98 101 +cancerdetector train_freq_prior top156 xgb vector_scaling cancerdetector / train_freq_prior / top156 / xgb / vector_scaling cancerdetector||train_freq_prior|top156|xgb|vector_scaling 0.9760427000261344 0.7513263165309906 203 4 104 +cancerdetector train_freq_prior top156 xgb linear_clip_normalize cancerdetector / train_freq_prior / top156 / xgb / linear_clip_normalize cancerdetector||train_freq_prior|top156|xgb|linear_clip_normalize 0.9766338013697982 0.7231077500335187 196 12 104 +cancerdetector uniform_prior top156 swn linear_clip_normalize cancerdetector / uniform_prior / top156 / swn / linear_clip_normalize cancerdetector||uniform_prior|top156|swn|linear_clip_normalize 0.9830785391673836 0.6701383431598213 70 139 105 +methylbert soft_labels_without_pooling top156 nnls vector_scaling methylbert / soft_labels_without_pooling / top156 / nnls / vector_scaling methylbert|soft_labels_without_pooling||top156|nnls|vector_scaling 0.9796782871031138 0.6947570210652172 140 70 105 +dismir soft_labels_with_pooling top156 nnls linear_simplex_projection dismir / soft_labels_with_pooling / top156 / nnls / linear_simplex_projection dismir|soft_labels_with_pooling||top156|nnls|linear_simplex_projection 0.989289450219986 0.6442277368567579 2 209 106 +methylbert soft_labels_without_pooling top156 swn linear_clip_normalize methylbert / soft_labels_without_pooling / top156 / swn / linear_clip_normalize methylbert|soft_labels_without_pooling||top156|swn|linear_clip_normalize 0.9820920925346164 0.6756062720785508 91 120 106 +cancerdetector uniform_prior top156 mlp vector_scaling cancerdetector / uniform_prior / top156 / mlp / vector_scaling cancerdetector||uniform_prior|top156|mlp|vector_scaling 0.9804754700434992 0.6847091454603098 118 94 106 +cancerdetector train_freq_prior top156 psls linear_clip_normalize cancerdetector / train_freq_prior / top156 / psls / linear_clip_normalize cancerdetector||train_freq_prior|top156|psls|linear_clip_normalize 0.9868087447454158 0.6550901208111577 19 194 107 +lookup soft_labels_with_pooling top156 psls vector_scaling lookup / soft_labels_with_pooling / top156 / psls / vector_scaling lookup|soft_labels_with_pooling||top156|psls|vector_scaling 0.9773333061051004 0.7103595079306159 181 33 107 +dismir soft_labels_with_pooling top156 psls linear_clip_normalize dismir / soft_labels_with_pooling / top156 / psls / linear_clip_normalize dismir|soft_labels_with_pooling||top156|psls|linear_clip_normalize 0.9862724891764136 0.6543865521488783 23 195 109 +dismir hard_labels top156 nnls uncalibrated dismir / hard_labels / top156 / nnls / uncalibrated dismir|hard_labels||top156|nnls|uncalibrated 0.9786096154394216 0.6975560488583235 157 61 109 +dismir soft_labels_without_pooling top156 mlp linear_clip_normalize dismir / soft_labels_without_pooling / top156 / mlp / linear_clip_normalize dismir|soft_labels_without_pooling||top156|mlp|linear_clip_normalize 0.9797018215182416 0.6909421196971394 138 81 110 +lookup soft_labels_with_pooling top156 nnls vector_scaling lookup / soft_labels_with_pooling / top156 / nnls / vector_scaling lookup|soft_labels_with_pooling||top156|nnls|vector_scaling 0.9765698612591258 0.7133779644656215 198 21 110 +dismir hard_labels diagbckg swn linear_simplex_projection dismir / hard_labels / diagbckg / swn / linear_simplex_projection dismir|hard_labels||diagbckg|swn|linear_simplex_projection 0.9794732836853818 0.6920570000852393 144 79 112 +cancerdetector train_freq_prior top156 mlp uncalibrated cancerdetector / train_freq_prior / top156 / mlp / uncalibrated cancerdetector||train_freq_prior|top156|mlp|uncalibrated 0.979082433271408 0.6936114898550142 151 72 112 +dismir soft_labels_without_pooling top156 xgb vector_scaling dismir / soft_labels_without_pooling / top156 / xgb / vector_scaling dismir|soft_labels_without_pooling||top156|xgb|vector_scaling 0.9750552420295132 0.7254349749590714 214 9 112 +dismir soft_labels_without_pooling top156 psls uncalibrated dismir / soft_labels_without_pooling / top156 / psls / uncalibrated dismir|soft_labels_without_pooling||top156|psls|uncalibrated 0.9803274484511604 0.6834242317744672 125 99 112 +dismir hard_labels diagbckg swn uncalibrated dismir / hard_labels / diagbckg / swn / uncalibrated dismir|hard_labels||diagbckg|swn|uncalibrated 0.9786256617977156 0.6951156950753312 156 68 112 +dismir hard_labels top156 xgb linear_simplex_projection dismir / hard_labels / top156 / xgb / linear_simplex_projection dismir|hard_labels||top156|xgb|linear_simplex_projection 0.9773103878220638 0.7073160972251501 184 41 113 +dismir hard_labels diagbckg psls linear_clip_normalize dismir / hard_labels / diagbckg / psls / linear_clip_normalize dismir|hard_labels||diagbckg|psls|linear_clip_normalize 0.983764952139134 0.6621274464228923 53 173 113 +dismir hard_labels top156 psls linear_clip_normalize dismir / hard_labels / top156 / psls / linear_clip_normalize dismir|hard_labels||top156|psls|linear_clip_normalize 0.9837599251167772 0.6621166126258149 54 174 114 +cancerdetector uniform_prior top156 psls linear_clip_normalize cancerdetector / uniform_prior / top156 / psls / linear_clip_normalize cancerdetector||uniform_prior|top156|psls|linear_clip_normalize 0.9868623675587782 0.641340431095511 16 215 116 +cancerdetector train_freq_prior top156 nnls linear_clip_normalize cancerdetector / train_freq_prior / top156 / nnls / linear_clip_normalize cancerdetector||train_freq_prior|top156|nnls|linear_clip_normalize 0.984040711745099 0.6571662704308798 48 184 116 +dismir soft_labels_with_pooling top156 nnls linear_clip_normalize dismir / soft_labels_with_pooling / top156 / nnls / linear_clip_normalize dismir|soft_labels_with_pooling||top156|nnls|linear_clip_normalize 0.986384951948504 0.642348810234722 21 213 117 +dismir hard_labels top156 xgb vector_scaling dismir / hard_labels / top156 / xgb / vector_scaling dismir|hard_labels||top156|xgb|vector_scaling 0.9760195854400396 0.7119998123344566 204 30 117 +lookup soft_labels_with_pooling top156 swn vector_scaling lookup / soft_labels_with_pooling / top156 / swn / vector_scaling lookup|soft_labels_with_pooling||top156|swn|vector_scaling 0.9773286987460434 0.7021958488902637 183 52 118 +cancerdetector uniform_prior top156 xgb