**0**wrote:

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

**50k**• written 9 weeks ago by Edie Crosse •

**0**

MacDonald is right...the value returned is the slope, no matter what the label is. You can verify this yourself by plotting the log2 of the normalized counts against your cell number; the number DESeq gives you should be the slope of that line. I've done that check myself against my own data, and it works out.

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