Hi!
I was wondering if their is a way to normalise the number of reads in a region and the RPKM of the nearest gene to that region, so that a correlation could be computed.
Like the following data shows number of tags in first column and RPKM in second column
Tags RPKM
15 0.14619
11 0
203 0.2259
129 10.701
300 7.0772
122 2.3234
346 10.666
77 3.117
201 16.749
What is the most potent way to normalise/scale the data in two columns so that a correlation can be computed?
Thank you
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Dataminer
1 answer
Calculating correlation doesn't require scaling, use Spearman's rank correlation if data is possibly not normal.
Believe it or not correlation is scale invariant ;) To try it out, it is easiest to start with R. I it has function cor and scale. scale is used to scale columns of a matrix to unit variance and mean 0. That is possibly useful for plotting and classification.
It is not surprising that the correlation is not perfect, that comes from the way RPKM is calculated. There are only two variables that influence the correlation: read count and exon length; the number of bases sequenced is constant for each sample. So exon length is the additional source of variance. That is also why some people think that RPKM introduces another gene-length dependent bias instead of resolving bias.
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Wouldn't that just be computing the RPKM for your region?
The aim is to correlate occupancy with RPKM