{"id":"W4409726988","doi":"10.1101/2025.04.20.649392","title":"DESeq2-MultiBatch: Batch Correction for Multi-Factorial RNA-seq Experiments","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Factorial; Factorial experiment; RNA-Seq; Computer science; Mathematics; Statistics; Chemistry","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":[0.01820757,0.003243226,0.002525873,0.002139388,0.002384773,0.003285482,0.004336057,0.001787848,0.03397899],"category_scores_gemma":[0.03277073,0.00238141,0.003354239,0.002534234,0.001452191,0.001912732,0.004067064,0.005391224,0.02398566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002046071,"about_ca_system_score_gemma":0.006453276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004658724,"about_ca_topic_score_gemma":0.01123305,"domain_scores_codex":[0.9909474,0.002874418,0.0005848371,0.002732735,0.002400506,0.0004599814],"domain_scores_gemma":[0.9872816,0.006380828,0.0007128079,0.002736783,0.002431449,0.0004566488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004143154,0.0004313224,0.0287366,0.003908751,0.00225796,0.0006394588,0.002179313,0.05346369,0.1585705,0.02045053,0.4839895,0.2412293],"study_design_scores_gemma":[0.0008774237,0.000500516,0.02467245,0.0004815514,0.0004463691,0.0005209566,0.0004144183,0.3472061,0.2342463,0.05807729,0.3315867,0.0009700046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01576667,0.0008320854,0.7989731,0.0006874344,0.001078669,0.0005589858,0.03962848,0.139521,0.002953553],"genre_scores_gemma":[0.05260137,0.0003695207,0.8249094,0.001529907,0.0002826232,0.003174899,0.05516296,0.05562865,0.006340692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03397899,"threshold_uncertainty_score":0.1136711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03414700239313771,"score_gpt":0.2915854920478053,"score_spread":0.2574384896546676,"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."}}