linear_clip_normalize cancerdetector / uniform_prior / top156 / xgb / linear_clip_normalize cancerdetector||uniform_prior|top156|xgb|linear_clip_normalize 0.9766659997653994 0.7079071674744601 195 40 118 +dismir hard_labels diagbckg nnls linear_clip_normalize dismir / hard_labels / diagbckg / nnls / linear_clip_normalize dismir|hard_labels||diagbckg|nnls|linear_clip_normalize 0.9841770760545284 0.6560648513146908 46 191 119 +dismir canonical_soft_labels diagbckg xgb linear_simplex_projection dismir / canonical_soft_labels / diagbckg / xgb / linear_simplex_projection dismir|canonical_soft_labels||diagbckg|xgb|linear_simplex_projection 0.9775590426722108 0.6958977880897396 174 65 120 +dismir soft_labels_without_pooling top156 xgb linear_clip_normalize dismir / soft_labels_without_pooling / top156 / xgb / linear_clip_normalize dismir|soft_labels_without_pooling||top156|xgb|linear_clip_normalize 0.9739834016169916 0.7212546960796313 225 15 120 +methylbert soft_labels_without_pooling top156 psls linear_clip_normalize methylbert / soft_labels_without_pooling / top156 / psls / linear_clip_normalize methylbert|soft_labels_without_pooling||top156|psls|linear_clip_normalize 0.982559297000932 0.6661310099478934 82 160 121 +dismir hard_labels diagbckg swn vector_scaling dismir / hard_labels / diagbckg / swn / vector_scaling dismir|hard_labels||diagbckg|swn|vector_scaling 0.9778444595289544 0.6943264381855573 172 71 122 +dismir hard_labels diagbckg xgb linear_simplex_projection dismir / hard_labels / diagbckg / xgb / linear_simplex_projection dismir|hard_labels||diagbckg|xgb|linear_simplex_projection 0.9765897981824097 0.7037961046350992 197 47 122 +dismir soft_labels_with_pooling top156 nnls vector_scaling dismir / soft_labels_with_pooling / top156 / nnls / vector_scaling dismir|soft_labels_with_pooling||top156|nnls|vector_scaling 0.9838973132214656 0.6542890149270473 50 196 123 +methylbert canonical_soft_labels top156 psls linear_simplex_projection methylbert / canonical_soft_labels / top156 / psls / linear_simplex_projection methylbert|canonical_soft_labels||top156|psls|linear_simplex_projection 0.9876851201881738 0.6176072220370314 9 238 124 +cancerdetector train_freq_prior top156 psls uncalibrated cancerdetector / train_freq_prior / top156 / psls / uncalibrated cancerdetector||train_freq_prior|top156|psls|uncalibrated 0.9841311782241052 0.6530689025169166 47 200 124 +dismir soft_labels_with_pooling top156 psls vector_scaling dismir / soft_labels_with_pooling / top156 / psls / vector_scaling dismir|soft_labels_with_pooling||top156|psls|vector_scaling 0.9825844964685124 0.6637410467717538 81 169 125 +cancerdetector train_freq_prior top156 swn uncalibrated cancerdetector / train_freq_prior / top156 / swn / uncalibrated cancerdetector||train_freq_prior|top156|swn|uncalibrated 0.9802211467677368 0.6752821747845923 127 123 125 +methylbert soft_labels_with_pooling top156 psls uncalibrated methylbert / soft_labels_with_pooling / top156 / psls / uncalibrated methylbert|soft_labels_with_pooling||top156|psls|uncalibrated 0.9798861183349042 0.6758383419938149 132 118 125 +cancerdetector uniform_prior top156 mlp linear_simplex_projection cancerdetector / uniform_prior / top156 / mlp / linear_simplex_projection cancerdetector||uniform_prior|top156|mlp|linear_simplex_projection 0.9802740854956724 0.6743315963619897 126 126 126 +dismir hard_labels diagbckg xgb vector_scaling dismir / hard_labels / diagbckg / xgb / vector_scaling dismir|hard_labels||diagbckg|xgb|vector_scaling 0.9750802694776136 0.7084710551124028 213 39 126 +cancerdetector train_freq_prior top156 xgb uncalibrated cancerdetector / train_freq_prior / top156 / xgb / uncalibrated cancerdetector||train_freq_prior|top156|xgb|uncalibrated 0.9732355891473864 0.714414956312825 232 20 126 +dismir soft_labels_with_pooling top156 xgb linear_simplex_projection dismir / soft_labels_with_pooling / top156 / xgb / linear_simplex_projection dismir|soft_labels_with_pooling||top156|xgb|linear_simplex_projection 0.9806680452291556 0.6690283422787259 113 145 129 +methylbert canonical_soft_labels top156 nnls linear_simplex_projection methylbert / canonical_soft_labels / top156 / nnls / linear_simplex_projection methylbert|canonical_soft_labels||top156|nnls|linear_simplex_projection 0.9871204340063896 0.6111243094464621 12 247 130 +methylbert soft_labels_with_pooling top156 nnls uncalibrated methylbert / soft_labels_with_pooling / top156 / nnls / uncalibrated methylbert|soft_labels_with_pooling||top156|nnls|uncalibrated 0.9816791395729584 0.6649128896179174 94 166 130 +lookup soft_labels_with_pooling top156 swn linear_simplex_projection lookup / soft_labels_with_pooling / top156 / swn / linear_simplex_projection lookup|soft_labels_with_pooling||top156|swn|linear_simplex_projection 0.9774023998041356 0.6881704904958728 177 85 131 +dismir soft_labels_without_pooling top156 xgb uncalibrated dismir / soft_labels_without_pooling / top156 / xgb / uncalibrated dismir|soft_labels_without_pooling||top156|xgb|uncalibrated 0.9708033620331116 0.720986597006923 247 16 132 +cancerdetector uniform_prior top156 psls uncalibrated cancerdetector / uniform_prior / top156 / psls / uncalibrated cancerdetector||uniform_prior|top156|psls|uncalibrated 0.9843862743774644 0.6363427545801509 43 223 133 +dismir canonical_soft_labels diagbckg xgb vector_scaling dismir / canonical_soft_labels / diagbckg / xgb / vector_scaling dismir|canonical_soft_labels||diagbckg|xgb|vector_scaling 0.9751857294745768 0.7004907465039463 212 55 134 +dismir hard_labels top156 mlp vector_scaling dismir / hard_labels / top156 / mlp / vector_scaling dismir|hard_labels||top156|mlp|vector_scaling 0.978940164418224 0.6767790978037843 154 115 135 +cancerdetector uniform_prior top156 nnls linear_clip_normalize cancerdetector / uniform_prior / top156 / nnls / linear_clip_normalize cancerdetector||uniform_prior|top156|nnls|linear_clip_normalize 0.983154443917095 0.6476315026262078 66 205 136 +methylbert soft_labels_without_pooling top156 swn uncalibrated methylbert / soft_labels_without_pooling / top156 / swn / uncalibrated methylbert|soft_labels_without_pooling||top156|swn|uncalibrated 0.9798672390073936 0.6696223262453173 133 141 137 +methylbert soft_labels_with_pooling