{"id":"W3087845087","doi":"10.1101/2020.09.21.305516","title":"Copy-scAT: Deconvoluting single-cell chromatin accessibility of genetic subclones in cancer","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network; Alberta Children's Hospital; Ontario Institute for Cancer Research; Canada's Michael Smith Genome Sciences Centre; University of British Columbia; University of Calgary","funders":"Canadian Institutes of Health Research; Health Canada; Azrieli Foundation; Alberta Innovates; University of Calgary; Government of Canada; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Alberta Health Services","keywords":"Epigenomics; Chromatin; Epigenetics; Biology; Phenotype; Genetics; Copy-number variation; Cancer; Cancer research; Computational biology; Cell; DNA methylation; Gene; Genome; Gene expression","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":[0.002971366,0.001393684,0.001437631,0.001500765,0.0005627617,0.00157477,0.001617988,0.001132742,0.006216795],"category_scores_gemma":[0.004613155,0.00129545,0.001587394,0.001165678,0.0007993516,0.0009639803,0.002077348,0.00195567,0.002723041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006822951,"about_ca_system_score_gemma":0.0009962698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002374672,"about_ca_topic_score_gemma":0.004527972,"domain_scores_codex":[0.9989076,0.0002082148,0.0000618347,0.0003136771,0.0004287594,0.00007997228],"domain_scores_gemma":[0.9976827,0.0013217,0.0002283324,0.0005683965,0.0001084866,0.00009033946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002783971,0.0002397362,0.03961718,0.002526234,0.001906869,0.001435501,0.001776397,0.09419964,0.5123612,0.0203879,0.05922208,0.2635432],"study_design_scores_gemma":[0.000266039,0.0002849211,0.0257045,0.00009474603,0.0002971369,0.001303105,0.000131188,0.5220197,0.388577,0.01984069,0.04117044,0.0003104882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1101426,0.0008558811,0.7265492,0.0003511839,0.0001742794,0.0001057857,0.01618032,0.1436232,0.002017454],"genre_scores_gemma":[0.3325453,0.001103195,0.5773153,0.0003769597,0.0001061336,0.0006567862,0.03085088,0.052451,0.004594474],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006216795,"threshold_uncertainty_score":0.02079725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317197002419775,"score_gpt":0.2396135665741754,"score_spread":0.2164415965499777,"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."}}