{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001559008,0.0001495749,0.0001185425,0.00007195668,0.00007210644,0.0001475262,0.0003373725,0.0001048893,0.0001277791],"category_scores_gemma":[0.00006054862,0.0001346913,0.00007006511,0.0003918796,0.00008089663,0.000008858371,0.00009784482,0.00009158123,0.0002361311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002044412,"about_ca_system_score_gemma":0.00008130605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005669095,"about_ca_topic_score_gemma":0.00003707471,"domain_scores_codex":[0.9988126,0.00002491371,0.0001660985,0.0005422015,0.000174704,0.0002794964],"domain_scores_gemma":[0.9994673,0.0000255411,0.00001747638,0.0003036429,0.0000407929,0.0001452757],"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":[0.00001959578,0.00005220004,0.0001139759,0.00001003818,0.000005641505,0.00001150953,0.0001715925,0.00002066068,0.9789682,0.000003487045,0.0081174,0.01250569],"study_design_scores_gemma":[0.00009336109,0.0001428031,0.0002576653,0.00002044021,0.000006105884,0.000003780915,0.00001548144,0.001022814,0.8039023,0.00001455985,0.19435,0.0001706737],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94787,0.0008860156,0.04761803,0.00121843,0.001335007,0.0001125651,0.0001605631,0.00005255771,0.0007468],"genre_scores_gemma":[0.9901506,0.00003686316,0.003363045,0.002542899,0.0005513089,0.000005526029,0.00008046273,0.00002547776,0.003243819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1862326,"threshold_uncertainty_score":0.5492554,"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."}}