Thanks so much for your reply. Sometimes it is hard to change someone else's mind, if you know what I mean! It's all about being persuasive and kind at the same time but it's just hard sometimes. But thanks a lot, I do appreciate the fact that the common way of doing this is actually the usual approach I use..
Hello everyone, I have been asked to summarise information from my count matrix - obtained from 4 replicates - in just two columns (one column per group). What's the best approach to do this? I usually use the following steps to get a classical heatmap containing as many tiles as per replicate
top <- head(order(-rowVars(assay(rld))),100)
and then I take out the row mean
mat <- assay(rld)[ top, ]
mat <- mat - rowMeans(mat)
Once I got what I want I use heatmap.2 to plot my results.
Since I have already applied the mean centring here, if I wanted to present my data as just two separate columns (thus merging values from each replicate in just one tile/condition), I should take the average from each group (i.e. condition 1 and condition 2); in this way I always get almost 'specular' results and I don't think it is correct to do this way. Any ideas?
1 answer
Z-scaling with only two columns is not very informative imho since you will see only a bimodal high and low pattern. Why don't you do it like everyone does, so perform standard DE analysis, take the differential genes and then show a heatmap for those genes based on the Z-score for all samples.
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