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+dentate_gyrus,Mossy,unspliced_only,75,2,0.48850250244140625,0.2958618998527527,0.19264060258865356,3018,114,12,2163.257184430702,2125.793223819302,0.17533616653974776 +dentate_gyrus,Neuroblast,scPTR_gamma,417,2,0.3079949915409088,0.32819610834121704,-0.020201116800308228,2951,166,10,2154.1149045521292,2125.793223819302,0.191766510004646 +dentate_gyrus,Neuroblast,raw_u_s_ratio,417,2,0.5127217769622803,0.28867948055267334,0.22404229640960693,2796,146,12,2139.602633617351,2125.793223819302,0.3502169658004279 +dentate_gyrus,Neuroblast,unspliced_only,417,2,0.30622172355651855,0.35018080472946167,-0.043959081172943115,3927,136,12,2137.7159154145797,2125.793223819302,0.3749809196340925 +dentate_gyrus,OL,scPTR_gamma,50,2,0.3665846586227417,0.1421527862548828,0.2244318723678589,1354,42,6,2333.402689873418,2125.793223819302,0.0016200248962954219 +dentate_gyrus,OL,raw_u_s_ratio,50,2,0.26753032207489014,0.1421527862548828,0.12537753582000732,1360,38,6,2331.784251968504,2125.793223819302,0.0014728785128519262 +dentate_gyrus,OL,unspliced_only,50,2,0.534844160079956,0.24209313094615936,0.2927510291337967,2100,102,12,2384.0226221079693,2125.793223819302,6.428967723741294e-08 +dentate_gyrus,OPC,scPTR_gamma,53,2,0.3932024836540222,0.17364031076431274,0.21956217288970947,1012,14,4,2233.8670157068063,2125.793223819302,0.2601786601709334 +dentate_gyrus,OPC,raw_u_s_ratio,53,2,0.33244553208351135,0.17718559503555298,0.15525993704795837,1068,20,4,2207.5555555555557,2125.793223819302,0.34316186894633194 +dentate_gyrus,Radial Glia-like,scPTR_gamma,51,2,0.3118036091327667,0.027165990322828293,0.28463761880993843,146,0,0,2082.2624113475176,2125.793223819302,0.6478332101393478 +dentate_gyrus,Radial Glia-like,raw_u_s_ratio,51,2,0.20574317872524261,0.027165990322828293,0.17857718840241432,133,0,0,2087.2,2125.793223819302,0.6635062058661867 +dentate_gyrus,Radial Glia-like,unspliced_only,51,2,0.2914108335971832,0.18258264660835266,0.10882818698883057,1783,38,4,2210.9969474969475,2125.793223819302,0.039140414174654435 diff --git a/output/comprehensive_fixes/results/coherence_ablation.json b/output/comprehensive_fixes/results/coherence_ablation.json new file mode 100644 index 0000000000000000000000000000000000000000..bb262b23512309bed80b7f22f2d1afb6264ee98c --- /dev/null +++ b/output/comprehensive_fixes/results/coherence_ablation.json @@ -0,0 +1,26 @@ +{ + "n_clusters_tested": 19, + "summary_by_method": { + "scPTR_gamma": { + "n_clusters": 19, + "mean_invisibility": 0.08081366305582617, + "total_sig_pathways": 2622, + "total_expected_pathways": 180, + "mean_diff_genes": 3719.157894736842 + }, + "raw_u_s_ratio": { + "n_clusters": 19, + "mean_invisibility": 0.15686803808631866, + "total_sig_pathways": 2372, + "total_expected_pathways": 168, + "mean_diff_genes": 3388.4736842105262 + }, + "unspliced_only": { + "n_clusters": 18, + "mean_invisibility": 0.25947572973867256, + "total_sig_pathways": 2258, + "total_expected_pathways": 192, + "mean_diff_genes": 5208.888888888889 + } + } +} \ No newline at end of file diff --git a/output/comprehensive_fixes/results/coherence_ablation_pathways.csv b/output/comprehensive_fixes/results/coherence_ablation_pathways.csv new file mode 100644 index 0000000000000000000000000000000000000000..dd3f2a6873db999e9c28ba015ecbfca608625663 --- /dev/null +++ b/output/comprehensive_fixes/results/coherence_ablation_pathways.csv @@ -0,0 +1,271 @@ +dataset,cluster,method,pathway +pancreas,Alpha,scPTR_gamma,Protein processing in endoplasmic reticulum +pancreas,Alpha,scPTR_gamma,Autophagy +pancreas,Alpha,scPTR_gamma,Thermogenesis +pancreas,Alpha,scPTR_gamma,Non-alcoholic fatty liver disease (NAFLD) +pancreas,Alpha,scPTR_gamma,Huntington disease +pancreas,Alpha,raw_u_s_ratio,Spliceosome +pancreas,Alpha,raw_u_s_ratio,Protein processing in endoplasmic reticulum +pancreas,Alpha,raw_u_s_ratio,RNA 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EP,raw_u_s_ratio,Ubiquitin mediated proteolysis +pancreas,Ngn3 high EP,raw_u_s_ratio,Spliceosome +pancreas,Ngn3 high EP,raw_u_s_ratio,Thermogenesis +pancreas,Ngn3 high EP,raw_u_s_ratio,Autophagy +pancreas,Ngn3 high EP,unspliced_only,Autophagy +pancreas,Ngn3 high EP,unspliced_only,Protein processing in endoplasmic reticulum +pancreas,Ngn3 high EP,unspliced_only,Ubiquitin mediated proteolysis +pancreas,Ngn3 high EP,unspliced_only,Bacterial invasion of epithelial cells +pancreas,Ngn3 high EP,unspliced_only,Axon guidance +pancreas,Ngn3 low EP,scPTR_gamma,RNA transport +pancreas,Ngn3 low EP,scPTR_gamma,Protein processing in endoplasmic reticulum +pancreas,Ngn3 low EP,scPTR_gamma,Spliceosome +pancreas,Ngn3 low EP,scPTR_gamma,Cell cycle +pancreas,Ngn3 low EP,scPTR_gamma,Thermogenesis +pancreas,Ngn3 low EP,raw_u_s_ratio,RNA transport +pancreas,Ngn3 low EP,raw_u_s_ratio,Thermogenesis +pancreas,Ngn3 low EP,raw_u_s_ratio,Spliceosome +pancreas,Ngn3 low EP,raw_u_s_ratio,Cell cycle +pancreas,Ngn3 low EP,raw_u_s_ratio,Nucleotide excision repair +pancreas,Ngn3 low EP,unspliced_only,Bacterial invasion of epithelial cells +pancreas,Ngn3 low EP,unspliced_only,Cell cycle +pancreas,Ngn3 low EP,unspliced_only,Pancreatic cancer +pancreas,Ngn3 low EP,unspliced_only,Protein processing in endoplasmic reticulum +pancreas,Ngn3 low EP,unspliced_only,Chronic myeloid leukemia +pancreas,Pre-endocrine,scPTR_gamma,Autophagy +pancreas,Pre-endocrine,scPTR_gamma,Protein processing in endoplasmic reticulum +pancreas,Pre-endocrine,scPTR_gamma,Huntington disease +pancreas,Pre-endocrine,scPTR_gamma,Thermogenesis +pancreas,Pre-endocrine,scPTR_gamma,Oxidative phosphorylation +pancreas,Pre-endocrine,raw_u_s_ratio,Protein processing in endoplasmic reticulum +pancreas,Pre-endocrine,raw_u_s_ratio,RNA transport +pancreas,Pre-endocrine,raw_u_s_ratio,Spliceosome +pancreas,Pre-endocrine,raw_u_s_ratio,Alzheimer disease +pancreas,Pre-endocrine,raw_u_s_ratio,Renal cell carcinoma 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immature,scPTR_gamma,Huntington disease +dentate_gyrus,Granule immature,scPTR_gamma,Parkinson disease +dentate_gyrus,Granule immature,scPTR_gamma,Retrograde endocannabinoid signaling +dentate_gyrus,Granule immature,scPTR_gamma,Long-term potentiation +dentate_gyrus,Granule immature,scPTR_gamma,Long-term depression +dentate_gyrus,Granule immature,raw_u_s_ratio,Huntington disease +dentate_gyrus,Granule immature,raw_u_s_ratio,Parkinson disease +dentate_gyrus,Granule immature,raw_u_s_ratio,Retrograde endocannabinoid signaling +dentate_gyrus,Granule immature,raw_u_s_ratio,Long-term potentiation +dentate_gyrus,Granule immature,raw_u_s_ratio,Long-term depression +dentate_gyrus,Granule immature,unspliced_only,Retrograde endocannabinoid signaling +dentate_gyrus,Granule immature,unspliced_only,Huntington disease +dentate_gyrus,Granule immature,unspliced_only,Long-term potentiation +dentate_gyrus,Granule immature,unspliced_only,Alzheimer