{"id":"W4417183891","doi":"10.1093/bib/bbaf657","title":"SpaTM: topic models for inferring spatially informed transcriptional programs","year":2025,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Benchmarking; Spatial analysis; Cluster analysis; Crime analysis; Topic model","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":[0.000172272,0.0001983215,0.0002067735,0.0001179967,0.00009609519,0.00008176325,0.0002420425,0.0002280985,0.000003522452],"category_scores_gemma":[0.00007709888,0.0002024748,0.0001353246,0.0001552203,0.00007518067,0.00003517934,0.00004738642,0.0001172764,0.000001689216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000432276,"about_ca_system_score_gemma":0.0002726179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001135479,"about_ca_topic_score_gemma":0.0004770832,"domain_scores_codex":[0.9987651,0.000009311022,0.0005629949,0.0001772944,0.0001334117,0.0003519384],"domain_scores_gemma":[0.9995043,0.00002311002,0.0000885862,0.0002250177,0.0001049974,0.00005403912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002202455,0.001725878,0.04361456,0.006636799,0.0007374012,0.000007328224,0.008335307,0.03398716,0.1012271,0.1276561,0.009607436,0.6642625],"study_design_scores_gemma":[0.01345467,0.001277745,0.007800811,0.001078519,0.0001448536,0.00002778575,0.0005712678,0.4283588,0.05962285,0.01717677,0.4685538,0.001932082],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3716668,0.0003427291,0.6121451,0.000897021,0.000626924,0.001425597,0.0000490018,0.00007641719,0.0127704],"genre_scores_gemma":[0.9284046,0.0002298476,0.0661616,0.003431425,0.0001258523,0.0001895429,0.0005740123,0.00002799155,0.0008551367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6623304,"threshold_uncertainty_score":0.8256685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02433102918432666,"score_gpt":0.2603119121612157,"score_spread":0.2359808829768891,"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."}}