library(DESeq2) coldata$SV1 <- svobj$sv[,1] coldata$SV2 <- svobj$sv[,2] Create DESeq object with SVs as covariates dds <- DESeqDataSetFromMatrix(countData = counts, colData = coldata, design = ~ SV1 + SV2 + condition) Run DESeq dds <- DESeq(dds) Common Download Issues & Fixes | Problem | Solution | | :--- | :--- | | "Package ‘sva’ is not available" | You forgot BiocManager::install() . CRAN doesn't host it. | | "Error: 'sequnator' not found" | You misspelled it. The function is sva() , not sequnator() . | | R crashes when running sva() | Your matrix is too large. Use method="irw" (faster, less memory). | | "Need at least 2 surrogate variables" | Your batch effect is weak, or you have too few samples (<10 total). | Pro Tip: The "Frozen SVA" for New Data If you plan to predict batch effects on future datasets, use frozen SVA :
April 14, 2026 | Category: Bioinformatics Tools sequator download
Mastering NGS Batch Effects: How to Download and Run Sequnator The function is sva() , not sequnator()
Enter (often misspelled as "Sequator" in searches). This powerful tool, specifically the SVA package component (Surrogate Variable Analysis), helps you estimate and correct hidden batch effects when you don’t know what the confounding variables are. | | "Need at least 2 surrogate variables"
# Assuming 'counts' is your expression matrix # Assuming 'coldata' has columns: sample, condition, batch_known library(edgeR) lcpm <- cpm(counts, log=TRUE) Model for your biological question mod <- model.matrix(~ condition, data=coldata) Null model mod0 <- model.matrix(~ 1, data=coldata) Step 3: Run the Estimation Now you run the core function to estimate the number of hidden batch effects.