Hello,

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

The canonical analysis for RNA-Seq is ANOVA, so the default for most software, including DESeq2 is to label the coefficient 'logFC', which is definitely not the 'per unit change of that variable', but instead is the log fold change between groups.

If you use a continuous variable, you still get the coefficient (in this case the regression slope, but still labeled 'logFC'), but now the interpretation is the log change in expression for a unit change in your continuous variable.