{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003309567,0.0006217589,0.0008539305,0.0007807301,0.0003280512,0.001291804,0.001133789,0.001006434,0.001879617],"category_scores_gemma":[0.01016858,0.0005830086,0.001358484,0.001450145,0.001167862,0.002231026,0.001375228,0.001785605,0.0005322131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009209645,"about_ca_system_score_gemma":0.00113105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003836088,"about_ca_topic_score_gemma":0.005546273,"domain_scores_codex":[0.9992681,0.0002893847,0.00003387949,0.0002623059,0.00008299872,0.00006336172],"domain_scores_gemma":[0.9943088,0.003731566,0.0007861237,0.0008085388,0.0002116635,0.0001533521],"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":[0.0006053149,0.0001835042,0.05302232,0.0003401302,0.0003021422,0.0003785582,0.0006464569,0.6687283,0.02331978,0.137967,0.002331718,0.1121748],"study_design_scores_gemma":[0.00001261199,0.00004019697,0.0055229,0.00001477571,0.00001822587,0.00005483303,0.00003828123,0.932592,0.001342816,0.058566,0.001769097,0.00002836654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08760308,0.0002645819,0.9100558,0.0003420578,0.00002577704,0.00002986717,0.0008339977,0.0003833873,0.0004614148],"genre_scores_gemma":[0.7968452,0.0008230705,0.1944371,0.0001984249,0.00007859034,0.0002623708,0.003881288,0.0002503386,0.003223591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003836088,"threshold_uncertainty_score":0.01750284,"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."}}