# 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.
If you work with next-generation sequencing (NGS) data, particularly RNA-seq, you know the nightmare of batch effects. You run your experiment, get your counts, but when you cluster the samples, they separate by date of extraction or sequencing run rather than by treatment group. sequator download
The object svobj$sv contains your new "Sequnator" variables. Add these to your DESeq2 design formula. Do not manually adjust the counts. Instead, include the surrogate variables in your statistical model: # Assuming 'counts' is your expression matrix #