Hi everyone, I'm a new user of DESeq2. I'm learning DESeq2 using this webpage.
In this DESeq2 RNAseq workflow, it says "we specified blind = FALSE, which means that differences between cell lines and treatment (the variables in the design) will not contribute to the expected variance-mean trend of the experiment. The experimental design is not used directly in the transformation, only in estimating the global amount of variability in the counts. For a fully unsupervised transformation, one can set blind = TRUE (which is the default).".
But in the instruction on R help, the instruction for blind is "logical, whether to blind the transformation to the experimental design. blind=TRUE should be used for comparing samples in an manner unbiased by prior information on samples, for example to perform sample QA (quality assurance). blind=FALSE should be used for transforming data for downstream analysis, where the full use of the design information should be made. blind=FALSE will skip re-estimation of the dispersion trend, if this has already been calculated. If many of genes have large differences in counts due to the experimental design, it is important to set blind=FALSE for downstream analysis."
They look opposite to me. For PCA plots of samples with different treatments, should I set blind to T or F?
Many thanks!
What do you find opposing? As PCA is typically the first step in quality control and detection of potential batch effects, one typically sets it to
Tto get a blinded/unbiased view on the data.