{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003997192,0.002055055,0.001973313,0.001944963,0.001112438,0.002189891,0.001978203,0.0015563,0.006596443],"category_scores_gemma":[0.007493102,0.001130046,0.002152596,0.001476003,0.001016738,0.001247566,0.002132165,0.002827195,0.006164935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006941632,"about_ca_system_score_gemma":0.001722106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002099591,"about_ca_topic_score_gemma":0.0039325,"domain_scores_codex":[0.9983006,0.0002731935,0.00007871063,0.0007865889,0.0004615949,0.00009929282],"domain_scores_gemma":[0.997244,0.001508516,0.0003518268,0.0004888397,0.0002872935,0.0001195928],"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":[0.001493981,0.0002498979,0.02600822,0.005122888,0.002236638,0.001269787,0.001476722,0.05911351,0.4531073,0.01438846,0.11519,0.3203425],"study_design_scores_gemma":[0.0002934826,0.0003447559,0.01403776,0.0002208166,0.0005047353,0.001351089,0.0002115947,0.4896124,0.3625746,0.02779996,0.1026127,0.000436126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.02534636,0.0009202833,0.8754715,0.0002904469,0.0002374487,0.0002338713,0.01316394,0.08225913,0.002077056],"genre_scores_gemma":[0.1039915,0.0008977132,0.8505668,0.0008600961,0.0001052495,0.001212886,0.0234054,0.01603645,0.002923799],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.006596443,"threshold_uncertainty_score":0.02206731,"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."}}