{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002155881,0.0001465476,0.0001334762,0.00002023165,0.0002963128,0.0001494264,0.0001940891,0.0001149611,0.00002321376],"category_scores_gemma":[0.0001352485,0.0001040546,0.00008686845,0.00009871147,0.0001788838,0.00001413391,0.00004408304,0.0001467654,0.000001462943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001331829,"about_ca_system_score_gemma":0.001775747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00179653,"about_ca_topic_score_gemma":0.005647793,"domain_scores_codex":[0.9987815,0.00003837891,0.0001360924,0.0004416572,0.0001991108,0.0004032375],"domain_scores_gemma":[0.998789,0.00008030653,0.0001192429,0.000260403,0.0006774549,0.00007364109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00564741,0.0001487078,0.009707578,0.000437674,0.00049253,0.000008958346,0.0003504398,0.001746478,0.8539989,0.0001390861,0.007859769,0.1194625],"study_design_scores_gemma":[0.002649246,0.0009427557,0.0004692262,0.0001028856,0.000113965,0.00003431238,0.001339292,0.001908504,0.7439661,0.00004137213,0.2479803,0.0004520602],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9616247,0.001329375,0.02667629,0.00392795,0.0008183547,0.002024734,0.002314994,0.00001987286,0.001263773],"genre_scores_gemma":[0.9929731,0.0001277447,0.0006720115,0.0002257639,0.00183346,0.0008852937,0.0003326486,0.00004935026,0.00290063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2401205,"threshold_uncertainty_score":0.4243224,"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."}}