{"id":"W4412599555","doi":"10.1016/j.csbj.2025.07.019","title":"PCLDA: An interpretable cell annotation tool for single-cell RNA-sequencing data based on simple statistical methods","year":2025,"lang":"en","type":"article","venue":"Computational and Structural Biotechnology Journal","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Ocean Networks Canada Society; University of Victoria","funders":"National Research Council Canada; Canada Research Chairs; Alliance de recherche numérique du Canada; Genome British Columbia; Michael Smith Health Research BC","keywords":"Annotation; Simple (philosophy); Computational biology; Computer science; Data mining; RNA; Bioinformatics; Artificial intelligence; Biology; Genetics; Gene","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":[0.004156401,0.002487803,0.001494738,0.004319271,0.0008248126,0.002739919,0.002636612,0.001387477,0.00806309],"category_scores_gemma":[0.01032649,0.001044546,0.002303324,0.001760332,0.0009457631,0.001575004,0.002270552,0.002869448,0.005825832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102633,"about_ca_system_score_gemma":0.002569584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002119805,"about_ca_topic_score_gemma":0.004231282,"domain_scores_codex":[0.9983264,0.000348871,0.0001886368,0.0004841727,0.0005659113,0.00008602072],"domain_scores_gemma":[0.9944137,0.003435283,0.0005697743,0.0006869435,0.0007211361,0.0001732508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001148104,0.0003702735,0.01757675,0.00365515,0.001164123,0.0009200037,0.0009644458,0.05917118,0.1945982,0.01981367,0.1258478,0.5747703],"study_design_scores_gemma":[0.0002418052,0.0002763032,0.007027128,0.0002101862,0.0001775982,0.0007117041,0.0001934997,0.7352979,0.1066756,0.03666657,0.1121709,0.0003509167],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004277661,0.0003374731,0.8816671,0.0001422997,0.0001113239,0.0001178975,0.00565374,0.1070195,0.0006731331],"genre_scores_gemma":[0.05958347,0.0005116476,0.9129726,0.000504304,0.0001087506,0.001225955,0.01455728,0.008509445,0.002026485],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00806309,"threshold_uncertainty_score":0.02697366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02863068430709688,"score_gpt":0.318929575695894,"score_spread":0.2902988913887972,"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."}}