Just plot the normalised counts. If you want, conduct differential expression analysis between male and female. Your analysis is in your hands.

If they are profiled in your study, XIST and TSIX should come out as 2 of the most differentially expressed between male and female.

Here is simple code to plot a heatmap of the top 50 most highly expressed transcripts in a group of samples:

# extract normalised counts matrix
norm <- counts(dds, normalized=TRUE)

# create a vector of the female sample IDs
femaleSampleIDs <- c("", "", ..., "")

require("RColorBrewer")
require("gplots")

select <- order(rowMeans(norm[,which(colnames(norm) %in% femaleSampleIDs)]), decreasing=TRUE)[1:50]

hmcol <- colorRampPalette(brewer.pal(9, "YlGn"))(50)

heatmap.2(data.matrix(norm[select, which(colnames(norm) %in% femaleSampleIDs)]), main="Female samples,/nhighly-expressed transcripts", cexRow=0.8, cexCol=0.8, col=hmcol, Rowv=FALSE, Colv=FALSE, labCol=NA, scale="none", dendrogram="none", trace="none", margin=c(1, 5), key.title="Counts density", key.xlab="Counts", key.ylab="")

text(0.05, 0.65, "Decreasing\nexpression\n(top 50)", xpd=T)

arrows(0.05, 0.6, 0.05, 0.4, lwd=3, xpd=T)

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You can also just produce scatter plots and compare male and female expression side by side from variance-stabilised (vst())or regularised log (rld()) counts. If using either of these to produce counts for this purpose, the ensure that you use blind=FALSE as a parameter to these unctions. You can then produce a boxplot with scatter points overlay by looking here: A: Boxplot in ggplot2

Kevin