{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006634737,0.000391204,0.0005921939,0.0006757025,0.0005885546,0.000719304,0.0004708655,0.0004547031,0.001512828],"category_scores_gemma":[0.000478959,0.0002528249,0.0004678899,0.0006754993,0.0005255209,0.0002796772,0.0004780541,0.0007825557,0.001477959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002752059,"about_ca_system_score_gemma":0.0005286182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000607799,"about_ca_topic_score_gemma":0.002952031,"domain_scores_codex":[0.9994629,0.00004445277,0.000042532,0.000215006,0.0001919319,0.00004314678],"domain_scores_gemma":[0.9997483,0.00008238288,0.00003697583,0.00006059247,0.0000509873,0.00002075173],"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":[0.00003891112,0.000009638095,0.00050935,0.00004652452,0.000008959197,0.00002276107,0.0000460774,0.0001618264,0.9933215,0.000210812,0.0002463152,0.00537725],"study_design_scores_gemma":[0.00001315922,0.0001479665,0.0105454,0.00001423331,0.00003422929,0.0002315338,0.00008088705,0.007234154,0.9658726,0.0008925745,0.01490251,0.0000307414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4041534,0.001947129,0.5640836,0.0003674458,0.0002860255,0.000652687,0.01837837,0.003379816,0.006751357],"genre_scores_gemma":[0.3236982,0.002068338,0.6453083,0.0008056292,0.00009670644,0.001759639,0.01868397,0.0009115317,0.006667697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001512828,"threshold_uncertainty_score":0.005060911,"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."}}