{"id":"W4399284947","doi":"10.1093/bib/bbae254","title":"A hybrid demultiplexing strategy that improves performance and robustness of cell hashing","year":2024,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Computer science; Multiplexing; Robustness (evolution); Hash function; Cluster analysis; Sample (material); Data mining; Artificial intelligence; Biology; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002810617,0.001066859,0.001144488,0.001639495,0.0007003643,0.001347947,0.001396837,0.001101807,0.002495289],"category_scores_gemma":[0.005845514,0.0006276533,0.0006830813,0.001032284,0.001004523,0.001613394,0.002191444,0.001770306,0.001938388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005845546,"about_ca_system_score_gemma":0.0006325648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006887167,"about_ca_topic_score_gemma":0.001229839,"domain_scores_codex":[0.9978341,0.0003291331,0.0001554074,0.0007568913,0.000736197,0.0001882937],"domain_scores_gemma":[0.9963353,0.001676862,0.0002900437,0.0009030596,0.0006145691,0.0001802279],"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":[0.0003398665,0.0001322007,0.003175335,0.0002670705,0.00009080501,0.0001239371,0.0002767315,0.004877905,0.8330561,0.001585732,0.001965732,0.1541087],"study_design_scores_gemma":[0.00003779666,0.0002131445,0.002905862,0.0000140515,0.00004609309,0.0003986395,0.00006667445,0.07062038,0.916094,0.001117135,0.008384767,0.000101485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1436951,0.001054509,0.8400283,0.0002772168,0.0002253411,0.0002980259,0.0006205256,0.01179925,0.002001778],"genre_scores_gemma":[0.2562595,0.0004872528,0.7351173,0.0005130481,0.00006992141,0.0004301964,0.001693856,0.001123287,0.004305711],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002810617,"threshold_uncertainty_score":0.01486409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01540056437455593,"score_gpt":0.2182856089997536,"score_spread":0.2028850446251976,"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."}}