Hello, before doing the DESeq2 analysis on my bulk RNA-seq data, I used SVA to identify new covariates and I want to visualize my pca with vst command in DESeq2, so I did the following passage (I also delete the batch effect of my RIN for the visualization):

mm_ALpcrRD <- model.matrix(~ condition + RIN, colData(dds_ALpcrRD))
mm0_ALpcrRD <- model.matrix(~1, colData(dds_ALpcrRD))
fit_ALpcrRD <- svaseq(matrice_ALpCR_vs_ALRD, mod=mm_ALpcrRD, mod0=mm0_ALpcrRD)

dds_ALpcrRD$SV1 <- fit_ALpcrRD$sv[,1]
dds_ALpcrRD$SV2 <- fit_ALpcrRD$sv[,2]

design(dds_ALpcrRD) <- ~  condition + SV1+ SV2
vsd_ALpcrRD <- vst(dds_ALpcrRD, blind=FALSE)
covariates_ALpcrRD <- colData(vsd_ALpcrRD)[,c("SV1","SV2")]

assay(vsd_ALpcrRD) <- limma::removeBatchEffect(assay(vsd_ALpcrRD), batch = vsd_ALpcrRD$RIN,
                                             design=mm_ALpcrRD)

plotPCA(vsd_ALpcrRD,intgroup=c("condition"))

With this passage I have a pca wht 100% of the variance in PC1 and 0% in PC2, do you think it is possible? Is the first time that happen this thing to me. What does this can mean? Thank you!

Perhaps Mike Love will be along later to comment on the order of vst and removeBatchEffect (not sure you should be using both here?), but anyway, you might consider either

A.) Not using RIN in your model, as the surrogate variables will probably do a better job of adjusting for RNA quality B.) If you do include RIN, do note that you are using it as an adjustment variable, in which case you should also include it in your null model.