{"id":"W2988567175","doi":"10.1101/837765","title":"Identifying differential cell populations in flow cytometry data accounting for marker frequency","year":2019,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; Simon Fraser University","funders":"","keywords":"Population; Phenotype; Cell; Flow cytometry; Cell type; Biology; Computational biology; Identification (biology); Genetics; Gene; Ecology; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.005032576,0.0007502857,0.001040589,0.003336162,0.0005386511,0.001656921,0.0009615381,0.0009967549,0.002455529],"category_scores_gemma":[0.01572654,0.0002487667,0.0007638151,0.002502285,0.001456128,0.001277617,0.001159374,0.001091869,0.000744562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005218974,"about_ca_system_score_gemma":0.0006868463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006071016,"about_ca_topic_score_gemma":0.0006003455,"domain_scores_codex":[0.997519,0.0005785843,0.0002091584,0.0006880422,0.0007804042,0.0002247437],"domain_scores_gemma":[0.9883952,0.007497459,0.001573726,0.001435139,0.0007173901,0.0003810217],"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":[0.001911198,0.0004582639,0.2203005,0.0005848481,0.0005161635,0.0007388302,0.0005523328,0.08328965,0.4067452,0.01925425,0.003206224,0.2624425],"study_design_scores_gemma":[0.0001019995,0.0007174289,0.1029518,0.00004242612,0.0001523557,0.001494733,0.0003351831,0.6695044,0.1629219,0.05582263,0.00580028,0.0001548633],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4637448,0.0002933059,0.5292789,0.0002264639,0.00006815773,0.00007427068,0.001934445,0.002440001,0.001939598],"genre_scores_gemma":[0.8434528,0.0001401121,0.1514408,0.0001252312,0.00006142296,0.0002177521,0.00336781,0.0003635621,0.0008306113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005032576,"threshold_uncertainty_score":0.02661508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.103628460916795,"score_gpt":0.2991727224892582,"score_spread":0.1955442615724632,"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."}}