{"id":"W2889795168","doi":"10.12688/f1000research.16198.2","title":"scClustViz – Single-cell RNAseq cluster assessment and visualization","year":2019,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canada First Research Excellence Fund; University of Toronto","keywords":"Cluster analysis; Visualization; Computational biology; Cell type; Interactive visualization; Biology; Graphical user interface; Cell; Cluster (spacecraft); Data mining; Computer science; Transcriptome; Data visualization; Gene expression profiling; Bioinformatics; Gene expression; Gene; Genetics; Artificial intelligence","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":[0.002799281,0.002401641,0.002464083,0.002963666,0.001172857,0.003747734,0.003974638,0.001627595,0.0678901],"category_scores_gemma":[0.005803363,0.001141513,0.002149212,0.002271966,0.0007099988,0.001719089,0.002199058,0.003506088,0.02778783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176592,"about_ca_system_score_gemma":0.002138284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003646954,"about_ca_topic_score_gemma":0.00458424,"domain_scores_codex":[0.9981855,0.0002508734,0.0001518924,0.0005032779,0.0007147951,0.0001937616],"domain_scores_gemma":[0.9975546,0.001137485,0.0001789787,0.0003619809,0.0006188645,0.0001480859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001866398,0.0001673958,0.004132314,0.004542234,0.0006766728,0.0009592627,0.001462993,0.009425998,0.1317263,0.0135015,0.6941459,0.1373931],"study_design_scores_gemma":[0.0007843711,0.0003189453,0.01208173,0.0007729891,0.0002733526,0.00124576,0.0006174707,0.2612798,0.299731,0.02981656,0.392129,0.0009490031],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.01250298,0.0006120703,0.3658093,0.0006253279,0.0007285225,0.0004382346,0.08990256,0.5224059,0.006975155],"genre_scores_gemma":[0.07076496,0.0008035956,0.6904595,0.001473557,0.0002060199,0.004695562,0.1077043,0.1118859,0.0120066],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.0678901,"threshold_uncertainty_score":0.227115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03882801886745165,"score_gpt":0.3369420188625023,"score_spread":0.2981139999950507,"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."}}