{"id":"W3194189918","doi":"10.3389/fimmu.2021.727626","title":"Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data","year":2021,"lang":"en","type":"article","venue":"Frontiers in Immunology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":158,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Human Genome Research Institute; Juno Therapeutics; U.S. Food and Drug Administration; National Institutes of Health; Cancer Research UK; Parker Institute for Cancer Immunotherapy; National Institute of Allergy and Infectious Diseases; Celgene; U.S. Department of Defense; Silicon Valley Community Foundation; Defence Science and Technology Laboratory; Hamilton Health Sciences Foundation; Bill and Melinda Gates Foundation; National Heart, Lung, and Blood Institute; Pfizer; Cancer Research Institute; National Cancer Institute; Kenneth Rainin Foundation","keywords":"Cluster analysis; Computer science; Normalization (sociology); Artificial intelligence; Segmentation; Multiplexing; Cell type; Pattern recognition (psychology); Identification (biology); Biclustering; Cell; Fuzzy clustering; 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":[0.006528819,0.0009154284,0.001156835,0.00208572,0.001133551,0.002497483,0.001818316,0.001253178,0.001392484],"category_scores_gemma":[0.01152988,0.0009475008,0.0006954905,0.001753732,0.001388503,0.001521666,0.00225661,0.001985532,0.001382732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207038,"about_ca_system_score_gemma":0.001117977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001233915,"about_ca_topic_score_gemma":0.002577449,"domain_scores_codex":[0.9960228,0.0008776563,0.0002859759,0.001233391,0.001240146,0.0003398457],"domain_scores_gemma":[0.9926853,0.00232317,0.001046279,0.002209384,0.001501799,0.0002341113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003903514,0.00009122432,0.009775574,0.0004652273,0.00008257227,0.0002359923,0.0008150325,0.006374505,0.8833894,0.007403235,0.001934329,0.0890426],"study_design_scores_gemma":[0.00002178674,0.00009167645,0.01453366,0.00009590545,0.00006864769,0.0003708678,0.0002573799,0.07663625,0.8849038,0.00770601,0.01522808,0.00008575962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1185372,0.0005100502,0.8727072,0.0002980889,0.0001327322,0.0002935275,0.001264171,0.004854755,0.0014024],"genre_scores_gemma":[0.1785387,0.0003330623,0.816178,0.0002290739,0.00004209842,0.0007805895,0.002033477,0.0009527959,0.0009122327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006528819,"threshold_uncertainty_score":0.03452814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02550901066348076,"score_gpt":0.269444644960008,"score_spread":0.2439356342965273,"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."}}