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get differential expression (version 1.0.0)

This tool compares two sets of data and find the differential expression. One very important component of the tool is the reference set. Actually, to use the tool, you need the two input sets of data, of course, and the reference set. The reference set is a set of genomic coordinates and, for each interval, it will count the number of feature on each sample and compute the differential expression. For each reference interval, it will output the direction of the regulation (up or down, with respect to the first input set), and a p-value from a Fisher exact test.

This reference set seems boring. Why not computing the differential expression without this set? The answer is: the differential expression of what? I cannot guess it. Actually, you might want to compare the expression of genes, of small RNAs, of transposable elements, of anything... So the reference set can be a list of genes, and in this case, you can compute the differential expression of genes. But you can also compute many other things.

Suppose that you cluster the data of your two input samples (you can do it with the clusterize and the mergeTranscriptLists tools). You now have a list of all the regions which are transcribed in at least one of the input samples. This can be your reference set. This reference set is interesting since you can detect the differential expression of data which is outside any annotation.

Suppose now that you clusterize using a sliding window the two input samples (you can do it with the clusterizeBySlidingWindows and the mergeSlidingWindowsClusters tools). You can now select all the regions of a given size which contain at least one read in one of the two input samples (do it with selectByTag and the tag nbElements). Again, this can be an other interesting reference set.

In most cases, the sizes of the two input samples will be different, so you should probably normalize the data, which is an available option. The ---rather crude--- normalization increases the number of data in the least populated sample and decreases the number of data in the most populated sample to the average number of data.