{"id":"W4224212751","doi":"10.1186/s13059-022-02659-1","title":"One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data","year":2022,"lang":"en","type":"article","venue":"Genome biology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Biology; RNA-Seq; Computational biology; Human genetics; Genome Biology; Cell; RNA; Genomics; Evolutionary biology; Genetics; Transcriptome; Genome; Gene; Gene expression","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000301762,0.0002425356,0.0003097897,0.000098212,0.0002473375,0.00002583424,0.0007643296,0.0002274937,0.0003032376],"category_scores_gemma":[0.00004460936,0.0002567678,0.00006568429,0.0002073587,0.0001217369,0.000003435365,0.001380829,0.0002501975,0.00007583658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006049508,"about_ca_system_score_gemma":0.00007868523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001004144,"about_ca_topic_score_gemma":0.00006372375,"domain_scores_codex":[0.9980843,0.00019533,0.0002929016,0.0008846814,0.00009087328,0.0004518937],"domain_scores_gemma":[0.998659,0.00003927081,0.000100526,0.0009708875,0.00004429588,0.000186021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003292621,0.0002222571,0.0005240678,0.00001563485,0.00005298298,0.000004971429,0.0001468516,0.00004634545,0.9965207,0.00003927508,0.0006430591,0.001454579],"study_design_scores_gemma":[0.00179844,0.004281691,0.0008358823,0.00001141301,0.0001526735,0.00004167879,0.000234257,0.0003699984,0.2924684,0.0006852399,0.6979287,0.0011917],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902775,0.002623658,0.003477767,0.0004769034,0.0002779625,0.0003049609,0.000717369,0.00002965847,0.001814235],"genre_scores_gemma":[0.9881424,0.0001703799,0.004014458,0.001600761,0.0002603024,0.00003179082,0.002711273,0.00004636,0.00302228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7040523,"threshold_uncertainty_score":0.9999884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02986299286436261,"score_gpt":0.2367506273608429,"score_spread":0.2068876344964803,"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."}}