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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.
Below is a definitive guide to downloading and running Sequnator/SVA correctly. Strictly speaking, "Sequnator" is a colloquial name for the SVA package in R/Bioconductor. It uses a method called Leek’s approach to identify hidden sources of variation (sequencing run, technician, time of day) and includes them in your differential expression model. sequator download
R (version 4.0 or higher) and RStudio (recommended). If you work with next-generation sequencing (NGS) data,
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: Strictly speaking, "Sequnator" is a colloquial name for
# Train on old data train <- sva(training_matrix, mod, mod0, method="irw") new_svs <- fsva(training_matrix, mod, svobj, new_matrix) Final Verdict Don't search for "Sequnator download.exe". The real power is in the SVA package via Bioconductor. It takes 2 minutes to install and can save your paper from being rejected due to hidden batch effects.
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.