{"id":"W4401155275","doi":"10.1016/j.isci.2024.110613","title":"CelltypeR: A flow cytometry pipeline to characterize single cells from brain organoids","year":2024,"lang":"en","type":"article","venue":"iScience","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Québec Consortium for Drug Discovery; Canada Research Chairs; Fondation Brain Canada; Novartis Pharmaceuticals Corporation","keywords":"Organoid; Cell type; Cell sorting; Induced pluripotent stem cell; Flow cytometry; Substantia nigra; Computational biology; Cell; Biology; Single-cell analysis; Workflow; Neuroscience; Cellular differentiation; Midbrain; Cell biology; Computer science; Dopamine; Embryonic stem cell; Molecular biology; Gene; Genetics; Central nervous system","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":[0.002226391,0.0019279,0.001338873,0.00215625,0.001159207,0.002453029,0.001870835,0.001090883,0.009481324],"category_scores_gemma":[0.003046692,0.0009721873,0.001606991,0.001166722,0.0005983103,0.001165591,0.001901301,0.00231382,0.006701416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001419102,"about_ca_system_score_gemma":0.002787211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003735681,"about_ca_topic_score_gemma":0.006096733,"domain_scores_codex":[0.9990717,0.00007489668,0.00008786968,0.0003978548,0.000258042,0.0001095326],"domain_scores_gemma":[0.9989441,0.0003416155,0.00009508021,0.0002514443,0.0002522805,0.0001155349],"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":[0.0009362067,0.0002184091,0.008546835,0.001139018,0.0004058539,0.0003883613,0.0007260529,0.02001672,0.7410361,0.008112036,0.0450138,0.1734607],"study_design_scores_gemma":[0.0002360626,0.0002942389,0.01749288,0.000110593,0.0001553769,0.0006468485,0.0002970955,0.296709,0.5508665,0.02151276,0.1113124,0.0003661597],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0131392,0.0001536053,0.8968431,0.0002041459,0.0000887252,0.0003967624,0.01604899,0.07172009,0.001405348],"genre_scores_gemma":[0.070738,0.0003994092,0.8686364,0.0005163157,0.0000579045,0.003482218,0.04014422,0.01280216,0.00322333],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009481324,"threshold_uncertainty_score":0.03171813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611951976829965,"score_gpt":0.2352957441575178,"score_spread":0.2191762243892182,"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."}}