{"id":"W2973165200","doi":"10.1101/gr.234807.118","title":"Gene expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ","year":2019,"lang":"en","type":"article","venue":"Genome Research","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":167,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Douglas Mental Health University Institute","funders":"National Cancer Institute; Stanford Research Computing Center, Stanford University; National Institutes of Health; Ludmer Centre for Neuroinformatics and Mental Health; Fondation Brain Canada; McGill University","keywords":"Laser capture microdissection; Biology; RNA; Computational biology; Gene expression; Gene expression profiling; RNA-Seq; Microdissection; Gene; Transcriptome; Molecular biology; Genetics","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":[0.0002266705,0.0001135993,0.0001438695,0.00006961998,0.00007375508,0.00002959771,0.0001449656,0.0001111238,0.00003127558],"category_scores_gemma":[0.00001109852,0.00009457541,0.00002801149,0.00009389711,0.0001069561,0.000004778357,0.00009533092,0.0001790733,0.00001343789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001322945,"about_ca_system_score_gemma":0.00005991349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000164025,"about_ca_topic_score_gemma":0.00003398113,"domain_scores_codex":[0.9988635,0.0001085278,0.0001403407,0.0003891151,0.0002225329,0.0002759777],"domain_scores_gemma":[0.9994295,0.00002743265,0.00003896516,0.0003030768,0.0001157118,0.00008529228],"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":[0.0004177146,0.0000883305,0.003950098,0.00006411446,0.00002147377,0.0000029917,0.0001592338,0.00004952024,0.9943421,0.000001581733,0.00002621926,0.0008766378],"study_design_scores_gemma":[0.000495234,0.0006236042,0.001860771,0.00003107035,0.00000566274,0.000004740753,0.0001308728,0.00001813166,0.9949017,0.00002668167,0.001784293,0.0001172184],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971552,0.0007105301,0.0008766192,0.00002564331,0.0000720867,0.0003282813,0.00006050312,0.000006397026,0.0007647586],"genre_scores_gemma":[0.9934658,0.0001375522,0.00528031,0.00001028035,0.0001381649,0.00000834946,0.0002115006,0.000026508,0.0007215256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004403691,"threshold_uncertainty_score":0.3856674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267788500163273,"score_gpt":0.2626889859647852,"score_spread":0.2400111009631525,"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."}}