The structure of my dataset is 4 cell lines of 2 genotype with biological duplicates (4*2*2 samples), the purpose of the analysis to assess the effect of different genotypes (KO vs WT) on the gene expression in 4 cell lines) 

I used "Interation" in order to test for differences in KO effects:

dds$group <- factor(paste0(dds$compartment, dds$genotype))
design(dds) <- ~ group
dds <- DESeq(dds)
resultsNames(dds)
results(dds, contrast=c("group", "CelltypeA_WT", "CelltypeB_cKO"))

as showed above, I could easily get the result as well as report the result as ".csv" files. 

but is there easy way to plot heatmap of the results above? 

something like: 

library("genefilter")
topVarGenes <- head(order(rowVars(assay(rld)),decreasing=TRUE),40)
mat <- assay(rld)[ topVarGenes, ]
mat <- mat - rowMeans(mat)
df <- as.data.frame(colData(rld)[,c("genotype","compartment")])
pheatmap(mat, annotation_col=df,fontsize=9,fontsize_number = 0.4 * fontsize)

TKS!

If you want to plot specific genes in a heatmap, don't use the topVarGenes to filter the mat. You can index the mat by the name or names of your specific genes:

mat <- assay(rld)
mat <- mat - rowMeans(mat)
mat <- mat[ your.specific.genes, ]