DESeq2-MultiBatch: Batch Correction for Multi-Factorial RNA-seq Experiments
Bibliographic record
Abstract
Summary RNA sequencing (RNA-seq) experiments frequently encounter batch effects that can significantly distort biological interpretations, particularly in complex, multifactorial studies where biological variables interact with experimental batch conditions. Existing batch correction tools primarily address technical variability and often neglect these critical interaction effects, resulting in incomplete adjustments. To address this gap, we introduce DESeq2-MultiBatch, a novel, lightweight batch correction method implemented entirely within the DESeq2 analytical framework. Unlike conventional approaches, DESeq2-MultiBatch directly leverages DESeq2’s internal model estimates to correct raw gene count data, accurately adjusting for experimental batch effects, including interactions with biological variables. Here, we demonstrate that DESeq2-MultiBatch effectively mitigates batch-related variability while preserving genuine biological differences. Benchmarking against widely used methods highlights DESeq2-MultiBatch as a robust, practical solution for improving exploratory data visualization and downstream analyses in multifactorial RNA-seq studies. Availability and implementation https://github.com/julienroyulaval/DESeq2-MultiBatch Contact julien.roy.10@ulaval.ca Supplementary information Supplementary data are available at Bioinformatics online.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.024 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".