disease +dentate_gyrus,Granule immature,unspliced_only,Oxidative phosphorylation +dentate_gyrus,Granule mature,scPTR_gamma,Alzheimer disease +dentate_gyrus,Granule mature,scPTR_gamma,Spliceosome +dentate_gyrus,Granule mature,scPTR_gamma,Huntington disease +dentate_gyrus,Granule mature,scPTR_gamma,Ubiquitin mediated proteolysis +dentate_gyrus,Granule mature,scPTR_gamma,Oxidative phosphorylation +dentate_gyrus,Granule mature,raw_u_s_ratio,Alzheimer disease +dentate_gyrus,Granule mature,raw_u_s_ratio,Huntington disease +dentate_gyrus,Granule mature,raw_u_s_ratio,Ubiquitin mediated proteolysis +dentate_gyrus,Granule mature,raw_u_s_ratio,Spliceosome +dentate_gyrus,Granule mature,raw_u_s_ratio,Ribosome +dentate_gyrus,Granule mature,unspliced_only,Huntington disease +dentate_gyrus,Granule mature,unspliced_only,Thermogenesis +dentate_gyrus,Granule mature,unspliced_only,Oxidative phosphorylation +dentate_gyrus,Granule mature,unspliced_only,Alzheimer disease +dentate_gyrus,Granule mature,unspliced_only,Parkinson disease +dentate_gyrus,Microglia,scPTR_gamma,Alzheimer disease +dentate_gyrus,Microglia,scPTR_gamma,Long-term potentiation +dentate_gyrus,Microglia,scPTR_gamma,Glutamatergic synapse +dentate_gyrus,Microglia,scPTR_gamma,Dopaminergic synapse +dentate_gyrus,Microglia,scPTR_gamma,Retrograde endocannabinoid signaling +dentate_gyrus,Microglia,raw_u_s_ratio,Ribosome +dentate_gyrus,Microglia,raw_u_s_ratio,Long-term potentiation +dentate_gyrus,Microglia,raw_u_s_ratio,Renal cell carcinoma +dentate_gyrus,Microglia,raw_u_s_ratio,Rap1 signaling pathway +dentate_gyrus,Microglia,raw_u_s_ratio,Ras signaling pathway +dentate_gyrus,Microglia,unspliced_only,Retrograde endocannabinoid signaling +dentate_gyrus,Microglia,unspliced_only,Ribosome +dentate_gyrus,Microglia,unspliced_only,Long-term potentiation +dentate_gyrus,Microglia,unspliced_only,Axon guidance +dentate_gyrus,Microglia,unspliced_only,Circadian entrainment +dentate_gyrus,Mossy,scPTR_gamma,Thermogenesis +dentate_gyrus,Mossy,scPTR_gamma,Ribosome +dentate_gyrus,Mossy,scPTR_gamma,Huntington disease +dentate_gyrus,Mossy,scPTR_gamma,Parkinson disease +dentate_gyrus,Mossy,scPTR_gamma,Alzheimer disease +dentate_gyrus,Mossy,raw_u_s_ratio,Thermogenesis +dentate_gyrus,Mossy,raw_u_s_ratio,mRNA surveillance pathway +dentate_gyrus,Mossy,raw_u_s_ratio,RNA transport +dentate_gyrus,Mossy,raw_u_s_ratio,cAMP signaling pathway +dentate_gyrus,Mossy,raw_u_s_ratio,Long-term potentiation +dentate_gyrus,Mossy,unspliced_only,Huntington disease +dentate_gyrus,Mossy,unspliced_only,Ribosome +dentate_gyrus,Mossy,unspliced_only,Alzheimer disease +dentate_gyrus,Mossy,unspliced_only,Retrograde endocannabinoid signaling +dentate_gyrus,Mossy,unspliced_only,Oxidative phosphorylation +dentate_gyrus,Neuroblast,scPTR_gamma,Alzheimer disease +dentate_gyrus,Neuroblast,scPTR_gamma,Long-term potentiation +dentate_gyrus,Neuroblast,scPTR_gamma,Retrograde endocannabinoid signaling +dentate_gyrus,Neuroblast,scPTR_gamma,Spliceosome +dentate_gyrus,Neuroblast,scPTR_gamma,Huntington disease +dentate_gyrus,Neuroblast,raw_u_s_ratio,Retrograde endocannabinoid signaling +dentate_gyrus,Neuroblast,raw_u_s_ratio,Long-term potentiation +dentate_gyrus,Neuroblast,raw_u_s_ratio,Alzheimer disease +dentate_gyrus,Neuroblast,raw_u_s_ratio,Thermogenesis +dentate_gyrus,Neuroblast,raw_u_s_ratio,Spliceosome +dentate_gyrus,Neuroblast,unspliced_only,Retrograde endocannabinoid signaling +dentate_gyrus,Neuroblast,unspliced_only,Thermogenesis +dentate_gyrus,Neuroblast,unspliced_only,Ribosome +dentate_gyrus,Neuroblast,unspliced_only,Huntington disease +dentate_gyrus,Neuroblast,unspliced_only,Oxidative phosphorylation +dentate_gyrus,OL,scPTR_gamma,Ribosome +dentate_gyrus,OL,scPTR_gamma,Huntington disease +dentate_gyrus,OL,scPTR_gamma,Long-term potentiation +dentate_gyrus,OL,scPTR_gamma,Retrograde endocannabinoid signaling +dentate_gyrus,OL,scPTR_gamma,Endocytosis +dentate_gyrus,OL,raw_u_s_ratio,Ribosome +dentate_gyrus,OL,raw_u_s_ratio,Huntington disease +dentate_gyrus,OL,raw_u_s_ratio,Long-term potentiation +dentate_gyrus,OL,raw_u_s_ratio,Retrograde endocannabinoid signaling +dentate_gyrus,OL,raw_u_s_ratio,Endocytosis +dentate_gyrus,OL,unspliced_only,Retrograde endocannabinoid signaling +dentate_gyrus,OL,unspliced_only,Long-term potentiation +dentate_gyrus,OL,unspliced_only,Long-term depression +dentate_gyrus,OL,unspliced_only,Alzheimer disease +dentate_gyrus,OL,unspliced_only,Huntington disease +dentate_gyrus,OPC,scPTR_gamma,Spliceosome +dentate_gyrus,OPC,scPTR_gamma,Ribosome +dentate_gyrus,OPC,scPTR_gamma,Thermogenesis +dentate_gyrus,OPC,scPTR_gamma,Axon guidance +dentate_gyrus,OPC,scPTR_gamma,Oxidative phosphorylation +dentate_gyrus,OPC,raw_u_s_ratio,Spliceosome +dentate_gyrus,OPC,raw_u_s_ratio,Ribosome +dentate_gyrus,OPC,raw_u_s_ratio,Oxidative phosphorylation +dentate_gyrus,OPC,raw_u_s_ratio,Axon guidance +dentate_gyrus,OPC,raw_u_s_ratio,Thermogenesis +dentate_gyrus,Radial Glia-like,unspliced_only,RNA transport +dentate_gyrus,Radial Glia-like,unspliced_only,Huntington disease +dentate_gyrus,Radial Glia-like,unspliced_only,Ribosome +dentate_gyrus,Radial Glia-like,unspliced_only,Protein processing in endoplasmic reticulum +dentate_gyrus,Radial Glia-like,unspliced_only,Oxidative phosphorylation diff --git a/output/comprehensive_fixes/results/hub_consistency.json b/output/comprehensive_fixes/results/hub_consistency.json new file mode 100644 index 0000000000000000000000000000000000000000..4174b76a2b3a006dd002a77271c1154a199b45c2 --- /dev/null +++ b/output/comprehensive_fixes/results/hub_consistency.json @@ -0,0 +1,179 @@ +{ + "pairwise_correlations": [ + { + "dataset_a": "dentate_gyrus", + "dataset_b": "neuroblastoma", + "spearman_r": 0.6207522318391883, + "spearman_p": 0.07443796489097941, + "n_shared": 9 + }, + { + "dataset_a": "dentate_gyrus", + "dataset_b": "pancreas", + "spearman_r": 0.34788117561515225, + "spearman_p": 0.17122073706262758, + "n_shared": 17 + }, + { + "dataset_a": "neuroblastoma", + "dataset_b": "pancreas", + "spearman_r": 0.6906864796671202, + "spearman_p": 0.012888876660556747, + "n_shared": 12 + } + ], + "universal_hubs": [ + { + "rbp": "YBX1", + "n_datasets_top20": 3, + "datasets": "dentate_gyrus, neuroblastoma, pancreas", + "targets_dentate_gyrus": 550, + "targets_neuroblastoma": 21, + "targets_pancreas": 158 + }, + { + "rbp": "FUS", + "n_datasets_top20": 2, + "datasets": "neuroblastoma, pancreas", + "targets_dentate_gyrus": 0, + "targets_neuroblastoma": 14, + "targets_pancreas": 32 + }, + { + "rbp": "HNRNPA1", + "n_datasets_top20": 2, + "datasets": "dentate_gyrus, pancreas", + "targets_dentate_gyrus": 209, + "targets_neuroblastoma": 0, + "targets_pancreas": 166 + }, + { + "rbp": "HNRNPA2B1", + "n_datasets_top20": 2, + "datasets": "neuroblastoma, pancreas", + "targets_dentate_gyrus": 0, + "targets_neuroblastoma": 46, + "targets_pancreas": 12 + }, + { + "rbp": "ELAVL1", + "n_datasets_top20": 2, + "datasets": 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(GO:0097553)", + "Membrane Depolarization During Cardiac Muscle Cell Action Potential (GO:0086012)" + ] + }, + { + "rbp": "DDX39B", + "n_targets": 21, + "n_sig_terms": 3, + "has_sig": true, + "top_terms": [ + "Positive Regulation Of Mitotic Cell Cycle Phase Transition (GO:1901992)", + "Negative Regulation Of Macromolecule Biosynthetic Process (GO:0010558)", + "Negative Regulation Of Cellular Biosynthetic Process (GO:0031327)" + ] + }, + { + "rbp": "DDX5", + "n_targets": 80, + "n_sig_terms": 28, + "has_sig": true, + "top_terms": [ + "Regulation Of Cell-Cell Adhesion (GO:0022407)", + "Establishment Of Centrosome Localization (GO:0051660)", + "Calcium Ion Transmembrane Import Into Cytosol (GO:0097553)", + "Calcium Ion Import Across Plasma Membrane (GO:0098703)", + "Response To Sterol (GO:0036314)" + ] + }, + { + "rbp": "DDX6", + "n_targets": 27, + "n_sig_terms": 3, + "has_sig": true, + "top_terms": [ + "Establishment Of Centrosome Localization (GO:0051660)", + "Regulation Of Cell-Cell 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Voltage-Gated Calcium Channel (GO:1902514)", + "Response To Sterol (GO:0036314)", + "Positive Regulation Of Voltage-Gated Calcium Channel Activity (GO:1901387)", + "Membrane Depolarization During Cardiac Muscle Cell Action Potential (GO:0086012)" + ] + }, + { + "rbp": "SRSF7", + "n_targets": 29, + "n_sig_terms": 13, + "has_sig": true, + "top_terms": [ + "Positive Regulation Of Cell Projection Organization (GO:0031346)", + "Heterophilic Cell-Cell Adhesion Via Plasma Membrane Cell Adhesion Molecules (GO:0007157)", + "Positive Regulation Of Developmental Growth (GO:0048639)", + "Regulation Of Exocytosis (GO:0017157)", + "Positive Regulation Of Mitotic Cell Cycle Phase Transition (GO:1901992)" + ] + }, + { + "rbp": "TNRC6B", + "n_targets": 12, + "n_sig_terms": 14, + "has_sig": true, + "top_terms": [ + "Insulin Secretion (GO:0030073)", + "Neurotransmitter Secretion (GO:0007269)", + "Positive Regulation Of Synaptic Transmission (GO:0050806)", + "Cellular Response To Transforming Growth Factor Beta Stimulus (GO:0071560)", + "Cellular Response To Oxidative Stress (GO:0034599)" + ] + }, + { + "rbp": "U2AF1", + "n_targets": 22, + "n_sig_terms": 9, + "has_sig": true, + "top_terms": [ + "Response To Sterol (GO:0036314)", + "Regulation Of Smoothened Signaling Pathway (GO:0008589)", + "Regulation Of Lipid Metabolic Process (GO:0019216)", + "Regulation Of Nucleic Acid-Templated Transcription (GO:1903506)", + "Adenylate Cyclase-Modulating G Protein-Coupled Receptor Signaling Pathway (GO:0007188)" + ] + }, + { + "rbp": "XRN2", + "n_targets": 24, + "n_sig_terms": 2, + "has_sig": true, + "top_terms": [ + "Transcription By RNA Polymerase II (GO:0006366)", + "Proteolysis Involved In Protein Catabolic Process (GO:0051603)" + ] + }, + { + "rbp": "YBX1", + "n_targets": 30, + "n_sig_terms": 20, + "has_sig": true, + "top_terms": [ + "Cellular Response To Amyloid-Beta (GO:1904646)", + "Calcium Ion Import Across Plasma Membrane (GO:0098703)", + "Regulation Of Cell-Cell Adhesion (GO:0022407)", + "Response To Amyloid-Beta (GO:1904645)", + "Heterophilic Cell-Cell Adhesion Via Plasma Membrane Cell Adhesion Molecules (GO:0007157)" + ] + }, + { + "rbp": "YTHDF2", + "n_targets": 19, + "n_sig_terms": 2, + "has_sig": true, + "top_terms": [ + "Proteolysis Involved In Protein Catabolic Process (GO:0051603)", + "Adenylate Cyclase-Modulating G Protein-Coupled Receptor Signaling Pathway (GO:0007188)" + ] + }, + { + "rbp": "ZFP36L1", + "n_targets": 97, + "n_sig_terms": 54, + "has_sig": true, + "top_terms": [ + "Membrane Depolarization During Cardiac Muscle Cell Action Potential (GO:0086012)", + "Calcium Ion Import Across Plasma Membrane (GO:0098703)", + "Regulation Of Heart Rate By Cardiac Conduction (GO:0086091)", + "Regulation Of Cell-Cell Adhesion (GO:0022407)", + "Cardiac Conduction (GO:0061337)" + ] + } + ] + }, + "dentate_gyrus": { + "n_eligible_rbps": 17, + "n_with_sig_go": 17, + "frac_with_sig_go": 1.0, + "mean_cross_rbp_jaccard": 0.173640289449113, + "n_known_biology_matches": 1, + "concordance_hits": [ + { + "rbp": "RBFOX3", + "matched_keywords": [ + "synap" + ] + } + ], + "bootstrap_null_frac": 0.916, + "per_rbp": [ + { + "rbp": "CELF1", + "n_targets": 16, + "n_sig_terms": 13, + "has_sig": true, + "top_terms": [ + "Dense Core Granule Cytoskeletal Transport (GO:0099519)", + "Regulation Of Dendritic Spine Development (GO:0060998)", + "Regulation Of Postsynapse Organization (GO:0099175)", + "Regulation Of Dendritic Spine Morphogenesis (GO:0061001)", + "Regulation Of Neuron Projection Development (GO:0010975)" + ] + }, + { + "rbp": "CELF4", + "n_targets": 31, + "n_sig_terms": 24, + "has_sig": true, + "top_terms": [ + "Dense Core Granule Cytoskeletal Transport (GO:0099519)", + "Regulation Of Dendritic Spine Development (GO:0060998)", + "Regulation Of JNK Cascade (GO:0046328)", + "Regulation Of Postsynapse Organization (GO:0099175)", + "Regulation Of Dendritic Spine Morphogenesis (GO:0061001)" + ] + }, + { + "rbp": "DDX39B", + 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0000000000000000000000000000000000000000..4aa53de01752f7fe168c53e37e94fda1ea241412 --- /dev/null +++ b/output/tier3/results/depmap_validation.csv @@ -0,0 +1,4 @@ +dataset,n_hub_rbps,n_nonhub_rbps,hub_mean_dep,nonhub_mean_dep,mannwhitney_p,hub_more_essential +pancreas,20,166,-0.8248364709210279,-0.46897721658598923,0.006143886726582537,True +dentate_gyrus,20,166,-0.654233695208099,-0.48953176787670366,0.12696093029879818,False +neuroblastoma,20,166,-1.1200964888610585,-0.43340372044863623,6.368369588864245e-05,True diff --git a/output/tier3/results/neuroblastoma_diff_degraded.csv b/output/tier3/results/neuroblastoma_diff_degraded.csv new file mode 100644 index 0000000000000000000000000000000000000000..5b94d49b2269f830118d3d84b8ea0b9b0a528bc0 --- /dev/null +++ b/output/tier3/results/neuroblastoma_diff_degraded.csv @@ -0,0 +1,7585 @@ +gene,p_value,log2_fc_gamma,fdr +ROBO1,0.0,17.306063,0.0 +PRKCE,0.0,16.490652,0.0 +SOX5,1.4701626187669009e-165,16.084223,1.1445045037958606e-164 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+MFSD1,1.6242687683271483e-115,0.0,6.560771887752795e-115 diff --git a/output/wrapup/figures/pancreas_sparsity.png b/output/wrapup/figures/pancreas_sparsity.png new file mode 100644 index 0000000000000000000000000000000000000000..71896c557fbf827d45c36020cdcb2dfb14e7382b Binary files /dev/null and b/output/wrapup/figures/pancreas_sparsity.png differ diff --git a/src/scptr/__init__.py b/src/scptr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..99d9cca696155caf6a9969bdd1ea2c8c6df9fab1 --- /dev/null +++ b/src/scptr/__init__.py @@ -0,0 +1,15 @@ +"""scPTR: Single-cell post-transcriptional regulation analysis.""" + +__version__ = "0.1.0" + +from . import preprocessing as pp +from . import tools as tl +from . import plotting as pl +from . import readwrite +from . import datasets +from . import benchmark + +try: + from . import deep +except ImportError: + pass # torch not installed; deep module unavailable diff --git a/src/scptr/_constants.py b/src/scptr/_constants.py new file mode 100644 index 0000000000000000000000000000000000000000..064f00b59fe8feb57794224ac6f23213361a6ccb --- /dev/null +++ b/src/scptr/_constants.py @@ -0,0 +1,31 @@ +"""Layer name constants and defaults for scPTR.""" + +# Layer names +UNSPLICED = "unspliced" +SPLICED = "spliced" +SMOOTHED_UNSPLICED = "Mu" +SMOOTHED_SPLICED = "Ms" +GAMMA = "gamma" +PT_VELOCITY = "pt_velocity" +VELOCITY_S = "velocity_S" + +# var columns +BETA = "beta" +TF_SCORE = "tf_score" +PTF_SCORE = "ptf_score" + +# obs columns +PT_STATE = "pt_state" + +# uns keys +UNS_KEY = "scptr" + +# Defaults +DEFAULT_N_NEIGHBORS = 30 +DEFAULT_BANDWIDTH = "adaptive" +DEFAULT_BETA_QUANTILE = 0.95 +DEFAULT_CLIP_QUANTILE = 0.99 +DEFAULT_GENE_BATCH_SIZE = 500 +DEFAULT_LEIDEN_RESOLUTION = 1.0 +DEFAULT_MIN_UNSPLICED_COUNTS = 10 +DEFAULT_MIN_UNSPLICED_CELLS = 10 diff --git a/src/scptr/_numba_kernels.py b/src/scptr/_numba_kernels.py new file mode 100644 index 0000000000000000000000000000000000000000..098cfce3792a440798ff61df68d776ab7aefccac --- /dev/null +++ b/src/scptr/_numba_kernels.py @@ -0,0 +1,214 @@ +"""Numba-JIT compiled kernels for scPTR.""" + +from __future__ import annotations + +import numpy as np +import numba as nb + + +@nb.njit(parallel=True, cache=True) +def _smooth_kernel( + data: np.ndarray, + indices_flat: np.ndarray, + indptr: np.ndarray, + distances_flat: np.ndarray, + bandwidths: np.ndarray, + out: np.ndarray, +) -> None: + """Gaussian kernel smoothing over kNN graph. + + Parameters + ---------- + data : (n_obs, n_genes) float32 + Gene expression matrix to smooth. + indices_flat : (nnz,) int32 + CSR indices array of the kNN graph. + indptr : (n_obs+1,) int32 + CSR indptr array of the kNN graph. + distances_flat : (nnz,) float32 + CSR data array (distances) of the kNN graph. + bandwidths : (n_obs,) float32 + Per-cell bandwidth for the Gaussian kernel. + out : (n_obs, n_genes) float32 + Output array (pre-allocated). + """ + n_obs = data.shape[0] + n_genes = data.shape[1] + + for i in nb.prange(n_obs): + start = indptr[i] + end = indptr[i + 1] + bw = bandwidths[i] + bw2 = bw * bw + if bw2 < 1e-12: + bw2 = 1e-12 + + # Compute weights + n_neighbors = end - start + weights = np.empty(n_neighbors + 1, dtype=np.float32) + neighbor_idx = np.empty(n_neighbors + 1, dtype=np.int64) + + # Self-connection with weight 1 + weights[0] = 1.0 + neighbor_idx[0] = i + total_weight = 1.0 + + for k in range(n_neighbors): + j = indices_flat[start + k] + d = distances_flat[start + k] + w = np.exp(-0.5 * d * d / bw2) + weights[k + 1] = w + neighbor_idx[k + 1] = j + total_weight += w + + # Weighted average + inv_total = 1.0 / total_weight + for g in range(n_genes): + val = 0.0 + for k in range(n_neighbors + 1): + val += weights[k] * data[neighbor_idx[k], g] + out[i, g] = val * inv_total + + +@nb.njit(parallel=True, cache=True) +def _compute_adaptive_bandwidths( + distances_flat: np.ndarray, + indptr: np.ndarray, +) -> np.ndarray: + """Compute per-cell adaptive bandwidth as median neighbor distance. + + Parameters + ---------- + distances_flat : (nnz,) float32 + indptr : (n_obs+1,) int32 + + Returns + ------- + bandwidths : (n_obs,) float32 + """ + n_obs = indptr.shape[0] - 1 + bandwidths = np.empty(n_obs, dtype=np.float32) + + for i in nb.prange(n_obs): + start = indptr[i] + end = indptr[i + 1] + n_neighbors = end - start + if n_neighbors == 0: + bandwidths[i] = 1.0 + continue + + # Copy distances for this cell and sort to get median + dists = np.empty(n_neighbors, dtype=np.float32) + for k in range(n_neighbors): + dists[k] = distances_flat[start + k] + dists.sort() + + mid = n_neighbors // 2 + if n_neighbors % 2 == 0: + bandwidths[i] = (dists[mid - 1] + dists[mid]) / 2.0 + else: + bandwidths[i] = dists[mid] + + if bandwidths[i] < 1e-6: + bandwidths[i] = 1.0 + + return bandwidths + + +@nb.njit(parallel=True, cache=True) +def _compute_gamma_kernel( + u_smooth: np.ndarray, + s_smooth: np.ndarray, + beta: np.ndarray, + clip_vals: np.ndarray, + out: np.ndarray, +) -> None: + """Compute per-cell per-gene degradation rate gamma. + + gamma[i,g] = beta[g] * u_smooth[i,g] / max(s_smooth[i,g], 1e-6) + Clipped at clip_vals[g] per gene. + + Parameters + ---------- + u_smooth : (n_obs, n_genes) float32 + s_smooth : (n_obs, n_genes) float32 + beta : (n_genes,) float32 + clip_vals : (n_genes,) float32 + Per-gene clip values (e.g., 99th percentile). + out : (n_obs, n_genes) float32 + """ + n_obs = u_smooth.shape[0] + n_genes = u_smooth.shape[1] + + for i in nb.prange(n_obs): + for g in range(n_genes): + s_val = s_smooth[i, g] + if s_val < 1e-6: + s_val = 1e-6 + gamma_val = beta[g] * u_smooth[i, g] / s_val + if gamma_val > clip_vals[g]: + gamma_val = clip_vals[g] + if gamma_val < 0.0: + gamma_val = 0.0 + out[i, g] = gamma_val + + +@nb.njit(parallel=True, cache=True) +def _velocity_kernel( + gamma: np.ndarray, + indices_flat: np.ndarray, + indptr: np.ndarray, + distances_flat: np.ndarray, + bandwidths: np.ndarray, + out: np.ndarray, +) -> None: + """Compute PT velocity as weighted mean gamma difference from neighbors. + + v[i,g] = sum_j(w_ij * (gamma[j,g] - gamma[i,g])) + + Parameters + ---------- + gamma : (n_obs, n_genes) float32 + indices_flat, indptr, distances_flat : kNN graph CSR arrays + bandwidths : (n_obs,) float32 + out : (n_obs, n_genes) float32 + """ + n_obs = gamma.shape[0] + n_genes = gamma.shape[1] + + for i in nb.prange(n_obs): + start = indptr[i] + end = indptr[i + 1] + bw = bandwidths[i] + bw2 = bw * bw + if bw2 < 1e-12: + bw2 = 1e-12 + + n_neighbors = end - start + if n_neighbors == 0: + for g in range(n_genes): + out[i, g] = 0.0 + continue + + # Compute weights + total_weight = 0.0 + for k in range(n_neighbors): + d = distances_flat[start + k] + w = np.exp(-0.5 * d * d / bw2) + total_weight += w + + if total_weight < 1e-12: + for g in range(n_genes): + out[i, g] = 0.0 + continue + + inv_total = 1.0 / total_weight + + for g in range(n_genes): + val = 0.0 + for k in range(n_neighbors): + j = indices_flat[start + k] + d = distances_flat[start + k] + w = np.exp(-0.5 * d * d / bw2) + val += w * (gamma[j, g] - gamma[i, g]) + out[i, g] = val * inv_total diff --git a/src/scptr/_utils.py b/src/scptr/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..757bb56468e6a05cbdf0a95634e344ec3f87be57 --- /dev/null +++ b/src/scptr/_utils.py @@ -0,0 +1,57 @@ +"""Utility functions for layer resolution, validation, and parameter logging.""" + +from __future__ import annotations + +import logging +from typing import Optional + +import numpy as np +from anndata import AnnData +from scipy.sparse import issparse + +from . import _constants as C + +logger = logging.getLogger("scptr") + + +def get_layer(adata: AnnData, layer: str) -> np.ndarray: + """Return a dense numpy array for the given layer.""" + mat = adata.layers[layer] + if issparse(mat): + return np.asarray(mat.todense()) + return np.asarray(mat) + + +def require_layers(adata: AnnData, *layers: str) -> None: + """Raise KeyError if any of the specified layers are missing.""" + missing = [l for l in layers if l not in adata.layers] + if missing: + raise KeyError(f"Missing required layers: {missing}") + + +def require_obs(adata: AnnData, *keys: str) -> None: + """Raise KeyError if any of the specified obs columns are missing.""" + missing = [k for k in keys if k not in adata.obs.columns] + if missing: + raise KeyError(f"Missing required obs columns: {missing}") + + +def require_var(adata: AnnData, *keys: str) -> None: + """Raise KeyError if any of the specified var columns are missing.""" + missing = [k for k in keys if k not in adata.var.columns] + if missing: + raise KeyError(f"Missing required var columns: {missing}") + + +def log_params(adata: AnnData, step: str, params: dict) -> None: + """Record run parameters in adata.uns['scptr'].""" + if C.UNS_KEY not in adata.uns: + adata.uns[C.UNS_KEY] = {} + adata.uns[C.UNS_KEY][step] = params + + +def to_dense_float32(mat) -> np.ndarray: + """Convert a matrix (sparse or dense) to dense float32.""" + if issparse(mat): + mat = np.asarray(mat.todense()) + return np.asarray(mat, dtype=np.float32) diff --git a/src/scptr/benchmark/_consistency.py b/src/scptr/benchmark/_consistency.py new file mode 100644 index 0000000000000000000000000000000000000000..c25bb686ccd234f729416bfce162b0c6c2cfef3c --- /dev/null +++ b/src/scptr/benchmark/_consistency.py @@ -0,0 +1,104 @@ +"""Cross-dataset consistency analysis.""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +from anndata import AnnData +from scipy import stats + +from .._constants import GAMMA +from .._utils import get_layer, require_layers + + +def cross_dataset_consistency( + adatas_dict: dict[str, AnnData], + case_insensitive: bool = True, +) -> pd.DataFrame: + """Compare per-gene median gamma across multiple datasets. + + For each pair of datasets, computes Spearman and Pearson correlations + of per-gene median gamma on their shared gene set. + + Parameters + ---------- + adatas_dict + Dictionary mapping dataset name to analyzed AnnData + (each must have ``gamma`` layer). + case_insensitive + Match gene symbols case-insensitively (default True). Useful + for cross-species comparisons (mouse Titlecase vs human UPPER). + + Returns + ------- + DataFrame with columns: ``dataset_a``, ``dataset_b``, ``n_shared_genes``, + ``spearman_r``, ``pearson_r``. + """ + names = sorted(adatas_dict.keys()) + # Pre-compute median gamma for each dataset + medians = {} + for name in names: + adata = adatas_dict[name] + require_layers(adata, GAMMA) + gamma = get_layer(adata, GAMMA) + medians[name] = pd.Series( + np.median(gamma, axis=0), index=adata.var_names + ) + + records = [] + for i, name_a in enumerate(names): + for name_b in names[i + 1:]: + if case_insensitive: + # Build uppercase-to-original mapping for both + map_a = {} + for g in medians[name_a].index: + if isinstance(g, str): + map_a[g.upper()] = g + map_b = {} + for g in medians[name_b].index: + if isinstance(g, str): + map_b[g.upper()] = g + shared_upper = set(map_a.keys()) & set(map_b.keys()) + n_shared = len(shared_upper) + + if n_shared < 3: + sp_r = pe_r = np.nan + else: + ga = np.array([medians[name_a][map_a[u]] for u in shared_upper], dtype=float) + gb = np.array([medians[name_b][map_b[u]] for u in shared_upper], dtype=float) + valid = np.isfinite(ga) & np.isfinite(gb) + ga, gb = ga[valid], gb[valid] + n_shared = len(ga) + + if len(ga) < 3: + sp_r = pe_r = np.nan + else: + sp_r, _ = stats.spearmanr(ga, gb) + pe_r, _ = stats.pearsonr(ga, gb) + else: + shared = medians[name_a].index.intersection(medians[name_b].index) + n_shared = len(shared) + + if n_shared < 3: + sp_r = pe_r = np.nan + else: + ga = medians[name_a][shared].values.astype(float) + gb = medians[name_b][shared].values.astype(float) + valid = np.isfinite(ga) & np.isfinite(gb) + ga, gb = ga[valid], gb[valid] + + if len(ga) < 3: + sp_r = pe_r = np.nan + else: + sp_r, _ = stats.spearmanr(ga, gb) + pe_r, _ = stats.pearsonr(ga, gb) + + records.append({ + "dataset_a": name_a, + "dataset_b": name_b, + "n_shared_genes": n_shared, + "spearman_r": float(sp_r) if not np.isnan(sp_r) else np.nan, + "pearson_r": float(pe_r) if not np.isnan(pe_r) else np.nan, + }) + + return pd.DataFrame(records) diff --git a/src/scptr/deep/_data.py b/src/scptr/deep/_data.py new file mode 100644 index 0000000000000000000000000000000000000000..3c27950898ec25844fc1d37ea0710b537c60ec1c --- /dev/null +++ b/src/scptr/deep/_data.py @@ -0,0 +1,97 @@ +"""AnnData to PyTorch DataLoader conversion for DeepPTR.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import torch +from torch.utils.data import DataLoader, TensorDataset + +if TYPE_CHECKING: + from anndata import AnnData + + +def setup_dataloaders( + adata: AnnData, + batch_size: int = 256, + val_frac: float = 0.1, + stratify_key: str | None = None, + seed: int = 0, + num_workers: int = 0, +) -> tuple[DataLoader, DataLoader, np.ndarray, np.ndarray]: + """Build train and validation DataLoaders from an AnnData object. + + Extracts raw integer counts from ``adata.layers['spliced']`` and + ``adata.layers['unspliced']``, together with per-cell library sizes. + + Parameters + ---------- + adata + Annotated data matrix. Must contain ``layers['spliced']`` and + ``layers['unspliced']``. + batch_size + Mini-batch size. + val_frac + Fraction of cells held out for validation. + stratify_key + Optional obs column for stratified splitting. + seed + Random seed for reproducibility. + num_workers + DataLoader workers. + + Returns + ------- + train_dl, val_dl, train_idx, val_idx + """ + from scipy.sparse import issparse + + from ._utils import get_library_sizes + + def _dense(mat): + if issparse(mat): + return np.asarray(mat.todense()) + return np.asarray(mat) + + s = _dense(adata.layers["spliced"]).astype(np.float32) + u = _dense(adata.layers["unspliced"]).astype(np.float32) + l_u, l_s = get_library_sizes(adata) + + n = adata.n_obs + indices = np.arange(n) + + if stratify_key is not None and stratify_key in adata.obs.columns: + from sklearn.model_selection import StratifiedShuffleSplit + + labels = adata.obs[stratify_key].values + splitter = StratifiedShuffleSplit( + n_splits=1, test_size=val_frac, random_state=seed + ) + train_idx, val_idx = next(splitter.split(indices, labels)) + else: + rng = np.random.RandomState(seed) + perm = rng.permutation(n) + n_val = max(1, int(n * val_frac)) + val_idx = perm[:n_val] + train_idx = perm[n_val:] + + def _make_loader(idx: np.ndarray, shuffle: bool) -> DataLoader: + ds = TensorDataset( + torch.from_numpy(s[idx]), + torch.from_numpy(u[idx]), + torch.from_numpy(l_s[idx]), + torch.from_numpy(l_u[idx]), + ) + return DataLoader( + ds, + batch_size=batch_size, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=False, + drop_last=False, + ) + + train_dl = _make_loader(train_idx, shuffle=True) + val_dl = _make_loader(val_idx, shuffle=False) + return train_dl, val_dl, train_idx, val_idx diff --git a/src/scptr/deep/_distributions.py b/src/scptr/deep/_distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..0be7a3171e5e92580291be5d60f7f49d4ef95097 --- /dev/null +++ b/src/scptr/deep/_distributions.py @@ -0,0 +1,47 @@ +"""Negative binomial distribution utilities for DeepPTR.""" + +from __future__ import annotations + +import torch +from torch import Tensor + + +def log_nb_positive( + x: Tensor, + mu: Tensor, + theta: Tensor, + eps: float = 1e-8, +) -> Tensor: + """Log-likelihood of the negative binomial distribution (mean-dispersion). + + Parameterized by mean ``mu`` and inverse dispersion ``theta`` so that + Var = mu + mu^2 / theta. Larger theta → less overdispersion. + + Parameters + ---------- + x : Tensor + Observed counts (non-negative integers), shape ``(N, G)``. + mu : Tensor + Predicted mean, shape ``(N, G)``. + theta : Tensor + Inverse dispersion, shape ``(G,)`` or ``(N, G)``. + eps : float + Small constant for numerical stability. + + Returns + ------- + Tensor + Log-probability, same shape as *x*. + """ + mu = mu.clamp(min=eps) + theta = theta.clamp(min=eps) + + log_theta_mu = torch.log(theta + mu + eps) + + return ( + torch.lgamma(x + theta) + - torch.lgamma(theta) + - torch.lgamma(x + 1.0) + + theta * (torch.log(theta + eps) - log_theta_mu) + + x * (torch.log(mu + eps) - log_theta_mu) + ) diff --git a/src/scptr/deep/_model.py b/src/scptr/deep/_model.py new file mode 100644 index 0000000000000000000000000000000000000000..8c65c05d2ff91f4c09a07ecf79876494503b9bd0 --- /dev/null +++ b/src/scptr/deep/_model.py @@ -0,0 +1,312 @@ +"""Structured VAE with kinetic model decoder for DeepPTR. + +The generative model factorises latent space into transcriptional (z_T) and +post-transcriptional (z_PT) factors. The decoder uses RNA kinetic equations +to map these to expected unspliced / spliced counts observed through a +negative binomial likelihood. +""" + +from __future__ import annotations + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from ._distributions import log_nb_positive +from ._utils import init_weights + + +# --------------------------------------------------------------------------- +# Encoder +# --------------------------------------------------------------------------- + + +class Encoder(nn.Module): + """Amortised inference network q(z_T, z_PT | x). + + Takes concatenated log1p(spliced, unspliced) and outputs mean and + log-variance for two independent Gaussian posteriors. + """ + + def __init__( + self, + n_genes: int, + d_hidden: int = 128, + d_T: int = 10, + d_PT: int = 10, + n_layers: int = 3, + dropout: float = 0.1, + ) -> None: + super().__init__() + n_input = 2 * n_genes # [log1p(s); log1p(u)] + + layers: list[nn.Module] = [] + in_dim = n_input + for _ in range(n_layers): + layers.extend( + [ + nn.Linear(in_dim, d_hidden), + nn.LayerNorm(d_hidden), + nn.ReLU(), + nn.Dropout(dropout), + ] + ) + in_dim = d_hidden + self.shared = nn.Sequential(*layers) + + # Heads for z_T + self.mu_T = nn.Linear(d_hidden, d_T) + self.logvar_T = nn.Linear(d_hidden, d_T) + + # Heads for z_PT + self.mu_PT = nn.Linear(d_hidden, d_PT) + self.logvar_PT = nn.Linear(d_hidden, d_PT) + + self.apply(init_weights) + + def forward( + self, s: Tensor, u: Tensor + ) -> tuple[Tensor, Tensor, Tensor, Tensor]: + """Encode observations to posterior parameters. + + Returns + ------- + mu_T, logvar_T, mu_PT, logvar_PT : Tensor + Each shape ``(N, d_*)``. + """ + x = torch.cat([torch.log1p(s), torch.log1p(u)], dim=-1) + h = self.shared(x) + return ( + self.mu_T(h), + self.logvar_T(h), + self.mu_PT(h), + self.logvar_PT(h), + ) + + +# --------------------------------------------------------------------------- +# Kinetic Decoder +# --------------------------------------------------------------------------- + + +class KineticDecoder(nn.Module): + r"""Decoder mapping (z_T, z_PT) → expected counts via kinetic model. + + .. math:: + \alpha_g &= \text{softplus}(f_\alpha(z_T))_g \\ + \gamma_g &= \text{softplus}(f_\gamma(z_{PT}))_g \\ + \beta_g &= \exp(\log\beta_g) \quad (\text{gene-specific parameter}) \\ + \mu^u_g &= \frac{\alpha_g / \beta_g}{\sum_g \alpha_g / \beta_g} + \cdot l_u \\ + \mu^s_g &= \frac{\alpha_g / \gamma_g}{\sum_g \alpha_g / \gamma_g} + \cdot l_s + """ + + def __init__( + self, + n_genes: int, + d_T: int = 10, + d_PT: int = 10, + d_hidden: int = 128, + ) -> None: + super().__init__() + self.n_genes = n_genes + + # alpha network: z_T → alpha (transcription rate) + self.f_alpha = nn.Sequential( + nn.Linear(d_T, d_hidden), + nn.LayerNorm(d_hidden), + nn.ReLU(), + nn.Linear(d_hidden, n_genes), + ) + + # gamma network: z_PT → gamma (degradation rate) + self.f_gamma = nn.Sequential( + nn.Linear(d_PT, d_hidden), + nn.LayerNorm(d_hidden), + nn.ReLU(), + nn.Linear(d_hidden, n_genes), + ) + + # Gene-specific splicing rate (not cell-specific) + self.log_beta = nn.Parameter(torch.zeros(n_genes)) + + # Inverse dispersion parameters (learnable, per-gene) + self.log_theta_s = nn.Parameter(torch.zeros(n_genes)) + self.log_theta_u = nn.Parameter(torch.zeros(n_genes)) + + self.apply(init_weights) + # Re-init log_beta/theta after apply + nn.init.zeros_(self.log_beta) + nn.init.constant_(self.log_theta_s, 2.0) # ~exp(2)≈7.4 + nn.init.constant_(self.log_theta_u, 2.0) + + def forward( + self, + z_T: Tensor, + z_PT: Tensor, + l_s: Tensor, + l_u: Tensor, + ) -> dict[str, Tensor]: + """Decode latent variables to NB distribution parameters. + + Parameters + ---------- + z_T : (N, d_T) + z_PT : (N, d_PT) + l_s : (N,) library size for spliced + l_u : (N,) library size for unspliced + + Returns + ------- + dict with keys: mu_s, mu_u, theta_s, theta_u, alpha, gamma, beta + """ + alpha = F.softplus(self.f_alpha(z_T)) # (N, G), positive + gamma = F.softplus(self.f_gamma(z_PT)) # (N, G), positive + beta = self.log_beta.exp() # (G,), positive + + eps = 1e-8 + + # Expected proportions from kinetic model + rho_u = alpha / (beta.unsqueeze(0) + eps) # (N, G) + rho_s = alpha / (gamma + eps) # (N, G) + + # Normalize to proportions (sum to 1 across genes) + rho_u = rho_u / (rho_u.sum(dim=-1, keepdim=True) + eps) + rho_s = rho_s / (rho_s.sum(dim=-1, keepdim=True) + eps) + + # Scale by library size + mu_u = rho_u * l_u.unsqueeze(-1) # (N, G) + mu_s = rho_s * l_s.unsqueeze(-1) # (N, G) + + theta_s = self.log_theta_s.exp() + theta_u = self.log_theta_u.exp() + + return { + "mu_s": mu_s, + "mu_u": mu_u, + "theta_s": theta_s, + "theta_u": theta_u, + "alpha": alpha, + "gamma": gamma, + "beta": beta, + } + + +# --------------------------------------------------------------------------- +# Full VAE +# --------------------------------------------------------------------------- + + +class DeepPTR(nn.Module): + """Structured VAE for mRNA degradation rate estimation. + + Combines an amortised encoder with a kinetic-model-constrained decoder + and a negative binomial observation model. + """ + + def __init__( + self, + n_genes: int, + d_T: int = 10, + d_PT: int = 10, + d_hidden: int = 128, + n_enc_layers: int = 3, + dropout: float = 0.1, + ) -> None: + super().