Originally posted by syambmed
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Method to categorize mRNA-seq data based upon expression value
Hello All,
I have a pre processed mRNA-seq data for hg19 genome, in which for each gene the RPKM value is calculated. The value ranges from 99960 to zero. I have just one sample.
I want to categorise these genes into highly, medium and weakly expressed genes.
What could be the best way to do it?
Your suggestions would be highly appreciated.
Thanks a lot.
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Originally posted by sikidiri View PostHello All,
I have a pre processed mRNA-seq data for hg19 genome, in which for each gene the RPKM value is calculated. The value ranges from 99960 to zero. I have just one sample.
I want to categorise these genes into highly, medium and weakly expressed genes.
What could be the best way to do it?
Your suggestions would be highly appreciated.
Thanks a lot.
I would first sort the values and use percentiles ("tiers") to define categories with a similar population and inspect the threshold values.
You may also want to consider absolute thresholds (like "RPKM<1", "1<RPKM<10" and "10<RPKM") but I do not know if there are "standards" for such values and I actually doubt that it is in practice reasonable to use values obtained from different protocols/conditions/software/etc..
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Hello Steven,
Thanks for your answer. But how to decide about the threshold to make these categories based upon expression is my main problem. Do you think any statistical tests would help me. Any paper/example would help me understand this better.
Thanks again.
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It's been a while since I've done it, but if you google "cluster optimal group number" you can find methods for gap calculations and other things to find an optimal cluster number. I recall there being R packages for a lot of this stuff, such as the cluster package.
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Originally posted by lpachter View PostThats correct- the procedure RCJ suggests will give you an estimate of the actual tag count for each transcript.
Is this to say that if one sums up the (FPKM * length in kb * reads mapped in millions ) of each transcript in a gene, one would obtain the total *estimated* read count for that gene?
But this has to be done individually for each transcript and then grouped into a gene, right?
Carmen
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Originally posted by Simon Anders View Post
If I don't care about isoforms or think that my coverage is too low to distinguish isoforms anyway, I expect to get optimal power by simply summing everything up.
Originally posted by Simon Anders View Post
Cuffdiff is, as I understand it, designed to deal with such issues, while our approach ignores them. I expect that DESeq, in compensation for being unsuitable to detect differences in isoform proportions as in your example, achieves much better detection power for differences in total expression (per gene, summing over isoforms), especially at very low counts.
Originally posted by Simon Anders View Post
As I am not clear on how biological noise is taken into account by cuffdiff I cannot be fully sure whether this expectation will hold (and I'm quite curious to learn more about cuffdiff once your paper is out).
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Originally posted by lpachter View PostPlease see Figure 1 of http://www.nature.com/nbt/journal/va...0.html#/figure
Cufflinks' model in figure 2 has three times more counts than figure 1 and doesn't differentiate anywhere near as cleanly between isoforms. Surely maximum-likelihood count assignment can be incorrect, too, given ambiguous reads? Looking at the supplementals, however, I'm inclined to accept that it may be incorrect less often than raw counts when dealing with real data.
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