{"id":"W4307395678","doi":"10.1016/j.csbj.2022.10.029","title":"Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets","year":2022,"lang":"en","type":"article","venue":"Computational and Structural Biotechnology Journal","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; University of Toronto; University Health Network; Lawson Health Research Institute; Western University; Ontario Institute for Cancer Research; Children’s Health Research Institute; Hospital for Sick Children","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Institute for Cancer Research; Lawson Health Research Institute; Genome Canada; Canada Research Chairs; Children's Health Research Institute","keywords":"Cluster analysis; Algorithm; Computer science; Computational biology; RNA; RNA-Seq; Transcriptome; Cancer; Cell; Biclustering; Data mining; Biology; Artificial intelligence; Gene expression; Gene; Genetics; Correlation clustering; CURE data clustering algorithm","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":[0.009394746,0.002162411,0.001635566,0.002792392,0.001882665,0.001808059,0.002356311,0.002093731,0.0007350491],"category_scores_gemma":[0.01392314,0.0004332578,0.001969367,0.002694656,0.0008851692,0.001609444,0.001022607,0.001092428,0.0007729657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00242074,"about_ca_system_score_gemma":0.002466274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01925285,"about_ca_topic_score_gemma":0.02267609,"domain_scores_codex":[0.996035,0.001184167,0.0003579679,0.001249749,0.0008892687,0.0002838844],"domain_scores_gemma":[0.9926364,0.003691636,0.0003482511,0.0008996901,0.002105541,0.0003183988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002474322,0.0008613725,0.03650686,0.001277122,0.002366163,0.0002406002,0.000606565,0.7474474,0.0273178,0.002471503,0.0171133,0.161317],"study_design_scores_gemma":[0.00008233025,0.0002684386,0.008627271,0.00003604385,0.00009215969,0.000097786,0.0002637881,0.9740181,0.0134002,0.001301723,0.001760784,0.00005142356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8906835,0.003125609,0.08725116,0.0008964342,0.0003430007,0.0004797569,0.006288148,0.008262231,0.002670137],"genre_scores_gemma":[0.6644503,0.0009160789,0.2965101,0.0005091134,0.00007142174,0.0003875704,0.03445314,0.001045731,0.001656522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01925285,"threshold_uncertainty_score":0.04968476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02765554061134281,"score_gpt":0.2733556053916518,"score_spread":0.245700064780309,"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."}}