Dear Michael,

could you please comment on how to properly apply a vst transformation to a new dataset (in this case, left-out samples)? I have been breaking my head about this and cannot seem to get it correct. dds_test is currently two samples but this could vary in the future. Ideal use case is just one test sample.

a small example of my current script:

dds_train <- DESeqDataSetFromTximport(txi_train, colData = train_samples, design = ~ sex. + condition)
dds_train <- dds_train[keep, ] # some filtering for minimal expression 
dds_train <- DESeq(dds_train) # to obtain size/normalization factores, although I believe vst internally normalizes the data so this strictly isn't necessary 
vsd_train <- vst(dds_train, blind = TRUE)

dds_test <- DESeqDataSetFromTximport(txi_test, colData = test_samples, design = ~ 1)
dds_test <- dds_test[keep, ] # some filtering for minimal expression --> different genes ! almost complete overlap
dds_test <- DESeq(dds_test)
disp_fun_train <- dispersionFunction(dds_train)
normFactors_test <- counts(dds_test) / assays(dds_test)[["avgTxLength"]]
normFactors_test[normFactors_test == 0] <- 1
normFactors_test <- normFactors_test / exp(rowMeans(log(normFactors_test)))
normalizationFactors(dds_test) <- normFactors_test
dispersionFunction(dds_test) <- disp_fun_train
vsd_test <- varianceStabilizingTransformation(dds_test, blind = FALSE)


> sizeFactors(dds_train)
NULL
> dim(normalizationFactors(dds_train))
[1] 24307    85

I will further perform PCA on the train data and then project the vst-transformed left-out samples in the same PCA space.

Thank you in advance!