{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001341136,0.0008675432,0.0007762622,0.0006916667,0.0003921257,0.00125819,0.0009887405,0.001022647,0.0007313187],"category_scores_gemma":[0.00346934,0.0006545734,0.001640234,0.0007170013,0.001134767,0.001023014,0.001363531,0.002202539,0.0003091393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001039845,"about_ca_system_score_gemma":0.0008004824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004040552,"about_ca_topic_score_gemma":0.006438807,"domain_scores_codex":[0.9996587,0.0001545644,0.00001589777,0.00007835514,0.00005728786,0.00003523766],"domain_scores_gemma":[0.9986664,0.0009093441,0.00009946267,0.0001919835,0.00008593718,0.00004686282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005256367,0.00003200907,0.001501638,0.00005217178,0.00006954204,0.00009701725,0.0001345422,0.943823,0.006997757,0.01683801,0.001549165,0.02885257],"study_design_scores_gemma":[0.000001994238,0.000003262266,0.0001409249,0.000001660113,0.000002144308,0.000007336991,0.000005820911,0.9895914,0.0005281949,0.009570441,0.0001426443,0.00000406345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05446226,0.0002501314,0.9429393,0.000508547,0.00002761997,0.00002606039,0.000426679,0.000823386,0.000536032],"genre_scores_gemma":[0.6677775,0.0004651439,0.3255949,0.0004144126,0.00008406245,0.0002709543,0.002480712,0.0003846918,0.002527737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004040552,"threshold_uncertainty_score":0.00803405,"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."}}