{"id":"W2751118311","doi":"10.1101/178624","title":"Interpretable dimensionality reduction of single cell transcriptome data with deep generative models","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"Natural Sciences and Engineering Research Council of Canada; Terry Fox Research Institute; Canada Research Chairs; Michael Smith Health Research BC; BC Cancer Foundation; Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Genome Canada","keywords":"Dimensionality reduction; Computer science; Cluster analysis; Probabilistic logic; Generative model; Embedding; Artificial intelligence; Data mining; Computational biology; Pattern recognition (psychology); Generative grammar; Biology","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":[0.0004131958,0.0005301277,0.0005810272,0.00009234017,0.0002016289,0.0001322467,0.001189695,0.0005787767,0.00001188367],"category_scores_gemma":[0.00004188366,0.0005143381,0.0001397962,0.00009152097,0.0003066297,0.00004445024,0.0005336825,0.0004125295,0.000002152049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006587448,"about_ca_system_score_gemma":0.0005155384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001506413,"about_ca_topic_score_gemma":0.00001902857,"domain_scores_codex":[0.9973006,0.0001296582,0.0004998791,0.001342239,0.0003287956,0.0003988535],"domain_scores_gemma":[0.9956685,0.00000963382,0.0005351499,0.003052196,0.0005576148,0.0001768631],"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":[0.0002666884,0.000398817,0.0003075039,0.0002648633,0.0002278275,0.000006302413,0.00001933071,0.001693705,0.996591,0.00004559708,0.0001750511,0.000003263551],"study_design_scores_gemma":[0.0007014364,0.000254281,0.0005099055,0.0002337696,0.0002111431,7.251207e-8,0.000005725035,0.00394626,0.9924674,0.000007767547,0.001018883,0.0006433606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9382744,0.002725157,0.05630653,0.00007049079,0.0009830463,0.0005727651,0.0009312315,0.00005668192,0.00007965576],"genre_scores_gemma":[0.9844828,0.0003619794,0.0145214,0.00004528205,0.0003853283,0.00004243046,0.00002531978,0.0001118298,0.00002360961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04620838,"threshold_uncertainty_score":0.9997308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03130409407956432,"score_gpt":0.2313930432621416,"score_spread":0.2000889491825772,"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."}}