{"id":"W3080404755","doi":"10.1101/2020.08.24.265298","title":"Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Vector Institute; Canadian Institute for Advanced Research; University Health Network; SickKids Foundation; University of Toronto","funders":"Hospital for Sick Children; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome Canada","keywords":"RNA-Seq; RNA; Biology; Computational biology; Cell type; Gene expression; Gene; Cell; Transcriptome; Genetics","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":[0.004456663,0.001654476,0.001711698,0.002273394,0.001036063,0.002434948,0.001711363,0.001306138,0.00758861],"category_scores_gemma":[0.008279406,0.001055904,0.002037376,0.001411847,0.0009810649,0.0009857138,0.002081961,0.002781348,0.00549068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006489652,"about_ca_system_score_gemma":0.0014354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001807188,"about_ca_topic_score_gemma":0.002964285,"domain_scores_codex":[0.9979255,0.0003126558,0.00009145764,0.0009770752,0.0005792386,0.0001140743],"domain_scores_gemma":[0.9962971,0.001981937,0.0005031714,0.0006330883,0.0004360027,0.0001486787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00136635,0.0002135322,0.03218669,0.003602708,0.001806177,0.001178004,0.001293227,0.04641478,0.5564148,0.009467646,0.06809715,0.277959],"study_design_scores_gemma":[0.0002285369,0.0003377987,0.02302062,0.0002393189,0.00049658,0.001284514,0.0002592906,0.4312035,0.4574019,0.01896156,0.066153,0.0004134774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04676575,0.001058555,0.8651129,0.0003738203,0.0002672757,0.0002418465,0.01360726,0.07035576,0.002216913],"genre_scores_gemma":[0.1707132,0.0006744843,0.792472,0.0009329647,0.0001152325,0.0009277282,0.01617876,0.015156,0.002829592],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00758861,"threshold_uncertainty_score":0.02538639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03192181322361316,"score_gpt":0.2177086929397148,"score_spread":0.1857868797161017,"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."}}