I am reading through the manual of DESeq2 package and I have run into two questions about how to use this package properly.
1) In order to perform the variance stabilising transformation, there are two ways of doing this.
i) vsd <- vst(dds, blind=TRUE) ii) vsd <- vst(dds, blind=FALSE)
I understand that using blind=TRUE (by default) is an unsupervised analysis and is good for the quality assurance of samples and using blind=FALSE is to make use of the design formula to estimate the dispersion, which is good for the downstream analysis.
I am wondering which one is recommended or deemed more reasonable if my aim is to make the PCA plots and heatmaps to show the clustering of all samples for the publication?
2) In the condition that the paired samples are to be analysed in terms of differential expression analysis (e.g., the same sample before treatment and after treatment), I realise that the "subject" term should be included in the design formula in addition to the "condition" term. However, which of the below formulas should be preferred and why?
i) ~ subject + condition ii) ~ subject + condition + subject:condition
Apparently, this question is about under which condition the interaction term ("subject:condition") should be used?