bioconductor-sva

name	bioconductor-sva
description	The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensi
when_to_use	Use when: Surrogate Variable Analysis (SVA): Identifying and adjusting for hidden, unmodeled, or latent sources of variation in high-dimensional datasets using sva and num.sv.; Known Batch Effect Correction (ComBat): Adjusting for known batch effects in normalized gene expression data using ComBat.; RNA-seq Count Correction (ComBat-seq): Removing batch effects from raw RNA-seq count data using ComBat_seq wh. Not for: For single-cell RNA-seq batch correction, use Seurat or batchelor instead because ComBat/sva can overcorrect and destroy single-cell biological heterogeneity.; When the batch effect is completely confounded with the biological variable of interest, d
user-invocable	false

sva

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 3.60.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: mgcv, genefilter, BiocParallel
• Imports: matrixStats, limma, edgeR
• System requirements: URL
• Install: BiocManager::install("sva")

When to Use

• Surrogate Variable Analysis (SVA): Identifying and adjusting for hidden, unmodeled, or latent sources of variation in high-dimensional datasets using sva and num.sv.
• Known Batch Effect Correction (ComBat): Adjusting for known batch effects in normalized gene expression data using ComBat.
• RNA-seq Count Correction (ComBat-seq): Removing batch effects from raw RNA-seq count data using ComBat_seq while preserving the integer count structure.
• Sequencing Data SVA: Estimating surrogate variables specifically for sequencing data using svaseq.
• Frozen SVA for Prediction: Removing batch effects for prediction and clustering on new test datasets using fsva.

When NOT to Use

• For single-cell RNA-seq batch correction, use Seurat or batchelor instead because ComBat/sva can overcorrect and destroy single-cell biological heterogeneity.
• When the batch effect is completely confounded with the biological variable of interest, do not use ComBat/sva because it will remove the biological signal.

Data Requirements

• Expression Data: A matrix of normalized gene expression values (e.g., from exprs) or raw integer counts (specifically for ComBat_seq).
• Design Matrix: A model matrix representing the biological variables of interest (full model) created with model.matrix.
• Batch Covariate: A vector or factor indicating the known batch for each sample.

Key Parameters

• dat: The gene expression matrix (genes in rows, samples in columns).
• mod: Model matrix of interest representing the biological variables to preserve.
• mod0 (NULL): Null model matrix containing only the adjustment variables.
• n.sv (NULL): Number of surrogate variables to estimate; can be estimated using num.sv.
• batch: Factor or vector specifying the known batch variable.
• covar_mod (NULL): Model matrix of covariates to adjust for during ComBat_seq run.
• par.prior (TRUE): Whether to use parametric empirical Bayesian adjustments in ComBat.
• group (NULL): Biological variable to preserve in ComBat_seq.

Best Practices

• Always specify the biological model of interest (mod) when running sva or ComBat to prevent the algorithm from accidentally removing the biological signal.
• For downstream differential expression with limma, include the estimated surrogate variables as covariates in the design formula (e.g., using lmFit and contrasts.fit).
• Use ComBat_seq instead of standard ComBat when working with raw RNA-seq counts to maintain the integer distribution required by count-based differential tools.
• Use num.sv to automatically estimate the number of latent factors before running sva.

Common Pitfalls

• ComBat on raw counts: Running standard ComBat on raw counts; fix this by using ComBat_seq for raw counts.
• Missing biological model: Failing to include the biological design matrix in ComBat, which leads to over-correction; fix this by always passing the mod parameter.
• Confounded designs: Attempting to correct batch effects when they are completely confounded with the biological group; fix this by avoiding ComBat/SVA entirely.

Alternatives

• limma: Provides removeBatchEffect for linear model-based batch correction, ideal for visualization but not for downstream differential testing.
• ruv (Remove Unwanted Variation): Uses control genes or replicate samples to estimate and remove unwanted variation.
• harman: A PCA-based batch correction method that constrains the correction to prevent over-correction of biological variation.

Citations

• Leek, J. T., & Storey, J. D. (2007). Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genetics, 3(9), e161.
• Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118-127.
• Zhang, Y., Parmigiani, G., & Johnson, W. E. (2020). ComBat-seq: batch effect adjustment in RNA-seq count data using empirical Bayes methods. NAR Genomics and Bioinformatics, 2(3), lqaa078.

References

• Homepage: https://bioconductor.org/packages/sva
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/sva/inst/doc/sva.pdf

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