The <tt>sva</tt> package for removing batch effects and other unwanted variation in high-throughput experiments
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Summary: Heterogeneity and latent variables are now widely recognized as major sources of bias and variability in high-throughput experiments. The most well-known source of latent variation in genomic experiments are batch effects—when samples are processed on different days, in different groups or by different people. However, there are also a large number of other variables that may have a major impact on high-throughput measurements. Here we describe the sva package for identifying, estimating and removing unwanted sources of variation in high-throughput experiments. The sva package supports surrogate variable estimation with the sva function, direct adjustment for known batch effects with the ComBat function and adjustment for batch and latent variables in prediction problems with the fsva function. Availability: The R package sva is freely available from http://www.bioconductor.org. Contact: jleek@jhsph.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Surrogate variable analysis was used to detect hidden technical or biological factors that may influence brain protein expression, and significant surrogate variables were adjusted for as covariates.
“The surrogate variable analysis (SVA) method in the sva R package (version 3.52.0) 45 was used to detect potentially hidden variables that may influence brain protein expression.”
From Unraveling sex differences in Alzheimer's disease and related endophenotypes with brain proteomes (2025) · cites this paper as [45]
sva and PEER, which use data across all sites, removed 85.1% to 87.7% of the original total false positive DEGs.
“Some methods (specifically sva and PEER) that leveraged all data across all sites were quite successful at ameliorating the high rate of false positives (Fig. 1, Supplementary Fig. 2), removing 85.1% to 87.7% of the original total false positive DEGs.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [26]
More than 85% of RNA-seq false positives could be removed by subtracting latent variable effects identified by sva or PEER through joint analysis of all genes across multiple sites, without decreasing sensitivity or specificity of DEG detection.
“However, the majority of RNA-seq false positives (>85%) could successfully be removed by subtracting the effects of latent variables identified by either sva26,27 or PEER30, which could be achieved by jointly analyzing the set of measurements of all genes across multiple sites, without a decrease in the sensitivity or specificity of DEG detection at each site or across sites.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [26]
Both library types had similar accuracy as measured by Matthews correlation coefficient when compared to 802 TaqMan assays.
“Both library types had similar accuracy as measured by Matthews correlation coefficient (MCC, Fig. 5d)40, 41, which is a joint measure of the assay’s sensitivity and specificity.”
From Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study (2014) · cites this paper as [41]
Batch correction was performed for each cell type separately using ComBat.
“Batch correction was performed for each cell type separately using ComBat as implemented in the R package sva (Leek et al., 2012), using the default parametric adjustment mode.”
From A cellular and spatial map of the choroid plexus across brain ventricles and ages (2021) · cites this paper as Leek et al., 2012
Unwanted or hidden sources of variation, such as the use of different library preps and different batches, were removed using the sva package.
“Unwanted or hidden sources of variation, such as the use of different library preps (Illumina and Takara) and different batches, were removed using the sva package 60 .”
From Transcriptomic analysis of purified human cortical microglia reveals age-associated changes (2017) · cites this paper as [60]
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- Authors
- Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D.
- Affiliations
- Boston University (US)
- Corresponding author
- Leek, Jeffrey T.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-01-17
- Volume
- 28
- Issue
- 6
- Pages
- 882-883
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3307112
- OpenAlex
- W2169353806
- Cited by
- 6,231
- References
- 12
- Citations by year
- 2026: 747 · 2025: 920 · 2024: 941 · 2023: 792 · 2022: 749 · 2021: 708 · 2020: 432 · 2019: 326 · 2018: 218 · 2017: 153 · 2016: 112 · 2015: 66 · 2014: 37 · 2013: 20 · 2012: 7
- FWCI
- 16.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R01HG002913)
- National Institute of General Medical Sciences (R01GM103552)
- National Center for Research Resources (R01RR021967, RR021967)
- 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 effects, surrogate variable analysis, latent variables, high-throughput experimentation, genomic data, combat, R package, bias removal
- MeSH terms
- Urinary Bladder Neoplasms, Humans, Regression Analysis, Software, Gene Expression Profiling, Genomics, High-Throughput Nucleotide Sequencing
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