top156 xgb vector_scaling methylbert / soft_labels_with_pooling / top156 / xgb / vector_scaling methylbert|soft_labels_with_pooling||top156|xgb|vector_scaling 0.976450915096682 0.6933736096814853 199 75 137 +dismir hard_labels top156 xgb linear_clip_normalize dismir / hard_labels / top156 / xgb / linear_clip_normalize dismir|hard_labels||top156|xgb|linear_clip_normalize 0.973610604084756 0.7068123605188965 231 43 137 +methylbert soft_labels_with_pooling top156 xgb linear_simplex_projection methylbert / soft_labels_with_pooling / top156 / xgb / linear_simplex_projection methylbert|soft_labels_with_pooling||top156|xgb|linear_simplex_projection 0.980944122726126 0.6640366902358195 110 168 139 +dismir soft_labels_without_pooling top156 mlp uncalibrated dismir / soft_labels_without_pooling / top156 / mlp / uncalibrated dismir|soft_labels_without_pooling||top156|mlp|uncalibrated 0.9764086488091552 0.6901217466455418 200 82 141 +dismir canonical_soft_labels top156 xgb linear_simplex_projection dismir / canonical_soft_labels / top156 / xgb / linear_simplex_projection dismir|canonical_soft_labels||top156|xgb|linear_simplex_projection 0.9793386185128596 0.6702325180415744 146 137 142 +lookup soft_labels_without_pooling top156 nnls linear_simplex_projection lookup / soft_labels_without_pooling / top156 / nnls / linear_simplex_projection lookup|soft_labels_without_pooling||top156|nnls|linear_simplex_projection 0.9667271546141532 0.7183075189043423 267 17 142 +dismir hard_labels diagbckg xgb linear_clip_normalize dismir / hard_labels / diagbckg / xgb / linear_clip_normalize dismir|hard_labels||diagbckg|xgb|linear_clip_normalize 0.9723504660456116 0.7033101797908973 238 48 143 +dismir hard_labels top156 mlp linear_simplex_projection dismir / hard_labels / top156 / mlp / linear_simplex_projection dismir|hard_labels||top156|mlp|linear_simplex_projection 0.9785064306073984 0.6714110509871994 159 131 145 +cancerdetector uniform_prior top156 xgb uncalibrated cancerdetector / uniform_prior / top156 / xgb / uncalibrated cancerdetector||uniform_prior|top156|xgb|uncalibrated 0.9731315402732034 0.6990493837423102 233 57 145 +lookup soft_labels_with_pooling top156 mlp vector_scaling lookup / soft_labels_with_pooling / top156 / mlp / vector_scaling lookup|soft_labels_with_pooling||top156|mlp|vector_scaling 0.9754691905199364 0.6915648246992887 211 80 146 +dismir soft_labels_with_pooling top156 psls uncalibrated dismir / soft_labels_with_pooling / top156 / psls / uncalibrated dismir|soft_labels_with_pooling||top156|psls|uncalibrated 0.9813715851970044 0.6557486594359956 100 192 146 +methylbert canonical_soft_labels top156 psls linear_clip_normalize methylbert / canonical_soft_labels / top156 / psls / linear_clip_normalize methylbert|canonical_soft_labels||top156|psls|linear_clip_normalize 0.9849191774188348 0.6049219807491361 37 258 148 +dismir canonical_soft_labels diagbckg xgb linear_clip_normalize dismir / canonical_soft_labels / diagbckg / xgb / linear_clip_normalize dismir|canonical_soft_labels||diagbckg|xgb|linear_clip_normalize 0.9736809700843962 0.6955380210633877 229 66 148 +dismir hard_labels top156 xgb uncalibrated dismir / hard_labels / top156 / xgb / uncalibrated dismir|hard_labels||top156|xgb|uncalibrated 0.9699191916349742 0.706525089059916 252 44 148 +dismir soft_labels_with_pooling top156 nnls uncalibrated dismir / soft_labels_with_pooling / top156 / nnls / uncalibrated dismir|soft_labels_with_pooling||top156|nnls|uncalibrated 0.9822749314087466 0.6438097903366182 87 211 149 +Celfie vector_scaling Celfie / vector_scaling celfie|||||vector_scaling 0.9635550868181862 0.7220204637654729 285 13 149 +methylbert canonical_soft_labels top156 swn vector_scaling methylbert / canonical_soft_labels / top156 / swn / vector_scaling methylbert|canonical_soft_labels||top156|swn|vector_scaling 0.9821579724375664 0.6441188076975395 90 210 150 +methylbert soft_labels_without_pooling top156 nnls linear_clip_normalize methylbert / soft_labels_without_pooling / top156 / nnls / linear_clip_normalize methylbert|soft_labels_without_pooling||top156|nnls|linear_clip_normalize 0.9804408533761014 0.6586523796358668 120 180 150 +methylbert canonical_soft_labels diagbckg nnls linear_simplex_projection methylbert / canonical_soft_labels / diagbckg / nnls / linear_simplex_projection methylbert|canonical_soft_labels||diagbckg|nnls|linear_simplex_projection 0.9868094402971151 0.5505825151259623 18 283 151 +methylbert canonical_soft_labels top156 nnls vector_scaling methylbert / canonical_soft_labels / top156 / nnls / vector_scaling methylbert|canonical_soft_labels||top156|nnls|vector_scaling 0.9830870074970576 0.6264060172383212 69 234 152 +dismir canonical_soft_labels diagbckg psls linear_simplex_projection dismir / canonical_soft_labels / diagbckg / psls / linear_simplex_projection dismir|canonical_soft_labels||diagbckg|psls|linear_simplex_projection 0.9781010086608808 0.670151564523591 165 138 152 +cancerdetector uniform_prior top156 swn uncalibrated cancerdetector / uniform_prior / top156 / swn / uncalibrated cancerdetector||uniform_prior|top156|swn|uncalibrated 0.9805007391709556 0.6567059254658996 117 187 152 +lookup soft_labels_without_pooling top156 nnls vector_scaling lookup / soft_labels_without_pooling / top156 / nnls / vector_scaling lookup|soft_labels_without_pooling||top156|nnls|vector_scaling 0.9545518529588478 0.7727869481312041 303 1 152 +dismir soft_labels_with_pooling top156 xgb vector_scaling dismir / soft_labels_with_pooling / top156 / xgb / vector_scaling dismir|soft_labels_with_pooling||top156|xgb|vector_scaling 0.9773790615357516 0.6736093333078064 180 127 154 +methylbert canonical_soft_labels top156 psls vector_scaling methylbert / canonical_soft_labels / top156 / psls / vector_scaling methylbert|canonical_soft_labels||top156|psls|vector_scaling 0.982844896606184 0.6292697477008016 78 230 154 +dismir hard_labels diagbckg xgb uncalibrated dismir / hard_labels / diagbckg / xgb / uncalibrated dismir|hard_labels||diagbckg|xgb|uncalibrated 0.9682010458059002 0.7029891907313631 258 50 154 +cancerdetector uniform_prior top156 mlp linear_clip_normalize cancerdetector / uniform_prior / top156 / mlp / linear_clip_normalize