I am trying to makeContrasts between samples such that I can identify differentially regulated genes in a single sample.. (not a pairwise comparison). Therefore, one of the suggestions was to use limma makecontrasts. And I would like to know how to transform `DESeq`

scaled/normalised counts to use in limma. Below is my code snipped as to how I get my normalised counts

```
design <- data.frame(row.names = colnames( countTable ),condition = pheno$Condition, libType = rep("single-end",dim(pheno)[1]))
conds<-as.factor(design$condition)
cds <- newCountDataSet( countTable, conds )
# estimateSizeFactors -----------------------------------------------------
cds <- estimateSizeFactors( cds )
sizeFactors( cds )
cds<-estimateDispersions(cds)
#Normalised Counts for other analysis
#Make a counts table that is scaled by the size factors
temp = t(sizeFactors(cds))
sizematrix<-matrix(data=temp, nrow=nrow(countTable), ncol=ncol(temp), byrow=TRUE)
scaledcounts = countTable/sizematrix
head(scaledcounts)
```

Now, I am following the limma guide for `voom()`

to convert to log2-cpm

```
library(limma)
design<-model.matrix(~Cell_type) # Cell_type<-c(rep(1,3),rep(2,3),rep(3,3),rep(4,3))
v<-voom(y,design,plot=TRUE)
```

How can I make contrasts such that I can identify genes that are upregulated or downregulated in just one cell_type? Also please comment, if the above transformation from RNA-Seq to Limma is valid.

Thanks!

Yes,

`y`

would be the`scaledcounts`

from DESeq. Should I use rawcounts here??Yes, you should. Like I said, follow the examples found in the limma user's guide.