{"id":"W4407858654","doi":"10.1038/s41467-025-57157-2","title":"Interpretable single-cell factor decomposition using sciRED","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cisco Systems (Canada); Princess Margaret Cancer Centre; Lunenfeld-Tanenbaum Research Institute; University of Toronto; University Health Network; Canadian Institute for Advanced Research; Western University","funders":"Canadian Institutes of Health Research; Canada First Research Excellence Fund; National Institute of General Medical Sciences; Government of Canada; Foundation for the National Institutes of Health","keywords":"Interpretability; Computational biology; Confounding; Computer science; Covariate; Artificial intelligence; Biology; Data mining; Pattern recognition (psychology); Machine learning; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006067815,0.0001124564,0.0001017308,0.00006964391,0.0002413894,0.00004869466,0.0006341765,0.0003025598,0.000009547599],"category_scores_gemma":[0.00004404583,0.0001189193,0.00008391919,0.0001728448,0.00008583665,0.000006510106,0.0002085766,0.0003221679,0.000002797006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004200268,"about_ca_system_score_gemma":0.00007904401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002459532,"about_ca_topic_score_gemma":0.0001775917,"domain_scores_codex":[0.9993711,0.00007344899,0.0001673326,0.00018902,0.00005888196,0.0001402609],"domain_scores_gemma":[0.9987441,0.00003154891,0.00005181525,0.001015153,0.0001214724,0.00003595156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002299009,0.0001972078,0.002047373,0.000009075051,0.00002757167,1.179045e-7,0.00003407263,0.00002831233,0.9954922,0.0002555039,0.001049425,0.0008362001],"study_design_scores_gemma":[0.0007720918,0.00009972988,0.001606079,0.00009109943,0.00007550382,0.000005349522,0.00007164945,0.004402688,0.8152477,0.0001873689,0.1771084,0.0003323864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9383575,0.01377543,0.02338314,0.00103344,0.0007509222,0.0003473758,0.00009511457,0.00006626502,0.02219076],"genre_scores_gemma":[0.9867076,0.0002010905,0.01162119,0.0006677088,0.0000384294,0.000008250692,0.0002736979,0.00001325734,0.0004687172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1802445,"threshold_uncertainty_score":0.4849388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02293571926087924,"score_gpt":0.3089810719710758,"score_spread":0.2860453527101965,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}