{"id":"W2809008801","doi":"10.1038/s41467-018-04696-6","title":"Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data","year":2018,"lang":"en","type":"article","venue":"Nature Communications","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Medical Research Council; Wellcome Trust; Wellcome","keywords":"Covariate; Computer science; Latent variable; ENCODE; Regression; Homogeneous; Expression (computer science); Regression analysis; Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics; Biology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00006962204,0.0001086016,0.00009011418,0.0000169725,0.0002241038,0.00004981893,0.0007554929,0.0002033659,0.000006017567],"category_scores_gemma":[0.00003855081,0.00009122099,0.00001452725,0.00006612259,0.0002042302,0.00001305208,0.0004187407,0.0002249305,0.000001598287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006412754,"about_ca_system_score_gemma":0.00004277528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001667937,"about_ca_topic_score_gemma":0.001436479,"domain_scores_codex":[0.9994107,0.00004808031,0.0001067449,0.0002571394,0.00007216681,0.0001052085],"domain_scores_gemma":[0.9980659,0.00003906004,0.00005472139,0.001712398,0.00008282808,0.0000450966],"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":[0.00007720722,0.0001103605,0.01122621,0.000004904009,0.00002161885,2.841128e-7,0.0001542488,0.000002711812,0.9866561,0.00003493687,0.001398802,0.0003126332],"study_design_scores_gemma":[0.0008213834,0.0003290445,0.004816624,0.00005299261,0.00005058872,0.000005244066,0.0001525053,0.0003276142,0.81179,0.0001605637,0.1811862,0.0003072483],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880992,0.006899894,0.002415427,0.000380396,0.0001779495,0.000126694,0.0003326704,0.00003097997,0.001536743],"genre_scores_gemma":[0.9595338,0.0003117243,0.03811137,0.0001581468,0.0001757759,0.000004198967,0.001628832,0.00001785929,0.00005827338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1797874,"threshold_uncertainty_score":0.3719884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02681571666254846,"score_gpt":0.2654872093028629,"score_spread":0.2386714926403145,"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."}}