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!