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Old 05-23-2014, 07:57 AM   #1
microbe89
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Default Comparing multiple samples with cuffdiff

I have samples of 4 different conditions. Let's say untreated/treated and tissue A/tissue B.
#1: tissue A untreated
#2: tissue B untreated
#3: tissue A treated
#4: tissue B treated.

I need to compare between the treated vs untreated samples and also between samples.
What is the proper way to use cuffdiff to compare the samples?
Run cuffdiff with the pair of samples to compare or run it with all samples and use commeRbund to select the pairs I want to compare?

For example, if I want to compare condition #3 and #4, do I need to run cuffdiff with #3 and #4 or run it with all samples and select #3 and #4 using cummeRbund?

Thanks,
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Old 05-23-2014, 08:53 AM   #2
blancha
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I would use htseq-count and then DESeq2 (or edgeR), since DESeq2 allows you to use a multi-factor design.

Here is an example for a model:
~ tissue + treatment + tissue:treatment

Cuffdiff does not allow you to do a multi-factor design.

---

If you just want to compare individual samples with Cuffdiff, follow the new workflow.
Run Cuffquant on all the samples.
Specify the sample names in the samples file.
Specify the contrasts of interest in the contrasts file (eg. condition #3 vs condition #4)
Run Cuffdiff with the samples file and the contrasts file.

The results will all be in the same files, with the comparisons one after the other.
If you want, you can make separate files by extracting the results for each comparison with CummeRbund.
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Old 05-23-2014, 10:39 PM   #3
microbe89
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Thank you for the DESeq2 suggestion. I'll check it out.

Regarding Cuffdiff and the contrasts file, I'd like to ask one more question.

If I want to compare condition #1 vs condition #2 and condition #3 vs condition #4, then I can either
1) include all samples from the 4 conditions and specify the comparisons
in the contrasts file,
or
2) run Cuffdiff twice with condition #1 vs condition #2 and condition #3 vs condition #4 separately.

I assumed the two results from Cuffdiff 1) and 2) are the same but they are not identical. I found a thread that are the same question as mine with no answer.

http://seqanswers.com/forums/showthread.php?t=13849


Quote:
Originally Posted by blancha View Post
I would use htseq-count and then DESeq2 (or edgeR), since DESeq2 allows you to use a multi-factor design.

Here is an example for a model:
~ tissue + treatment + tissue:treatment

Cuffdiff does not allow you to do a multi-factor design.

---

If you just want to compare individual samples with Cuffdiff, follow the new workflow.
Run Cuffquant on all the samples.
Specify the sample names in the samples file.
Specify the contrasts of interest in the contrasts file (eg. condition #3 vs condition #4)
Run Cuffdiff with the samples file and the contrasts file.

The results will all be in the same files, with the comparisons one after the other.
If you want, you can make separate files by extracting the results for each comparison with CummeRbund.
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Old 05-24-2014, 04:22 AM   #4
blancha
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I just noticed in your experimental design that you don't have any replicates.
A multi-factor design may therefore not be appropriate in your case.
You may even want to ignore the p-values calculated by Cuffdiff or DESeq2 altogether, and only take into account the fold changes (not the estimated fold changes calculated by DESeq2, but the actual fold changes).

To try and answer your question directly, I'd need more information.
Both ways of running Cuffdiff are correct, incidentally, so you're not making any mistakes either way.
What version of Cufflinks are you using?
What are the differences in the results between running Cuffdiff in the 2 manners you described? Are the FPKM values different, or just the p-values in the comparisons?

One possibility is that, in the absence of replicates, Cuffdiff is calculating the variance levels across all the conditions, resulting in different results when you include less conditions. It's just a hypothesis. Cuffdiff is a bit of a black box, so others may come up with better answers.

I would be more concerned with the absence of replicates in your experimental design, than with Cuffdiff's quirks. To get more consistent results than with Cuffdiff, you can always use htseq-count to count the reads, and then DESeq2 to normalize the counts relative to the total number of reads. But, the determination of the significance of the differences between the samples will always be unreliable in the absence of replicates.
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Old 10-02-2014, 05:00 AM   #5
JakobHedegaard
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Default contrast file for cuffdiff

Hi,
I have a number of RNA-Seq samples classified into three groups "1", "2" and "3".
Following the new workflow (cuffquant) I have provided cuffdiff with a sample sheet and have received the results of the three contrasts "1vs2", "1vs3" and "2vs3". So far so good...
But now I would like to test "1vs2+3", "2vs1+3" and "3vs1+2" - so each group in contrast to the other two.
Can I do this using a contrast file? The example in the manual does not suggest this...
Or do I have to provide three new samplesheets with two groups in each (e.g. "1" and "2+3" for one of the paired contrasts)?
Thanks,
Jakob
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Old 10-02-2014, 05:03 AM   #6
Bukowski
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Jakob, you may get more joy posting this as a new question rather than appending it to a thread that hasn't been posted to in several months..
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