{"id":"W4393876998","doi":"10.5281/zenodo.3267922","title":"Statistical modeling, estimation, and remediation of sample index hopping in multiplexed droplet-based single-cell RNA-seq data.","year":2019,"lang":"en","type":"dataset","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":"McGill University","funders":"","keywords":"Index (typography); Sample (material); Multiplexing; Estimation; Computer science; Statistics; Environmental science; Data mining; Biological system; Mathematics; Biology; Engineering; Chromatography; Chemistry; Telecommunications","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":[0.005881423,0.002215404,0.001706423,0.002159211,0.0009118049,0.002170389,0.004078965,0.002445865,0.01217762],"category_scores_gemma":[0.01165307,0.0006975955,0.001618173,0.003085751,0.0006332274,0.001027231,0.001767913,0.002055981,0.016804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001806714,"about_ca_system_score_gemma":0.002207914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01182091,"about_ca_topic_score_gemma":0.02509517,"domain_scores_codex":[0.9976467,0.0006020694,0.000222812,0.001001641,0.0003773328,0.0001494512],"domain_scores_gemma":[0.9947768,0.003134661,0.0003247438,0.001079306,0.0005276847,0.000156731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005961515,0.0002580904,0.02046545,0.005651418,0.000789204,0.0002510788,0.0001767455,0.0257809,0.006541723,0.004442688,0.9088904,0.02615609],"study_design_scores_gemma":[0.001366043,0.0002823746,0.0453451,0.001132579,0.0005804669,0.0007413779,0.0002752585,0.06642206,0.0136767,0.021125,0.8487834,0.0002696095],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002493436,0.0003574442,0.003442063,0.0001641711,0.00005145432,0.00003881499,0.9913024,0.001618954,0.0005313631],"genre_scores_gemma":[0.003303729,0.00009639315,0.005198214,0.0001001939,0.000006170388,0.0002001049,0.9904479,0.0001837389,0.0004636666],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01217762,"threshold_uncertainty_score":0.04073817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05840313639946403,"score_gpt":0.2641756131145631,"score_spread":0.205772476715099,"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."}}