{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005677397,0.001329433,0.001894369,0.002167684,0.001295771,0.002581895,0.00556368,0.002439498,0.006063416],"category_scores_gemma":[0.01310015,0.001738889,0.002610127,0.002968236,0.001576304,0.002431027,0.00259973,0.00334809,0.004145907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002923201,"about_ca_system_score_gemma":0.003423582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02401371,"about_ca_topic_score_gemma":0.04074061,"domain_scores_codex":[0.9973394,0.001023944,0.0001434334,0.0007053644,0.0006253955,0.0001624486],"domain_scores_gemma":[0.995533,0.002486711,0.0003140068,0.0005426974,0.0009048525,0.0002186133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004906097,0.0001076146,0.003136741,0.0003784792,0.000303565,0.0001691656,0.0003804009,0.7831138,0.005190328,0.04276578,0.02695969,0.1370039],"study_design_scores_gemma":[0.00002618349,0.0000140196,0.0002220301,0.0000181537,0.00001281736,0.00003544857,0.00001795739,0.9701704,0.0009699006,0.02508256,0.003409619,0.00002084995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001971074,0.0001290596,0.9935777,0.000231097,0.00002936405,0.00007601868,0.001195441,0.002440589,0.0003495936],"genre_scores_gemma":[0.07036674,0.0004121909,0.9143649,0.0005108548,0.0001483832,0.0007958186,0.008234221,0.001587696,0.003579128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02401371,"threshold_uncertainty_score":0.04774785,"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."}}