cancerdetector||uniform_prior|top156|mlp|linear_clip_normalize 0.979263830724825 0.6651766755448876 147 163 155 +dismir hard_labels top156 mlp linear_clip_normalize dismir / hard_labels / top156 / mlp / linear_clip_normalize dismir|hard_labels||top156|mlp|linear_clip_normalize 0.977398819429422 0.6713344592436614 178 132 155 +lookup soft_labels_without_pooling top156 swn vector_scaling lookup / soft_labels_without_pooling / top156 / swn / vector_scaling lookup|soft_labels_without_pooling||top156|swn|vector_scaling 0.951283992382522 0.7495925365242709 306 5 156 +lookup soft_labels_without_pooling top156 psls vector_scaling lookup / soft_labels_without_pooling / top156 / psls / vector_scaling lookup|soft_labels_without_pooling||top156|psls|vector_scaling 0.9491287089053384 0.7638584499115328 309 2 156 +dismir canonical_soft_labels top156 mlp linear_simplex_projection dismir / canonical_soft_labels / top156 / mlp / linear_simplex_projection dismir|canonical_soft_labels||top156|mlp|linear_simplex_projection 0.9794664069208232 0.663546415425269 145 170 158 +dismir canonical_soft_labels top156 nnls linear_simplex_projection dismir / canonical_soft_labels / top156 / nnls / linear_simplex_projection dismir|canonical_soft_labels||top156|nnls|linear_simplex_projection 0.9761271359807264 0.6774909980459737 202 113 158 +dismir canonical_soft_labels top156 xgb vector_scaling dismir / canonical_soft_labels / top156 / xgb / vector_scaling dismir|canonical_soft_labels||top156|xgb|vector_scaling 0.9767653328172508 0.6749427207783316 192 125 159 +cancerdetector train_freq_prior top156 nnls uncalibrated cancerdetector / train_freq_prior / top156 / nnls / uncalibrated cancerdetector||train_freq_prior|top156|nnls|uncalibrated 0.9806589471247688 0.6482791729690589 114 204 159 +dismir canonical_soft_labels diagbckg nnls linear_simplex_projection dismir / canonical_soft_labels / diagbckg / nnls / linear_simplex_projection dismir|canonical_soft_labels||diagbckg|nnls|linear_simplex_projection 0.9797941366257849 0.657645863898668 136 182 159 +methylbert canonical_soft_labels top156 nnls linear_clip_normalize methylbert / canonical_soft_labels / top156 / nnls / linear_clip_normalize methylbert|canonical_soft_labels||top156|nnls|linear_clip_normalize 0.9837247156394178 0.5934525774323025 55 265 160 +methylbert canonical_soft_labels diagbckg psls linear_simplex_projection methylbert / canonical_soft_labels / diagbckg / psls / linear_simplex_projection methylbert|canonical_soft_labels||diagbckg|psls|linear_simplex_projection 0.986601024308086 0.5111037646731131 20 301 161 +dismir canonical_soft_labels diagbckg xgb uncalibrated dismir / canonical_soft_labels / diagbckg / xgb / uncalibrated dismir|canonical_soft_labels||diagbckg|xgb|uncalibrated 0.9694516044598344 0.6951962165789081 254 67 161 +methylbert hard_labels top156 psls linear_simplex_projection methylbert / hard_labels / top156 / psls / linear_simplex_projection methylbert|hard_labels||top156|psls|linear_simplex_projection 0.985412564321376 0.5257553671659034 30 292 161 +dismir canonical_soft_labels top156 nnls vector_scaling dismir / canonical_soft_labels / top156 / nnls / vector_scaling dismir|canonical_soft_labels||top156|nnls|vector_scaling 0.9661020828716184 0.7026188192321847 272 51 162 +dismir soft_labels_with_pooling top156 xgb linear_clip_normalize dismir / soft_labels_with_pooling / top156 / xgb / linear_clip_normalize dismir|soft_labels_with_pooling||top156|xgb|linear_clip_normalize 0.9774028420149322 0.6688940779418842 176 148 162 +methylbert canonical_soft_labels top156 swn linear_simplex_projection methylbert / canonical_soft_labels / top156 / swn / linear_simplex_projection methylbert|canonical_soft_labels||top156|swn|linear_simplex_projection 0.9809928321035246 0.6404301931405051 109 216 163 +lookup soft_labels_without_pooling top156 mlp vector_scaling lookup / soft_labels_without_pooling / top156 / mlp / vector_scaling lookup|soft_labels_without_pooling||top156|mlp|vector_scaling 0.9271902958941048 0.7544008261887544 322 2 163 +methylbert canonical_soft_labels top156 swn linear_clip_normalize methylbert / canonical_soft_labels / top156 / swn / linear_clip_normalize methylbert|canonical_soft_labels||top156|swn|linear_clip_normalize 0.9813723366118896 0.6314337684303575 99 228 164 +dismir hard_labels diagbckg nnls vector_scaling dismir / hard_labels / diagbckg / nnls / vector_scaling dismir|hard_labels||diagbckg|nnls|vector_scaling 0.979627066779678 0.6565459803009593 142 188 165 +methylbert canonical_soft_labels diagbckg nnls linear_clip_normalize methylbert / canonical_soft_labels / diagbckg / nnls / linear_clip_normalize methylbert|canonical_soft_labels||diagbckg|nnls|linear_clip_normalize 0.984223076380641 0.5434255559166933 44 287 166 +dismir soft_labels_with_pooling top156 mlp vector_scaling dismir / soft_labels_with_pooling / top156 / mlp / vector_scaling dismir|soft_labels_with_pooling||top156|mlp|vector_scaling 0.9822765508417632 0.6124656614938736 86 245 166 +methylbert hard_labels top156 nnls linear_simplex_projection methylbert / hard_labels / top156 / nnls / linear_simplex_projection methylbert|hard_labels||top156|nnls|linear_simplex_projection 0.984739195046728 0.5233788471790006 39 293 166 +lookup soft_labels_with_pooling top156 swn linear_clip_normalize lookup / soft_labels_with_pooling / top156 / swn / linear_clip_normalize lookup|soft_labels_with_pooling||top156|swn|linear_clip_normalize 0.9743648331089833 0.6775814822309097 220 112 166 +lookup soft_labels_without_pooling top156 psls linear_simplex_projection lookup / soft_labels_without_pooling / top156 / psls / linear_simplex_projection lookup|soft_labels_without_pooling||top156|psls|linear_simplex_projection 0.9656479576434596 0.6990788204177236 276 56 166 +methylbert canonical_soft_labels diagbckg swn vector_scaling methylbert / canonical_soft_labels / diagbckg / swn / vector_scaling methylbert|canonical_soft_labels||diagbckg|swn|vector_scaling 0.982899943244847 0.6013484554524894 73 261 167 +dismir canonical_soft_labels top156 mlp vector_scaling dismir / canonical_soft_labels / top156 / mlp / vector_scaling dismir|canonical_soft_labels||top156|mlp|vector_scaling 0.9773795776439388 0.6673286115065853 179 158 169 +dismir