{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001907471,0.0008110383,0.0008334059,0.000214001,0.0001292677,0.0001916862,0.0008777105,0.0008247433,0.00005845955],"category_scores_gemma":[0.00007847654,0.0008955647,0.0003315017,0.0003249687,0.000171183,0.00001261858,0.0007782095,0.0004803278,0.00002349952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008462137,"about_ca_system_score_gemma":0.0005725917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008303051,"about_ca_topic_score_gemma":0.000004763427,"domain_scores_codex":[0.9966214,0.0001506041,0.0008269532,0.001327781,0.0004357595,0.0006375167],"domain_scores_gemma":[0.9971724,0.00002293089,0.0004479424,0.001319429,0.0006454855,0.0003917896],"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":[0.0002491921,0.0003867813,0.003017747,0.0007298499,0.0001365134,0.00001618244,0.00003258979,0.0005976132,0.9944409,0.00002251921,0.0003665063,0.000003639841],"study_design_scores_gemma":[0.0006509324,0.000278203,0.001749924,0.0002053386,0.0001510472,1.984009e-8,0.00001277636,0.0002245959,0.9877383,0.000001720313,0.008019419,0.000967705],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822872,0.00198045,0.01277431,0.0000740712,0.001678431,0.0006851227,0.0003624882,0.00009916681,0.0000587272],"genre_scores_gemma":[0.984269,0.0003988664,0.01367681,0.0002681621,0.001073341,0.00003271441,0.000006677288,0.0002399919,0.00003441652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007652913,"threshold_uncertainty_score":0.9993495,"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."}}