{"id":"W4382632303","doi":"10.1093/bioinformatics/btad242","title":"SpatialSort: a Bayesian model for clustering and cell population annotation of spatial proteomics data","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Terry Fox Research Institute; BC Cancer Agency","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Cancer Research UK; BC Cancer Foundation; Terry Fox Research Institute; Michael Smith Health Research BC; V Foundation for Cancer Research","keywords":"Cluster analysis; Computer science; Annotation; Bayesian probability; Data mining; Spatial analysis; Source code; Profiling (computer programming); Population; Context (archaeology); Consensus clustering; Artificial intelligence; Machine learning; Fuzzy clustering; CURE data clustering algorithm; Geography; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.000209209,0.0001079085,0.0001296759,0.00005975136,0.00006451215,0.00002415196,0.0001556581,0.0001145239,6.361231e-7],"category_scores_gemma":[0.00005700518,0.0001087347,0.00003264732,0.00006108061,0.00002651074,0.00001951269,0.0001146506,0.00003371295,9.038133e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006113255,"about_ca_system_score_gemma":0.00004087868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007586827,"about_ca_topic_score_gemma":0.000195211,"domain_scores_codex":[0.999248,0.000008959604,0.0003504711,0.0001470662,0.00009528722,0.0001502307],"domain_scores_gemma":[0.9994178,0.00001130536,0.000155071,0.0003177993,0.00005654355,0.00004142159],"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":[0.0008363657,0.0001913023,0.01804122,0.003623274,0.0001120674,7.644374e-7,0.00308638,0.0875159,0.8052537,0.0001261206,0.001871702,0.0793412],"study_design_scores_gemma":[0.0005817936,0.0001133715,0.001112363,0.00001714092,0.00002082433,0.000001086875,0.00006028619,0.9771473,0.02060467,0.00007939283,0.0001345738,0.0001272274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3332299,0.00001571616,0.6659092,0.0000242648,0.00007929726,0.0003903797,0.0002795017,0.00001635206,0.0000553969],"genre_scores_gemma":[0.9139072,0.00007518419,0.0826835,0.00003942168,0.00007808713,0.00001596531,0.003112159,0.00001845959,0.00007003258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8896314,"threshold_uncertainty_score":0.4434073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03764032023504185,"score_gpt":0.2650842334166531,"score_spread":0.2274439131816113,"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."}}