canonical_soft_labels top156 mlp linear_clip_normalize dismir / canonical_soft_labels / top156 / mlp / linear_clip_normalize dismir|canonical_soft_labels||top156|mlp|linear_clip_normalize 0.9779678663224952 0.6629403185271456 168 171 170 +methylbert soft_labels_without_pooling top156 mlp linear_simplex_projection methylbert / soft_labels_without_pooling / top156 / mlp / linear_simplex_projection methylbert|soft_labels_without_pooling||top156|mlp|linear_simplex_projection 0.9785283924415028 0.6572977347851529 158 183 171 +methylbert hard_labels diagbckg nnls linear_simplex_projection methylbert / hard_labels / diagbckg / nnls / linear_simplex_projection methylbert|hard_labels||diagbckg|nnls|linear_simplex_projection 0.985728591092212 0.4592436299080625 28 314 171 +methylbert canonical_soft_labels diagbckg nnls vector_scaling methylbert / canonical_soft_labels / diagbckg / nnls / vector_scaling methylbert|canonical_soft_labels||diagbckg|nnls|vector_scaling 0.983170357082346 0.5650872106731323 65 279 172 +lookup soft_labels_without_pooling top156 swn linear_simplex_projection lookup / soft_labels_without_pooling / top156 / swn / linear_simplex_projection lookup|soft_labels_without_pooling||top156|swn|linear_simplex_projection 0.9508201515085526 0.7096787567336592 308 37 173 +methylbert canonical_soft_labels diagbckg swn linear_simplex_projection methylbert / canonical_soft_labels / diagbckg / swn / linear_simplex_projection methylbert|canonical_soft_labels||diagbckg|swn|linear_simplex_projection 0.9825341960408712 0.5990197952463866 83 263 173 +dismir canonical_soft_labels top156 xgb linear_clip_normalize dismir / canonical_soft_labels / top156 / xgb / linear_clip_normalize dismir|canonical_soft_labels||top156|xgb|linear_clip_normalize 0.9757295182000468 0.6699139637102154 206 140 173 +dismir hard_labels diagbckg psls uncalibrated dismir / hard_labels / diagbckg / psls / uncalibrated dismir|hard_labels||diagbckg|psls|uncalibrated 0.976732527241497 0.667468835152242 193 156 175 +dismir hard_labels top156 psls uncalibrated dismir / hard_labels / top156 / psls / uncalibrated dismir|hard_labels||top156|psls|uncalibrated 0.9767228916465478 0.6674439607398395 194 157 176 +methylbert canonical_soft_labels diagbckg psls linear_clip_normalize methylbert / canonical_soft_labels / diagbckg / psls / linear_clip_normalize methylbert|canonical_soft_labels||diagbckg|psls|linear_clip_normalize 0.9839653316557032 0.5024058924333387 49 304 177 +methylbert hard_labels diagbckg psls linear_simplex_projection methylbert / hard_labels / diagbckg / psls / linear_simplex_projection methylbert|hard_labels||diagbckg|psls|linear_simplex_projection 0.985401259527594 0.4279310086272839 31 323 177 +lookup soft_labels_with_pooling top156 mlp linear_simplex_projection lookup / soft_labels_with_pooling / top156 / mlp / linear_simplex_projection lookup|soft_labels_with_pooling||top156|mlp|linear_simplex_projection 0.9713668429274016 0.6785901843454469 243 111 177 +dismir canonical_soft_labels top156 nnls linear_clip_normalize dismir / canonical_soft_labels / top156 / nnls / linear_clip_normalize dismir|canonical_soft_labels||top156|nnls|linear_clip_normalize 0.9722536600317846 0.6760357087407792 239 116 178 +methylbert canonical_soft_labels diagbckg swn linear_clip_normalize methylbert / canonical_soft_labels / diagbckg / swn / linear_clip_normalize methylbert|canonical_soft_labels||diagbckg|swn|linear_clip_normalize 0.9821877221366552 0.5892438258350485 89 267 178 +cancerdetector uniform_prior top156 nnls uncalibrated 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linear_clip_normalize methylbert / hard_labels / top156 / psls / linear_clip_normalize methylbert|hard_labels||top156|psls|linear_clip_normalize 0.9828831868677947 0.5216948380434975 74 296 185 +dismir hard_labels diagbckg psls vector_scaling dismir / hard_labels / diagbckg / psls / vector_scaling dismir|hard_labels||diagbckg|psls|vector_scaling 0.9770632688161672 0.6570978971863266 188 185 187 +dismir canonical_soft_labels diagbckg psls linear_clip_normalize dismir / canonical_soft_labels / diagbckg / psls / linear_clip_normalize dismir|canonical_soft_labels||diagbckg|psls|linear_clip_normalize 0.974443643348332 0.6677448645701006 218 155 187 +lookup soft_labels_without_pooling top156 xgb vector_scaling lookup / soft_labels_without_pooling / top156 / xgb / vector_scaling lookup|soft_labels_without_pooling||top156|xgb|vector_scaling 0.8814512980163727 0.7128504755950935 350 24 187 +dismir hard_labels top156 psls vector_scaling dismir / hard_labels / top156 / psls / vector_scaling dismir|hard_labels||top156|psls|vector_scaling 0.9769892011319914 0.6569162255080523 189 186 188 +dismir soft_labels_with_pooling top156 mlp linear_simplex_projection dismir / soft_labels_with_pooling / top156 / mlp / linear_simplex_projection dismir|soft_labels_with_pooling||top156|mlp|linear_simplex_projection 0.9800247876688608 0.60825405747595 128 248 188 +methylbert canonical_soft_labels top156 psls uncalibrated methylbert / canonical_soft_labels / top156 / psls / uncalibrated methylbert|canonical_soft_labels||top156|psls|uncalibrated 0.9806352449425548 0.6001455852152049 115 262 189 +methylbert hard_labels diagbckg nnls linear_clip_normalize methylbert / hard_labels / diagbckg / nnls / linear_clip_normalize methylbert|hard_labels||diagbckg|nnls|linear_clip_normalize 0.9832349444923149 0.4575889162012269 63 315 189 +dismir canonical_soft_labels top156 psls linear_simplex_projection dismir / canonical_soft_labels / top156 / psls / linear_simplex_projection 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0.6628058993025691 215 172 194 +dismir hard_labels diagbckg mlp linear_clip_normalize dismir / hard_labels / diagbckg / mlp / linear_clip_normalize dismir|hard_labels||diagbckg|mlp|linear_clip_normalize 0.9720908851258636 0.6689439615491678 240 147 194 +methylbert canonical_soft_labels top156 swn uncalibrated methylbert / canonical_soft_labels / top156 / swn / uncalibrated methylbert|canonical_soft_labels||top156|swn|uncalibrated 0.979014247182445 0.6230758030459741 153 236 195 +methylbert canonical_soft_labels diagbckg nnls uncalibrated methylbert / canonical_soft_labels / diagbckg / nnls / uncalibrated