{"id":"W4288051411","doi":"10.1093/bioadv/vbac049","title":"More accurate estimation of cell composition in bulk expression through robust integration of single-cell information","year":2022,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Health Canada","funders":"","keywords":"Covariance; Collinearity; Univariate; Computer science; Expression (computer science); RNA-Seq; Data mining; Analysis of covariance; Computational biology; Algorithm; Biological system; Gene expression; Mathematics; Gene; Multivariate statistics; Transcriptome; Biology; Statistics; Machine learning; Genetics","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":[0.0001458029,0.0001292306,0.0001682316,0.0001022346,0.00007704356,0.00001682711,0.0001624365,0.00006832073,0.00001072195],"category_scores_gemma":[0.00002180981,0.0001259937,0.00005959221,0.0001825758,0.00005383999,0.0001579441,0.00007000003,0.00009281695,0.000001125788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003407691,"about_ca_system_score_gemma":0.00004226785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001708468,"about_ca_topic_score_gemma":0.000005184257,"domain_scores_codex":[0.9988602,0.0000357738,0.0006567069,0.00008850107,0.0002325239,0.0001263026],"domain_scores_gemma":[0.999148,0.00001789542,0.0005360816,0.0001776681,0.00009903729,0.00002134466],"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":[0.0001607007,0.0001926833,0.0001335751,0.0002704812,0.000002968317,1.228083e-7,0.002050483,0.2487143,0.7445386,0.00003426618,0.00005701091,0.003844785],"study_design_scores_gemma":[0.0007838474,0.000412068,0.00008565765,0.00005072715,0.000008687869,0.000002199133,0.001929108,0.07775187,0.917834,0.0000490639,0.0009527139,0.0001400917],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8250114,0.0002387493,0.1727204,0.00002079801,0.0001802623,0.0002852759,0.00009308889,0.00001015409,0.001439848],"genre_scores_gemma":[0.9528873,0.0001504408,0.04573569,0.00006092971,0.00001273948,0.00002205977,0.001110618,0.000007599227,0.00001263459],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1732953,"threshold_uncertainty_score":0.5137872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643830154938968,"score_gpt":0.2404196319931348,"score_spread":0.2239813304437452,"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."}}