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Old 11-11-2010, 04:54 PM   #1
upendra_35
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Default qPCR validation of RNA-Seq data

I am wondering if anyone has a good and easy way of comparing RNA-Seq data and qPCR result/validation statistically. I have seen papers comparing the microarray data and qPCR but not RNA-Seq data. Any help would be highly appreciate.
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Old 11-12-2010, 08:09 AM   #2
steven
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Quote:
Originally Posted by upendra_35 View Post
I am wondering if anyone has a good and easy way of comparing RNA-Seq data and qPCR result/validation statistically. I have seen papers comparing the microarray data and qPCR but not RNA-Seq data. Any help would be highly appreciate.
There are papers with comparison of RNA-seq and qPCR data. Check out for instance Griffith et al, Nature Methods 2010, or Asmann et al BMC Genomics, 2009. Probably the Mortazavi et al too, and those from the Burge lab.
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Old 11-12-2010, 04:13 PM   #3
upendra_35
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Thanks Steven. I will check it out....
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Old 11-16-2010, 04:52 PM   #4
malachig
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For added convenience to people landing here ... in Griffith et al. Nature Methods. 2010 Oct;7(10):843-847 [Abstract | Full Text | PDF | Supplementary Materials] the qPCR validations are presented in Figure 2. The experiment involved qPCR validation of 192 differentially expressed exons identified in the RNA-seq data. An additional qualitative assay involving RT-PCR, gel electrophoresis and Sanger sequencing was used to validate 189 exon-skipping junctions (see Supplementary Figure 10a in the Supplementary Materials). The actual data values are available from the ALEXA-seq website here. The validation rates for the quantitative and qualitative assays were 88% and 85% respectively. The selection of targets for validation was not biased towards highly expressed exons. Actually it was biased towards lowly expressed exons that were nevertheless identified as expressed above background by RNA-seq. These likely represent minor isoforms in many cases (see Supplementary Figure 10b in the Supplementary Materials). The paper also includes direct comparison to both Affymetrix exon arrays and custom NimbleGen splicing microarrays.
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Old 10-30-2013, 08:33 AM   #5
sindrle
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Hi!
In lack of more cDNA I only have 12 genes from qPCR.. I want to compare edgeR vs DESeq2 vs Cuffdiff and see which I will use based on match with qPCR:

YES/no indicates if it is significantly changed.

Gene qPCR edgeR DESeq2 Cuffdiff
1 no YES no no
2 YES YES no no
3 no no no no
4 YES YES no no
5 YES no no no
6 no no no no
7 no no no no
8 no no no no
9 no no no no
10 no no no no
11 no no no no
12 no no no no

I really want to use edgeR, but is this test good enought?
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Old 10-30-2013, 08:51 AM   #6
dpryan
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I'm not sure why you tested 8 of those since they weren't DE according to any of the programs (I assume #5 was either borderline or had a huge foldchange). Given your effective n=3 of informative genes, edgeR would seem to be in the lead. However, you haven't tested any that DESeq2 or cuffdiff uniquely called DE. BTW, making more cDNA doesn't even take an hour...
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Old 10-30-2013, 03:52 PM   #7
sindrle
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Yeah, actually this genes where already measured, thats why I used them, I got the message about not having cDNA, but Ill ask again tomorrow!

How many should I aim to test from each?

Thanks!
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