methylbert|canonical_soft_labels||diagbckg|nnls|uncalibrated 0.981195459624431 0.5434517364670225 105 286 196 +dismir canonical_soft_labels diagbckg nnls linear_clip_normalize dismir / canonical_soft_labels / diagbckg / nnls / linear_clip_normalize dismir|canonical_soft_labels||diagbckg|nnls|linear_clip_normalize 0.9761327962979498 0.6557354838222149 201 193 197 +methylbert soft_labels_with_pooling top156 xgb linear_clip_normalize methylbert / soft_labels_with_pooling / top156 / xgb / linear_clip_normalize methylbert|soft_labels_with_pooling||top156|xgb|linear_clip_normalize 0.9769699674563964 0.646487030093476 190 206 198 +dismir hard_labels diagbckg mlp linear_simplex_projection dismir / hard_labels / diagbckg / mlp / linear_simplex_projection dismir|hard_labels||diagbckg|mlp|linear_simplex_projection 0.971242492662195 0.6685556958268493 245 151 198 +dismir canonical_soft_labels top156 nnls uncalibrated dismir / canonical_soft_labels / top156 / nnls / uncalibrated dismir|canonical_soft_labels||top156|nnls|uncalibrated 0.9611461572441738 0.6794555952002354 289 107 198 +methylbert hard_labels top156 nnls linear_clip_normalize methylbert / hard_labels / top156 / nnls / linear_clip_normalize methylbert|hard_labels||top156|nnls|linear_clip_normalize 0.981432312658505 0.5185995964334162 98 299 199 +methylbert hard_labels top156 nnls vector_scaling methylbert / hard_labels / top156 / nnls / vector_scaling methylbert|hard_labels||top156|nnls|vector_scaling 0.980768209228968 0.5395534462191175 111 288 200 +methylbert hard_labels diagbckg psls linear_clip_normalize methylbert / hard_labels / diagbckg / psls / linear_clip_normalize methylbert|hard_labels||diagbckg|psls|linear_clip_normalize 0.9828712979025656 0.4251520464182817 77 325 201 +lookup soft_labels_with_pooling top156 psls linear_clip_normalize lookup / soft_labels_with_pooling / top156 / psls / linear_clip_normalize lookup|soft_labels_with_pooling||top156|psls|linear_clip_normalize 0.975553226802052 0.6539223611261666 208 197 203 +EpiDISH vector_scaling EpiDISH / vector_scaling epidish|||||vector_scaling 0.9595093966944118 0.6787757635377812 296 110 203 +cancerdetector uniform_prior top156 mlp uncalibrated cancerdetector / uniform_prior / top156 / mlp / uncalibrated cancerdetector||uniform_prior|top156|mlp|uncalibrated 0.9757281567572678 0.6515685811640629 207 202 205 +methylbert soft_labels_without_pooling top156 nnls uncalibrated methylbert / soft_labels_without_pooling / top156 / nnls / uncalibrated methylbert|soft_labels_without_pooling||top156|nnls|uncalibrated 0.9755052524237084 0.6533479755811302 210 199 205 +methylbert hard_labels diagbckg nnls vector_scaling methylbert / hard_labels / diagbckg / nnls / vector_scaling methylbert|hard_labels||diagbckg|nnls|vector_scaling 0.9813510293338744 0.4742650457173906 101 310 206 +methylbert canonical_soft_labels diagbckg psls uncalibrated methylbert / canonical_soft_labels / diagbckg / psls / uncalibrated methylbert|canonical_soft_labels||diagbckg|psls|uncalibrated 0.9810269133658952 0.5037049549876972 108 303 206 +methylbert canonical_soft_labels diagbckg swn uncalibrated methylbert / canonical_soft_labels / diagbckg / swn / uncalibrated methylbert|canonical_soft_labels||diagbckg|swn|uncalibrated 0.9796914407732816 0.5810707817071048 139 272 206 +dismir canonical_soft_labels diagbckg psls uncalibrated dismir / canonical_soft_labels / diagbckg / psls / uncalibrated dismir|canonical_soft_labels||diagbckg|psls|uncalibrated 0.9647175278403984 0.6728243890480486 282 129 206 +dismir soft_labels_with_pooling top156 mlp linear_clip_normalize dismir / soft_labels_with_pooling / top156 / mlp / linear_clip_normalize dismir|soft_labels_with_pooling||top156|mlp|linear_clip_normalize 0.9784655078595684 0.6072577966806741 160 252 206 +dismir canonical_soft_labels diagbckg psls vector_scaling dismir / canonical_soft_labels / diagbckg / psls / vector_scaling dismir|canonical_soft_labels||diagbckg|psls|vector_scaling 0.9679460449751122 0.6680234412718142 259 153 206 +dismir canonical_soft_labels top156 psls linear_clip_normalize dismir / canonical_soft_labels / top156 / psls / linear_clip_normalize dismir|canonical_soft_labels||top156|psls|linear_clip_normalize 0.9675409366406093 0.6685171353949805 260 152 206 +methylbert hard_labels top156 psls vector_scaling methylbert / hard_labels / top156 / psls / 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0.9671549906004054 0.6677591133852269 264 154 209 +methylbert canonical_soft_labels top156 nnls uncalibrated methylbert / canonical_soft_labels / top156 / nnls / uncalibrated methylbert|canonical_soft_labels||top156|nnls|uncalibrated 0.9791353707027284 0.5874592244175505 150 269 210 +methylbert canonical_soft_labels top156 mlp vector_scaling methylbert / canonical_soft_labels / top156 / mlp / vector_scaling methylbert|canonical_soft_labels||top156|mlp|vector_scaling 0.9790684039613128 0.5831054586953605 152 270 211 +lookup soft_labels_with_pooling top156 mlp linear_clip_normalize lookup / soft_labels_with_pooling / top156 / mlp / linear_clip_normalize lookup|soft_labels_with_pooling||top156|mlp|linear_clip_normalize 0.9672530760746983 0.6671861551484414 263 159 211 +dismir canonical_soft_labels top156 psls uncalibrated dismir / canonical_soft_labels / top156 / psls / uncalibrated dismir|canonical_soft_labels||top156|psls|uncalibrated 0.9592279961845678 0.6750678561175077 298 124 211 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216 +lookup soft_labels_without_pooling top156 swn uncalibrated lookup / soft_labels_without_pooling / top156 / swn / uncalibrated lookup|soft_labels_without_pooling||top156|swn|uncalibrated 0.8862004219499117 0.6879874070848191 347 86 217 +dismir canonical_soft_labels top156 psls vector_scaling dismir / canonical_soft_labels / top156 / psls / vector_scaling dismir|canonical_soft_labels||top156|psls|vector_scaling 0.9615900221179586 0.6689700435040191 288 146 217 +methylbert hard_labels diagbckg nnls uncalibrated methylbert / hard_labels / diagbckg / nnls / uncalibrated methylbert|hard_labels||diagbckg|nnls|uncalibrated 0.9805955797144666 0.4473814286001095 116 319 218 +methylbert canonical_soft_labels top156 mlp linear_clip_normalize methylbert / canonical_soft_labels / top156 / mlp / linear_clip_normalize methylbert|canonical_soft_labels||top156|mlp|linear_clip_normalize 0.9784509427111636 0.578606749567086 161 274 218 +methylbert canonical_soft_labels diagbckg mlp vector_scaling methylbert / canonical_soft_labels / diagbckg / mlp / vector_scaling methylbert|canonical_soft_labels||diagbckg|mlp|vector_scaling 0.9781435775883812 0.5796028456518442 164 273 219 +methylbert hard_labels diagbckg psls vector_scaling methylbert / hard_labels / diagbckg / psls / vector_scaling methylbert|hard_labels||diagbckg|psls|vector_scaling 0.9804015785645184 0.4497016951264497 121 317 219 +methylbert hard_labels top156 swn vector_scaling methylbert / hard_labels / top156 / swn / vector_scaling methylbert|hard_labels||top156|swn|vector_scaling 0.979478004828608 0.5203052691293485 143 298 221 +methylbert soft_labels_without_pooling top156 mlp uncalibrated methylbert / soft_labels_without_pooling / top156 / mlp / uncalibrated methylbert|soft_labels_without_pooling||top156|mlp|uncalibrated 0.9742804344391224 0.6393882987948545 222 219 221 +methylbert canonical_soft_labels diagbckg mlp linear_clip_normalize methylbert / canonical_soft_labels / diagbckg / mlp / linear_clip_normalize 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/ top156 / swn / linear_simplex_projection methylbert|hard_labels||top156|swn|linear_simplex_projection 0.9778862644352828 0.5011529936559359 171 306 239 +lookup soft_labels_without_pooling top156 psls linear_clip_normalize lookup / soft_labels_without_pooling / top156 / psls / linear_clip_normalize lookup|soft_labels_without_pooling||top156|psls|linear_clip_normalize 0.938128688308277 0.6658477536026853 316 162 239 +methylbert hard_labels diagbckg swn linear_simplex_projection methylbert / hard_labels / diagbckg / swn / linear_simplex_projection methylbert|hard_labels||diagbckg|swn|linear_simplex_projection 0.977981732422728 0.4613700785677471 167 313 240 +dismir soft_labels_with_pooling top156 mlp uncalibrated dismir / soft_labels_with_pooling / top156 / mlp / uncalibrated dismir|soft_labels_with_pooling||top156|mlp|uncalibrated 0.9738926637300732 0.6070905904459072 227 253 240 +methylbert canonical_soft_labels top156 mlp uncalibrated methylbert / canonical_soft_labels / top156 / mlp / uncalibrated methylbert|canonical_soft_labels||top156|mlp|uncalibrated 0.9758809406592552 0.5705586317752142 205 276 241 +methylbert hard_labels diagbckg swn linear_clip_normalize methylbert / hard_labels / diagbckg / swn / linear_clip_normalize methylbert|hard_labels||diagbckg|swn|linear_clip_normalize 0.9779296483279448 0.4621324812562306 170 312 241 +lookup soft_labels_with_pooling top156 psls uncalibrated lookup / soft_labels_with_pooling / top156 / psls / uncalibrated lookup|soft_labels_with_pooling||top156|psls|uncalibrated 0.9665390741541592 0.6381380629336383 269 220 245 +UXM U25 linear_simplex_projection UXM U25 / linear_simplex_projection uxm|||||linear_simplex_projection 0.967366249650194 0.6265511034927271 262 233 248 +Houseman_ineq linear_simplex_projection Houseman_ineq / linear_simplex_projection epidish_houseman|||||linear_simplex_projection 0.9651151181673008 0.6399096425435296 279 217 248 +UXM U25 vector_scaling UXM U25 / vector_scaling uxm|||||vector_scaling 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lookup / soft_labels_with_pooling / top156 / xgb / vector_scaling lookup|soft_labels_with_pooling||top156|xgb|vector_scaling 0.932182422522156 0.6433044434873 320 212 266 +methylbert hard_labels top156 mlp linear_clip_normalize methylbert / hard_labels / top156 / mlp / linear_clip_normalize methylbert|hard_labels||top156|mlp|linear_clip_normalize 0.9699546224660262 0.546093409721603 251 284 268 +methylbert hard_labels diagbckg mlp linear_clip_normalize methylbert / hard_labels / diagbckg / mlp / linear_clip_normalize methylbert|hard_labels||diagbckg|mlp|linear_clip_normalize 0.9743337537554744 0.450403168080724 221 316 269 +methylbert hard_labels diagbckg mlp vector_scaling methylbert / hard_labels / diagbckg / mlp / vector_scaling methylbert|hard_labels||diagbckg|mlp|vector_scaling 0.9739292093097068 0.4652141601552911 226 311 269 +Houseman_ineq linear_clip_normalize Houseman_ineq / linear_clip_normalize epidish_houseman|||||linear_clip_normalize 0.9417614896048736 0.6321711977115294 313 226 270 +dismir canonical_soft_labels top156 swn linear_simplex_projection dismir / canonical_soft_labels / top156 / swn / linear_simplex_projection dismir|canonical_soft_labels||top156|swn|linear_simplex_projection 0.9640203230325212 0.6054279514695621 283 257 270 +methylbert hard_labels diagbckg swn uncalibrated methylbert / hard_labels / diagbckg / swn / uncalibrated methylbert|hard_labels||diagbckg|swn|uncalibrated 0.974431385821913 0.4449634686013121 219 322 271 +UXM U25 linear_clip_normalize UXM U25 / linear_clip_normalize uxm|||||linear_clip_normalize 0.9590530619146902 0.6116008213750364 299 246 273 +lookup soft_labels_with_pooling top156 xgb linear_simplex_projection lookup / soft_labels_with_pooling / top156 / xgb / linear_simplex_projection lookup|soft_labels_with_pooling||top156|xgb|linear_simplex_projection 0.9331307497608928 0.6314427305638436 319 227 273 +methylbert hard_labels diagbckg mlp linear_simplex_projection methylbert / hard_labels / diagbckg / mlp / linear_simplex_projection methylbert|hard_labels||diagbckg|mlp|linear_simplex_projection 0.9736184238960672 0.4488562869686171 230 318 274 +dismir canonical_soft_labels top156 swn vector_scaling dismir / canonical_soft_labels / top156 / swn / vector_scaling dismir|canonical_soft_labels||top156|swn|vector_scaling 0.957874685396496 0.6074393681117423 301 251 276 +methylbert soft_labels_without_pooling top156 xgb uncalibrated methylbert / soft_labels_without_pooling / top156 / xgb / uncalibrated methylbert|soft_labels_without_pooling||top156|xgb|uncalibrated 0.9597957324967314 0.6018319412308407 295 260 278 +lookup soft_labels_without_pooling top156 xgb linear_simplex_projection lookup / soft_labels_without_pooling / top156 / xgb / linear_simplex_projection lookup|soft_labels_without_pooling||top156|xgb|linear_simplex_projection 0.8796663231699264 0.6484911315851813 352 203 278 +methylbert hard_labels top156 mlp uncalibrated