Dear bioconductor community,

This question might be very basic, but unfortunately I am not able to solve it using google or the bioconductor forums...

Following

data.deseq.result <- DESeq(data.deseq.input)
data.deseq <- as.data.frame(results(data.deseq.result))

data.deseq contains a summary of my expression data for the comparisons, e.g log2FC for my treatment group over my control group. Is there any chance to access the log2FC values for the individual samples (e.g. as in log2FC relative to the mean log2FC of the control group)?

Or, maybe on a higher/background level: When having phenotypic data (e.g. body weight), what is the best way to check for correlation between these data and transcriptome data?

Any help is appreciated, Martin

You can get the normalized counts for your data, which is what is used for calculating the changes, so I guess you could compare that with the Base Mean, or the average of the control group.

To handle numerical factors, look up how to do dose response. DESeq can calculate the slope of the regression line and a p-vale for that.