{"id":"W3198493858","doi":"10.3390/cancers13174456","title":"Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler","year":2021,"lang":"en","type":"article","venue":"Cancers","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill Genome Centre; McGill University Health Centre","funders":"National Center for Advancing Translational Sciences","keywords":"Breast cancer; Profiling (computer programming); Computational biology; Transcriptome; Tumor microenvironment; Biology; Human breast; Computer science; Cancer research; Bioinformatics; Cancer; Tumor cells; Gene; Gene expression; Genetics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.02097126,0.001905996,0.00175767,0.005868657,0.001131816,0.004792144,0.004164089,0.002470647,0.01100335],"category_scores_gemma":[0.02617406,0.001943229,0.002031577,0.006818124,0.001833536,0.002971182,0.004319818,0.004058331,0.0219848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001908202,"about_ca_system_score_gemma":0.003874331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002648685,"about_ca_topic_score_gemma":0.004880586,"domain_scores_codex":[0.9820337,0.006414386,0.001732149,0.002183893,0.007094038,0.0005419292],"domain_scores_gemma":[0.9762789,0.006799735,0.001409018,0.008468829,0.006263134,0.0007803429],"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":[0.0006249733,0.0002337615,0.005988579,0.002872161,0.0005181798,0.0007106569,0.001249836,0.007955748,0.1012002,0.05564483,0.2082042,0.6147969],"study_design_scores_gemma":[0.00008283002,0.0001636446,0.003531557,0.001080372,0.0001608459,0.001036164,0.0002405688,0.008848605,0.05610282,0.03311693,0.8954422,0.0001932733],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002108755,0.007095267,0.9338257,0.00468772,0.0007545353,0.0005307107,0.006695799,0.03025796,0.01404356],"genre_scores_gemma":[0.009022254,0.006257114,0.9661745,0.001255299,0.0002498765,0.001140231,0.007711709,0.003484642,0.004704475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02097126,"threshold_uncertainty_score":0.1109079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0727911662810485,"score_gpt":0.3492397881826393,"score_spread":0.2764486219015909,"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."}}