ComBat-seq: batch effect adjustment for RNA-seq count data
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Abstract
The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.
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Fine-tuned Geneformer's cell embeddings primarily clustered by cell types and showed improved integration of platforms compared to the original data even after batch effect removal using ComBat or Harmony methods.
“Interestingly, the fine-tuned Geneformer’s cell embeddings primarily clustered by cell types and showed improved integration of platforms compared to the original data even after batch effect removal using the ComBat17 or Harmony18 methods (Extended Data Fig. 4b–f).”
From Transfer learning enables predictions in network biology (2023) · cites this paper as [17]
Geneformer fine-tuned with only Drop-seq data automatically integrated DroNc-seq data such that the fine-tuned Geneformer cell embeddings primarily clustered by cell types and showed improved integration of platforms compared to the original data even after batch effect removal using ComBat or Harmony.
“Geneformer fine-tuned with only Drop-seq data automatically integrated DroNc-seq data such that the fine-tuned Geneformer cell embeddings primarily clustered by cell types and showed improved integration of platforms compared to the original data even after batch effect removal using the ComBat17 or Harmony18 methods.”
From Transfer learning enables predictions in network biology (2023) · cites this paper as [17]
ComBat-seq was applied to adjust for sequencing batch effects.
“To adjust for sequencing batch effects within our total sample population, ComBat-seq was applied as previously described [37].”
Batch effects in the functional profile of the melanoma cohort were corrected using ComBat-Seq with study metadata as batch and treatment response as group.
“Before functional analysis, we used ComBat-Seq (v3.48.0)46 to correct the batch effects from the functional profile of the cohort of patients with melanoma. ComBat-Seq was run on the read count matrix with study metadata as batch and treatment response as group (full model).”
From Gut microbiota-derived hexa-acylated lipopolysaccharides enhance cancer immunotherapy responses (2025) · cites this paper as [46]
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- Authors
- Zhang, Y., Parmigiani, G., & Johnson, W. E.
- Affiliations
- Gilead Sciences (United States) (US)
- Harvard University (US)
- Boston University (US)
- Corresponding author
- Johnson, W Evan
- Venue
- NAR Genomics and Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-09-01
- Volume
- 2
- Issue
- 3
- Pages
- lqaa078-lqaa078
- ISSN
- 2631-9268 (ISSN-L)
- PMC
- PMC7518324
- OpenAlex
- W3088521497
- Cited by
- 1,566
- References
- 17
- Citations by year
- 2026: 276 · 2025: 378 · 2024: 389 · 2023: 247 · 2022: 186 · 2021: 76 · 2020: 10 · 2019: 1
- FWCI
- 42.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NAR Genomics and Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (4P30CA006516-51, 5U01 CA220413-03, 5R01GM127430-02)
- National Science Foundation (NSF-DMS 1810829)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- batch effect correction, RNA-seq count data, negative binomial regression, differential gene expression, statistical power, false positives, genomic data integration
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