{"id":"W3129861733","doi":"10.1093/nargab/lqab011","title":"Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes","year":2021,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; SickKids Foundation; University Health Network; Canadian Institute for Advanced Research; Vector Institute","funders":"Canadian Institutes of Health Research; Hospital for Sick Children; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science","keywords":"RNA-Seq; RNA; Biology; Cell type; Computational biology; Gene expression; Gene; Population; Cell; Genetics; Transcriptome","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001227001,0.0002229137,0.0002429176,0.00005119816,0.0001499445,0.0001132864,0.0002221361,0.0001696223,0.00002224511],"category_scores_gemma":[0.00001793584,0.0001745594,0.0001058025,0.0001197101,0.000109766,0.000009550622,0.0002018094,0.0001008919,0.000004932998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001791009,"about_ca_system_score_gemma":0.0001454727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001709937,"about_ca_topic_score_gemma":0.00001968836,"domain_scores_codex":[0.9988781,0.00003163853,0.000465545,0.0001977311,0.0001569527,0.0002699835],"domain_scores_gemma":[0.9991347,0.00002063576,0.0001470373,0.0003905957,0.000208648,0.00009836639],"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":[0.00007152117,0.0001055428,0.0005265069,0.0001393949,0.00003504162,0.000001440811,0.0008570526,0.0003809005,0.994944,0.00003166779,0.000320573,0.00258635],"study_design_scores_gemma":[0.0005132701,0.0002053822,0.0001229662,0.00002277848,0.00004846488,0.00001280952,0.001503679,0.002893176,0.9634228,0.00003282783,0.03094761,0.0002741817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885974,0.001474636,0.007716518,0.00007481009,0.0003389402,0.0001908053,0.00007488985,0.000007797022,0.001524207],"genre_scores_gemma":[0.9732398,0.00142142,0.02380553,0.0004890875,0.0002340743,0.000002682481,0.0001215133,0.0000358655,0.0006500068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03152115,"threshold_uncertainty_score":0.7118328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02578047590593834,"score_gpt":0.2153481003316333,"score_spread":0.189567624425695,"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."}}