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Old 01-10-2014, 06:51 PM   #1
miaom
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Default Cufflink/Cuffdiff how to get fpkm for individual sample

I have 3 groups of samples (A, B and C), and n=4 for each group (12 in total)

Question 1.
Cuffdiff output only gives group-level averaged fpkm. If i want to run some correlation tests for the gene expressions vs. phenotypic traits, how can i get the individual level of fpkm?

Question 2.
If i run cuffdiff in two ways:
- 3 groups altogether (GrpA,B,C) and let the program deal with the statistics (either ANOVA or T-test)
- 3 pair-wise comparisons (GrpA-B, GrpA-C, GrpB-C) one-by-one
I wonder at the end if i could get the same number of significantly expressed genes for each comparison, for example GrpA-B? Did any one test that before?

Thanks in advance!
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Old 01-10-2014, 07:44 PM   #2
andylemire
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Quote:
Question 1.
Cuffdiff output only gives group-level averaged fpkm. If i want to run some correlation tests for the gene expressions vs. phenotypic traits, how can i get the individual level of fpkm?
The *.read_group_tracking files in the cuffdiff output directory contain the individual FPKMs for each replicate, but I admit I haven't looked at them myself to see how they compare to the group-level average.

Quote:
Question 2.
If i run cuffdiff in two ways:
- 3 groups altogether (GrpA,B,C) and let the program deal with the statistics (either ANOVA or T-test)
- 3 pair-wise comparisons (GrpA-B, GrpA-C, GrpB-C) one-by-one
I wonder at the end if i could get the same number of significantly expressed genes for each comparison, for example GrpA-B? Did any one test that before?
That depends on how you run cuffdiff, and the three groups themselves. The cuffdiff default uses a pooled model of dispersion across all replicated conditions, so you'd probably get different results from pair-wise versus all together (3 sets of 2 models averaged in the pair-wise, or 1 set of 3 models averaged in the all together). Using the per-condition model of dispersion might overcome this, but I haven't tried it.
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Old 01-10-2014, 10:21 PM   #3
miaom
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thank
You helped a looot!
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