methylbert / hard_labels / top156 / mlp / uncalibrated methylbert|hard_labels||top156|mlp|uncalibrated 0.9668630626011409 0.5308943481890962 266 290 278 +lookup soft_labels_with_pooling top156 xgb linear_clip_normalize lookup / soft_labels_with_pooling / top156 / xgb / linear_clip_normalize lookup|soft_labels_with_pooling||top156|xgb|linear_clip_normalize 0.9246752057855526 0.6157992669133736 323 239 281 +Houseman_ineq uncalibrated Houseman_ineq / uncalibrated epidish_houseman|||||uncalibrated 0.9094137280725918 0.6304023096670268 335 229 282 +methylbert canonical_soft_labels top156 xgb linear_clip_normalize methylbert / canonical_soft_labels / top156 / xgb / linear_clip_normalize methylbert|canonical_soft_labels||top156|xgb|linear_clip_normalize 0.9637393676071628 0.5516187318892062 284 282 283 +UXM U25 uncalibrated UXM U25 / uncalibrated uxm|||||uncalibrated 0.9485689943195332 0.6027893917256765 310 259 285 +lookup soft_labels_with_pooling top156 xgb uncalibrated lookup / soft_labels_with_pooling / top156 / xgb / uncalibrated lookup|soft_labels_with_pooling||top156|xgb|uncalibrated 0.9065176417458268 0.6271225268187544 338 231 285 +EpiDISH linear_clip_normalize EpiDISH / linear_clip_normalize epidish|||||linear_clip_normalize 0.9352763357183804 0.6067134594072809 317 254 286 +methylbert hard_labels diagbckg xgb vector_scaling methylbert / hard_labels / diagbckg / xgb / vector_scaling methylbert|hard_labels||diagbckg|xgb|vector_scaling 0.9712825750616068 0.4101924907702685 244 329 287 +lookup soft_labels_without_pooling top156 nnls uncalibrated lookup / soft_labels_without_pooling / top156 / nnls / uncalibrated lookup|soft_labels_without_pooling||top156|nnls|uncalibrated 0.8750529039775802 0.6372055863646 354 222 288 +methylbert hard_labels diagbckg mlp uncalibrated methylbert / hard_labels / diagbckg / mlp / uncalibrated methylbert|hard_labels||diagbckg|mlp|uncalibrated 0.968574464223038 0.4277754971693899 256 324 290 +methylbert canonical_soft_labels diagbckg xgb vector_scaling methylbert / 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methylbert / hard_labels / diagbckg / xgb / uncalibrated methylbert|hard_labels||diagbckg|xgb|uncalibrated 0.9526444092266148 0.3693956272872996 305 344 325 +lookup hard_labels diagbckg xgb vector_scaling lookup / hard_labels / diagbckg / xgb / vector_scaling lookup|hard_labels||diagbckg|xgb|vector_scaling 0.8925124059755473 0.4469939461691248 343 321 332 +lookup hard_labels top156 xgb vector_scaling lookup / hard_labels / top156 / xgb / vector_scaling lookup|hard_labels||top156|xgb|vector_scaling 0.8925124059755473 0.4469939461691248 343 321 332 +methylbert hard_labels top156 xgb linear_clip_normalize methylbert / hard_labels / top156 / xgb / linear_clip_normalize methylbert|hard_labels||top156|xgb|linear_clip_normalize 0.959451287891978 0.2595239876586996 297 367 332 +lookup hard_labels top156 nnls vector_scaling lookup / hard_labels / top156 / nnls / vector_scaling lookup|hard_labels||top156|nnls|vector_scaling 0.909666141585115 0.3701972573215738 332 343 337 +methylbert hard_labels top156 xgb uncalibrated methylbert / hard_labels / top156 / xgb / uncalibrated methylbert|hard_labels||top156|xgb|uncalibrated 0.9509967595028496 0.2564788166288729 307 368 338 +lookup hard_labels diagbckg nnls vector_scaling lookup / hard_labels / diagbckg / nnls / vector_scaling lookup|hard_labels||diagbckg|nnls|vector_scaling 0.9096661415851148 0.3701972573215738 333 343 338 +lookup hard_labels diagbckg psls vector_scaling lookup / hard_labels / diagbckg / psls / vector_scaling lookup|hard_labels||diagbckg|psls|vector_scaling 0.8802284387166424 0.4208791496810208 351 327 339 +lookup hard_labels top156 xgb linear_simplex_projection lookup / hard_labels / top156 / xgb / linear_simplex_projection lookup|hard_labels||top156|xgb|linear_simplex_projection 0.8846094574455851 0.4056290229272045 349 331 340 +lookup hard_labels diagbckg xgb linear_simplex_projection lookup / hard_labels / diagbckg / xgb / linear_simplex_projection lookup|hard_labels||diagbckg|xgb|linear_simplex_projection 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linear_simplex_projection lookup / hard_labels / top156 / psls / linear_simplex_projection lookup|hard_labels||top156|psls|linear_simplex_projection 0.9140081859475322 0.3297150848849868 326 357 341 +lookup hard_labels diagbckg psls linear_simplex_projection lookup / hard_labels / diagbckg / psls / linear_simplex_projection lookup|hard_labels||diagbckg|psls|linear_simplex_projection 0.9140081760528534 0.3297150848849868 327 357 342 +lookup hard_labels top156 swn linear_simplex_projection lookup / hard_labels / top156 / swn / linear_simplex_projection lookup|hard_labels||top156|swn|linear_simplex_projection 0.91556275921001 0.309568328492628 324 363 344 +lookup hard_labels diagbckg xgb linear_clip_normalize lookup / hard_labels / diagbckg / xgb / linear_clip_normalize lookup|hard_labels||diagbckg|xgb|linear_clip_normalize 0.8676419140099911 0.4025270496942212 357 333 345 +lookup hard_labels top156 xgb linear_clip_normalize lookup / hard_labels / top156 / xgb / linear_clip_normalize 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hard_labels top156 nnls uncalibrated lookup / hard_labels / top156 / nnls / uncalibrated lookup|hard_labels||top156|nnls|uncalibrated 0.8351667246470189 0.3425440425382599 365 348 356 +lookup hard_labels top156 psls uncalibrated lookup / hard_labels / top156 / psls / uncalibrated lookup|hard_labels||top156|psls|uncalibrated 0.8354586056390909 0.3394624136771186 363 350 356 +lookup hard_labels diagbckg mlp vector_scaling lookup / hard_labels / diagbckg / mlp / vector_scaling lookup|hard_labels||diagbckg|mlp|vector_scaling 0.8064526529026768 0.2748680257208303 373 365 369 +lookup hard_labels top156 mlp linear_simplex_projection lookup / hard_labels / top156 / mlp / linear_simplex_projection lookup|hard_labels||top156|mlp|linear_simplex_projection 0.8266039494927329 0.1510911369781309 368 372 370 +lookup hard_labels top156 mlp vector_scaling lookup / hard_labels / top156 / mlp / vector_scaling lookup|hard_labels||top156|mlp|vector_scaling 0.8226510086301468 0.2007346507913184 370 370 370 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