9.4 years ago by
Heat maps of your data can be made easily, as Sean said, once you generate some form of normalized gene counts from your reads. An easy way to do this is to run your data through tophat and cufflinks, or cuffdiff. With cufflinks you will get FPKM values for each gene for each sample, and with cuffdiff you can easily get FPKM values and ratios between any pair of samples (e.g. test and control, replicated or not). By default, the ratios are in natural log space, but you can transform them to the space you like. You can supply a GFF file of gene descriptions, and the output will simply be a table of those genes with quantification. At that point it will basically resemble the structure of microarray data that you are familiar with - the rows are genes (or transcripts depending on how you run things), and the columns are conditions. One caveat, with RNA Seq you will have genes detected in one sample but not the other (not so good for evaluating ratios), and you'll have to come up with some heuristic that suits you.
Bioconductor is another way to go, but even in that case, you would supply a GFF, or a set of bins (perhaps extracted using biomaRt) over which to quantify reads and assess ratios. The output once again is a table, from which you can make a heat map.