I have used DESeq2 to find genes where the expression level correlates with the size of a continous variable - in this case, the number of cells in a population (pop). We noticed that we have a batch effect in our data depending on what day we processed the experiment (day). So my design set up was as follows:
cData=data.frame(day=as.factor(df$day), pop=df[,x]) rownames(cData)<-colnames(d) dds<-DESeqDataSetFromMatrix(countData=d, colData=cData, design=~day+pop) dds<-DESeq(d.deseq)
The output of the results gives you log2FoldChange which is the "per unit of change of that variable." I am wondering whether there is a way to extract or infer from the data the coefficient of the regression slope? We would like to be able to deduce the size of a cell population from gene expression levels within a tissue sample.
Many thanks! Edie