{"id":"W2949272108","doi":"10.1038/s41467-018-04368-5","title":"Interpretable dimensionality reduction of single cell transcriptome data with deep generative models","year":2018,"lang":"en","type":"article","venue":"Nature Communications","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":382,"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; Computational biology; Single cell sequencing; Artificial intelligence; Data mining; Pattern recognition (psychology); Generative grammar; Biology; Gene; Genetics; Phenotype","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":[0.001193337,0.0007602789,0.0007182895,0.0006341143,0.0003679954,0.001057269,0.0008862604,0.0008618519,0.0006378237],"category_scores_gemma":[0.003218713,0.0005872214,0.001511773,0.000643788,0.0009939787,0.0009143741,0.00124147,0.001872635,0.0002520687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009121803,"about_ca_system_score_gemma":0.0007350126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003948194,"about_ca_topic_score_gemma":0.006044519,"domain_scores_codex":[0.9996939,0.0001394828,0.000014776,0.0000642493,0.00005388501,0.00003379371],"domain_scores_gemma":[0.9987709,0.0008444022,0.00009510906,0.0001692801,0.00007725089,0.00004300644],"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":[0.00004521025,0.00002820131,0.001267332,0.00004095631,0.00005676268,0.00008753318,0.0001234187,0.947594,0.006277855,0.01465701,0.001041072,0.02878072],"study_design_scores_gemma":[0.000001633255,0.000003487918,0.0001253692,0.00000136206,0.000001918768,0.000006651785,0.000005236805,0.9916433,0.0004269293,0.007670275,0.0001102076,0.000003622865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0639969,0.0002386035,0.933773,0.0004297396,0.00002555935,0.00002547948,0.0003073569,0.0006740248,0.0005293807],"genre_scores_gemma":[0.716715,0.0004301247,0.2780318,0.0003306164,0.00007289067,0.0002321434,0.001800213,0.0002646801,0.00212252],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003948194,"threshold_uncertainty_score":0.007850409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04044070147336464,"score_gpt":0.2841181105598681,"score_spread":0.2436774090865035,"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."}}