__init__() + self.n_genes = n_genes + self.d_T = d_T + self.d_PT = d_PT + + self.encoder = Encoder( + n_genes=n_genes, + d_hidden=d_hidden, + d_T=d_T, + d_PT=d_PT, + n_layers=n_enc_layers, + dropout=dropout, + ) + self.decoder = KineticDecoder( + n_genes=n_genes, + d_T=d_T, + d_PT=d_PT, + d_hidden=d_hidden, + ) + + # -- helpers -- + + @staticmethod + def reparameterize(mu: Tensor, logvar: Tensor) -> Tensor: + """Sample z = mu + eps * std with reparameterization trick.""" + std = (0.5 * logvar).exp() + return mu + std * torch.randn_like(std) + + @staticmethod + def kl_divergence(mu: Tensor, logvar: Tensor) -> Tensor: + """KL(q(z) || N(0,I)), summed over latent dims, mean over batch.""" + return -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).sum(dim=-1).mean() + + # -- forward -- + + def forward( + self, + s: Tensor, + u: Tensor, + l_s: Tensor, + l_u: Tensor, + kl_weight: float = 1.0, + ) -> dict[str, Tensor]: + """Full forward pass: encode → sample → decode → loss. + + Parameters + ---------- + s : (N, G) spliced counts + u : (N, G) unspliced counts + l_s : (N,) spliced library size + l_u : (N,) unspliced library size + kl_weight : float annealing coefficient for KL term + + Returns + ------- + dict + ``loss``, ``recon_loss``, ``kl_loss``, and decoder outputs. + """ + mu_T, logvar_T, mu_PT, logvar_PT = self.encoder(s, u) + + z_T = self.reparameterize(mu_T, logvar_T) + z_PT = self.reparameterize(mu_PT, logvar_PT) + + dec = self.decoder(z_T, z_PT, l_s, l_u) + + # Reconstruction: sum NB log-likelihood over genes, mean over batch + ll_s = log_nb_positive(s, dec["mu_s"], dec["theta_s"]).sum(dim=-1).mean() + ll_u = log_nb_positive(u, dec["mu_u"], dec["theta_u"]).sum(dim=-1).mean() + recon_loss = -(ll_s + ll_u) + + kl_T = self.kl_divergence(mu_T, logvar_T) + kl_PT = self.kl_divergence(mu_PT, logvar_PT) + kl_loss = kl_T + kl_PT + + loss = recon_loss + kl_weight * kl_loss + + return { + "loss": loss, + "recon_loss": recon_loss, + "kl_loss": kl_loss, + "kl_T": kl_T, + "kl_PT": kl_PT, + "mu_T": mu_T, + "logvar_T": logvar_T, + "mu_PT": mu_PT, + "logvar_PT": logvar_PT, + **dec, + } + + @torch.no_grad() + def get_latent( + self, s: Tensor, u: Tensor + ) -> tuple[Tensor, Tensor, Tensor, Tensor]: + """Return posterior means for z_T and z_PT (no sampling).""" + mu_T, logvar_T, mu_PT, logvar_PT = self.encoder(s, u) + return mu_T, logvar_T, mu_PT, logvar_PT diff --git a/src/scptr/deep/_trainer.py b/src/scptr/deep/_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..382dd7893529159a37c2868dab2c3100ad82a0a7 --- /dev/null +++ b/src/scptr/deep/_trainer.py @@ -0,0 +1,196 @@ +"""Training loop for DeepPTR with KL warmup and early stopping.""" + +from __future__ import annotations + +import math +from dataclasses import dataclass, field + +import torch +from torch import nn +from torch.utils.data import DataLoader + +from ._model import DeepPTR + + +@dataclass +class TrainHistory: + """Stores per-epoch training metrics.""" + + train_loss: list[float] = field(default_factory=list) + val_loss: list[float] = field(default_factory=list) + train_recon: list[float] = field(default_factory=list) + val_recon: list[float] = field(default_factory=list) + train_kl: list[float] = field(default_factory=list) + val_kl: list[float] = field(default_factory=list) + kl_weight: list[float] = field(default_factory=list) + lr: list[float] = field(default_factory=list) + + +class Trainer: + """Train a :class:`DeepPTR` model. + + Parameters + ---------- + model + A :class:`DeepPTR` instance. + lr + Initial learning rate for Adam. + weight_decay + L2 regularization. + max_epochs + Maximum training epochs. + kl_warmup_epochs + Number of epochs for linear KL annealing (0→1). + patience + Early-stopping patience (starts counting after warmup). + max_grad_norm + Gradient clipping threshold. + device + ``"cuda"`` or ``"cpu"``. + """ + + def __init__( + self, + model: DeepPTR, + lr: float = 1e-3, + weight_decay: float = 1e-6, + max_epochs: int = 400, + kl_warmup_epochs: int = 50, + patience: int = 30, + max_grad_norm: float = 5.0, + device: str | None = None, + ) -> None: + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + self.device = torch.device(device) + self.model = model.to(self.device) + self.max_epochs = max_epochs + self.kl_warmup_epochs = kl_warmup_epochs + self.patience = patience + self.max_grad_norm = max_grad_norm + + self.optimizer = torch.optim.Adam( + model.parameters(), lr=lr, weight_decay=weight_decay + ) + self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + self.optimizer, mode="min", factor=0.5, patience=10, min_lr=1e-6 + ) + self.history = TrainHistory() + + def _kl_weight(self, epoch: int) -> float: + if self.kl_warmup_epochs <= 0: + return 1.0 + return min(1.0, epoch / self.kl_warmup_epochs) + + def _run_epoch( + self, loader: DataLoader, kl_w: float, train: bool = True + ) -> tuple[float, float, float]: + self.model.train(train) + total_loss = 0.0 + total_recon = 0.0 + total_kl = 0.0 + n_batches = 0 + + ctx = torch.enable_grad() if train else torch.no_grad() + with ctx: + for s, u, l_s, l_u in loader: + s = s.to(self.device) + u = u.to(self.device) + l_s = l_s.to(self.device) + l_u = l_u.to(self.device) + + out = self.model(s, u, l_s, l_u, kl_weight=kl_w) + loss = out["loss"] + + if train: + self.optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_( + self.model.parameters(), self.max_grad_norm + ) + self.optimizer.step() + + total_loss += loss.item() + total_recon += out["recon_loss"].item() + total_kl += out["kl_loss"].item() + n_batches += 1 + + return ( + total_loss / max(n_batches, 1), + total_recon / max(n_batches, 1), + total_kl / max(n_batches, 1), + ) + + def fit( + self, + train_dl: DataLoader, + val_dl: DataLoader, + verbose: bool = True, + ) -> TrainHistory: + """Run the full training loop. + + Parameters + ---------- + train_dl, val_dl + Training and validation DataLoaders. + verbose + Print progress every 10 epochs. + + Returns + ------- + TrainHistory + """ + best_val = math.inf + wait = 0 + best_state = None + + for epoch in range(1, self.max_epochs + 1): + kl_w = self._kl_weight(epoch) + + tr_loss, tr_recon, tr_kl = self._run_epoch(train_dl, kl_w, train=True) + vl_loss, vl_recon, vl_kl = self._run_epoch(val_dl, kl_w, train=False) + + self.scheduler.step(vl_loss) + cur_lr = self.optimizer.param_groups[0]["lr"] + + self.history.train_loss.append(tr_loss) + self.history.val_loss.append(vl_loss) + self.history.train_recon.append(tr_recon) + self.history.val_recon.append(vl_recon) + self.history.train_kl.append(tr_kl) + self.history.val_kl.append(vl_kl) + self.history.kl_weight.append(kl_w) + self.history.lr.append(cur_lr) + + if verbose and (epoch % 10 == 0 or epoch == 1): + print( + f"Epoch {epoch:4d} | " + f"train {tr_loss:.2f} (recon {tr_recon:.2f}, kl {tr_kl:.2f}) | " + f"val {vl_loss:.2f} | kl_w {kl_w:.3f} | lr {cur_lr:.1e}" + ) + + # Early stopping (only after warmup) + if epoch >= self.kl_warmup_epochs: + if vl_loss < best_val: + best_val = vl_loss + wait = 0 + best_state = { + k: v.cpu().clone() + for k, v in self.model.state_dict().items() + } + else: + wait += 1 + if wait >= self.patience: + if verbose: + print( + f"Early stopping at epoch {epoch} " + f"(best val={best_val:.2f})" + ) + break + + # Restore best model + if best_state is not None: + self.model.load_state_dict(best_state) + self.model.to(self.device) + + return self.history diff --git a/src/scptr/plotting/__init__.py b/src/scptr/plotting/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..314a43c65be5bcd0c5bf394acc30695fdcb6a054 --- /dev/null +++ b/src/scptr/plotting/__init__.py @@ -0,0 +1,23 @@ +"""Plotting module for scPTR.""" + +from ._phase import phase_portrait +from ._gamma import gamma_heatmap, gamma_violin +from ._states import pt_umap, pt_comparison +from ._variance import tf_ptf_scatter +from ._network import network_graph +from ._velocity import pt_velocity_embedding, pt_velocity_stream +from ._benchmark import halflife_scatter, enrichment_barplot + +__all__ = [ + "phase_portrait", + "gamma_heatmap", + "gamma_violin", + "pt_umap", + "pt_comparison", + "tf_ptf_scatter", + "network_graph", + "pt_velocity_embedding", + "pt_velocity_stream", + "halflife_scatter", + "enrichment_barplot", +] diff --git a/src/scptr/plotting/_variance.py b/src/scptr/plotting/_variance.py new file mode 100644 index 0000000000000000000000000000000000000000..dab77f8d921f153ab75defb0a7798a3693d847a7 --- /dev/null +++ b/src/scptr/plotting/_variance.py @@ -0,0 +1,111 @@ +"""TF/PTF variance decomposition plots.""" + +from __future__ import annotations + +import matplotlib.pyplot as plt +import numpy as np +from anndata import AnnData + +from .._constants import TF_SCORE, PTF_SCORE +from .._utils import require_var +from ._utils import setup_axes, save_or_show + + +def tf_ptf_scatter( + adata: AnnData, + label_top: int = 10, + figsize: tuple[float, float] = (10, 5), + save: str | None = None, + show: bool = True, + ax: plt.Axes | None = None, +) -> plt.Figure | None: + """Two-panel variance decomposition plot. + + Left: histogram of TF scores showing distribution of transcriptional + vs post-transcriptional regulation. Right: ranked TF scores with + top genes labeled. + + Parameters + ---------- + adata + Annotated data matrix with ``var['tf_score']`` and ``var['ptf_score']``. + label_top + Number of extreme genes to label in the ranked plot. + figsize + Figure size. + save + Path to save. + show + Whether to display. + ax + Pre-existing axes (used for left panel only if provided). + """ + require_var(adata, TF_SCORE, PTF_SCORE) + + tf = adata.var[TF_SCORE].values + ptf = adata.var[PTF_SCORE].values + genes = adata.var_names.tolist() + + if ax is not None: + # Single-axis mode: just histogram + fig = ax.figure + ax.hist(tf, bins=50, color="steelblue", alpha=0.8, edgecolor="white") + ax.axvline(0.5, color="red", linestyle="--", alpha=0.5, label="TF=PTF") + ax.set_xlabel("Transcriptional Fraction (TF)") + ax.set_ylabel("Number of genes") + ax.set_title("Variance Decomposition") + ax.legend() + else: + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize) + + # Left: TF score histogram + ax1.hist(tf, bins=50, color="steelblue", alpha=0.8, edgecolor="white") + ax1.axvline(0.5, color="red", linestyle="--", alpha=0.5, label="TF=PTF") + median_tf = np.median(tf) + ax1.axvline(median_tf, color="orange", linestyle="-", alpha=0.7, + label=f"Median={median_tf:.3f}") + n_tf_dom = (tf > 0.5).sum() + n_ptf_dom = (tf <= 0.5).sum() + ax1.set_xlabel("Transcriptional Fraction (TF)") + ax1.set_ylabel("Number of genes") + ax1.set_title(f"TF > 0.5: {n_tf_dom} genes | PTF > 0.5: {n_ptf_dom} genes") + ax1.legend(fontsize=8) + + # Right: ranked TF score with labels + sorted_idx = np.argsort(tf) + ranks = np.arange(len(tf)) + ax2.scatter(ranks, tf[sorted_idx], s=3, alpha=0.5, c="steelblue", + rasterized=True) + ax2.axhline(0.5, color="red", linestyle="--", alpha=0.3) + ax2.set_xlabel("Gene rank") + ax2.set_ylabel("TF score") + ax2.set_title("Genes ranked by TF score") + + # Label top TF genes (right side) + if label_top > 0: + top_tf_idx = sorted_idx[-label_top:] + for idx in top_tf_idx: + rank = np.searchsorted(sorted_idx, idx) + ax2.annotate( + genes[idx], + (rank, tf[idx]), + fontsize=6, alpha=0.8, + xytext=(5, 0), textcoords="offset points", + ) + # Label top PTF genes (left side) + top_ptf_idx = sorted_idx[:label_top] + for idx in top_ptf_idx: + rank = np.searchsorted(sorted_idx, idx) + ax2.annotate( + genes[idx], + (rank, tf[idx]), + fontsize=6, alpha=0.8, + xytext=(5, 0), textcoords="offset points", + ) + + fig.suptitle("Variance Decomposition: Transcriptional vs Post-Transcriptional", + fontsize=12, y=1.02) + fig.tight_layout() + + save_or_show(fig, save, show) + return fig if not show else None diff --git a/src/scptr/readwrite.py b/src/scptr/readwrite.py new file mode 100644 index 0000000000000000000000000000000000000000..690712d226bc76890c09d208e055c34480cbc2a0 --- /dev/null +++ b/src/scptr/readwrite.py @@ -0,0 +1,52 @@ +"""Read/write functions for scPTR.""" + +from __future__ import annotations + +import logging +import warnings + +import anndata as ad +from anndata import AnnData + +from ._constants import UNSPLICED, SPLICED + +logger = logging.getLogger("scptr") + + +def _validate_layers(adata: AnnData) -> None: + """Warn if unspliced/spliced layers are missing.""" + missing = [] + if UNSPLICED not in adata.layers: + missing.append(UNSPLICED) + if SPLICED not in adata.layers: + missing.append(SPLICED) + if missing: + warnings.warn( + f"AnnData is missing expected layers: {missing}. " + "scPTR requires 'unspliced' and 'spliced' layers for analysis.", + UserWarning, + stacklevel=3, + ) + + +def read_h5ad(path: str, **kwargs) -> AnnData: + """Read an h5ad file and return an AnnData object. + + Thin wrapper around ``anndata.read_h5ad`` that validates + the presence of unspliced/spliced layers. + """ + adata = ad.read_h5ad(path, **kwargs) + _validate_layers(adata) + return adata + + +def read_loom(path: str, **kwargs) -> AnnData: + """Read a loom file and return an AnnData object. + + Uses scanpy's read_loom which maps loom layers to AnnData layers. + """ + import scanpy as sc + + adata = sc.read_loom(path, **kwargs) + _validate_layers(adata) + return adata