{"id":"W2038426170","doi":"10.1002/cyto.a.22271","title":"Normalization of mass cytometry data with bead standards","year":2013,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":803,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; U.S. Public Health Service; National Cancer Institute; National Institutes of Health; National Eye Institute; University of Toronto; Damon Runyon Cancer Research Foundation","keywords":"Mass cytometry; Normalization (sociology); Cytometry; Reproducibility; Quality assurance; Mass spectrometry; Flow cytometry; Computer science; Biomedical engineering; Materials science; Chemistry; Analytical Chemistry (journal); Biological system; Chromatography; Medicine; Biology; Pathology; External quality assessment; Molecular biology","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":[0.01616191,0.00340053,0.003761556,0.009978929,0.002993646,0.005679599,0.004915555,0.002716889,0.01958437],"category_scores_gemma":[0.03943957,0.002344342,0.001992363,0.01376627,0.002243075,0.002600642,0.003833315,0.005841852,0.01817636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002915825,"about_ca_system_score_gemma":0.003136796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001962596,"about_ca_topic_score_gemma":0.003049122,"domain_scores_codex":[0.9650097,0.004924206,0.003843734,0.009331365,0.01516929,0.001721633],"domain_scores_gemma":[0.9823762,0.002892844,0.001037959,0.005282338,0.008138167,0.000272409],"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":[0.002278313,0.0010127,0.01564613,0.001909282,0.0005556362,0.0003571587,0.001542039,0.006579302,0.7588194,0.01143562,0.01970183,0.1801627],"study_design_scores_gemma":[0.0001760762,0.0005715138,0.02307213,0.0003321267,0.0001829088,0.00053338,0.0004302492,0.04268027,0.8022045,0.01315257,0.1163512,0.0003129995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04622513,0.001716858,0.9078909,0.0005938736,0.002229836,0.003117601,0.01148663,0.01612141,0.01061775],"genre_scores_gemma":[0.07683567,0.002194873,0.8607024,0.001147429,0.0003923939,0.01321932,0.0250267,0.008905567,0.01157571],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01958437,"threshold_uncertainty_score":0.08547336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02018228295733,"score_gpt":0.2599773743387014,"score_spread":0.2397950913813715,"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."}}