{"id":"W1991669423","doi":"10.1371/journal.pone.0067019","title":"ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":933,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Lawson Health Research Institute; Ontario Genomics","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"RNA-Seq; Analysis of variance; Replication (statistics); Biology; Variance (accounting); Sample size determination; Population variance; Expression (computer science); RNA; Statistics; Computational biology; Population; Sample (material); Gene expression; Genetics; Transcriptome; Mathematics; Gene; Computer science","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":[0.0000379684,0.0001219535,0.0002037118,0.00005286924,0.00009701226,0.00002608581,0.0001035268,0.0000953895,0.00005894294],"category_scores_gemma":[0.00002841602,0.0001098712,0.0001311392,0.00007889812,0.00001910458,0.000001067409,0.00008538083,0.00002961956,0.000007341211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007238698,"about_ca_system_score_gemma":0.000006767953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008889332,"about_ca_topic_score_gemma":0.00005571288,"domain_scores_codex":[0.9992378,0.00002217191,0.0001740538,0.0002766584,0.0001077845,0.000181581],"domain_scores_gemma":[0.9994889,0.0000109631,0.00007720953,0.0002577623,0.0001133598,0.00005183456],"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":[0.00002510609,0.0002097934,0.0321287,0.00001872374,0.0007561351,4.283756e-8,0.00001961206,0.00002820404,0.9659246,0.00001023003,0.0003966415,0.0004821938],"study_design_scores_gemma":[0.0005006209,0.0001673531,0.2670102,0.00001090525,0.000555894,1.053121e-7,0.00002657313,0.0007814392,0.7301565,0.0002589632,0.0003167215,0.0002147939],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968132,0.0002670608,0.00228876,0.0000844626,0.00007116752,0.0003348485,0.0000384605,0.000005159717,0.00009692328],"genre_scores_gemma":[0.9944423,0.00009459265,0.003978403,0.00006269618,0.0002332193,0.000150852,0.0003886526,0.00001640358,0.0006328456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2357682,"threshold_uncertainty_score":0.4480417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0263654920602898,"score_gpt":0.2222645298640864,"score_spread":0.1958990378037966,"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."}}