{"id":"W4309012390","doi":"10.1101/2022.11.11.516066","title":"CelltypeR: A flow cytometry pipeline to annotate, characterize and isolate single cells from brain organoids","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"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; Fondation Brain Canada; McGill University","keywords":"Cell type; Organoid; Cell sorting; Flow cytometry; Cell; Biology; Computational biology; Induced pluripotent stem cell; Substantia nigra; Neuroscience; Computer science; Cell biology; Embryonic stem cell; Dopamine; Molecular biology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003712371,0.0007005414,0.000636128,0.0002299426,0.0002093553,0.0002402714,0.0006440948,0.0006464521,0.0001880343],"category_scores_gemma":[0.0001325293,0.0008128798,0.0001785217,0.0004030568,0.00009764122,0.00001202701,0.001041441,0.0006809504,0.00003596034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001130162,"about_ca_system_score_gemma":0.0002894393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001333673,"about_ca_topic_score_gemma":0.00001117489,"domain_scores_codex":[0.9967815,0.000177344,0.0005766567,0.001541846,0.0003053813,0.0006172856],"domain_scores_gemma":[0.9977805,0.00004060969,0.0002493331,0.001264594,0.000238146,0.0004268063],"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":[0.0001933329,0.0002421431,0.001954712,0.0001202512,0.0001291338,0.00003921484,0.00002542743,0.00006410469,0.9954684,0.000001740653,0.001748526,0.00001303864],"study_design_scores_gemma":[0.0007012514,0.0002861071,0.008543694,0.00007407942,0.00008420467,3.930168e-8,0.000004427689,0.0002939451,0.9083146,5.967062e-7,0.08070392,0.0009930768],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876612,0.001225775,0.00562106,0.0004440445,0.001777544,0.0006075767,0.002514363,0.0001371037,0.00001129691],"genre_scores_gemma":[0.9885661,0.0004113673,0.007196073,0.002391529,0.0009840865,0.0001004625,0.00002758269,0.0002392338,0.00008355455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08715372,"threshold_uncertainty_score":0.9994322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103001069287625,"score_gpt":0.2017711105007333,"score_spread":0.1907410998078571,"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."}}