{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005564281,0.000240111,0.0002316891,0.0002563857,0.0006331353,0.0007313566,0.001801596,0.0002973349,0.0008737749],"category_scores_gemma":[0.0003680418,0.0002794788,0.00009413553,0.0001922311,0.00005983,0.00002716714,0.003498534,0.0004355549,0.0002402809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008831974,"about_ca_system_score_gemma":0.00001603405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007196653,"about_ca_topic_score_gemma":0.000008530705,"domain_scores_codex":[0.997871,0.0001863811,0.0004027016,0.0008995358,0.0002488962,0.0003915108],"domain_scores_gemma":[0.9982408,0.00001250702,0.0001803791,0.001186326,0.0002949615,0.00008505406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004758124,0.0008705445,0.002586731,0.002307807,0.0002590152,0.00001225821,0.0007368917,0.002219264,0.8450845,0.0002373535,0.1018337,0.0433761],"study_design_scores_gemma":[0.006417933,0.0006328663,0.03105029,0.0006969913,0.0003322165,0.0000662072,0.0005072233,0.06508024,0.01841296,0.001429031,0.8725166,0.002857492],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6698805,0.0007958952,0.3030601,0.0002618424,0.002014803,0.00238857,0.008306506,0.0002805287,0.01301122],"genre_scores_gemma":[0.9212841,0.000147646,0.003423652,0.00005743432,0.000449859,2.176232e-7,0.07314285,0.001084559,0.0004097023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8266715,"threshold_uncertainty_score":0.9999657,"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."}}