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Old 10-10-2012, 01:21 AM   #41
sudders
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Originally Posted by kenietz View Post
Hi dvanic,
Now whats bugging me is the following. how can one do DE on 3 sets of transcriptome data when the contigs from the different sets have different IDs? I mean every assembly is creating some contigs and there are named with some IDs which will differ between the assemblies.
As far as I can see you have two alternatives here. First you could use some kind of alignment to match the contigs across samples. Maybe best reciprical blast hits? A second alternative would be pool all three samples into one and assemble them all together to generate one assembly. Then quantify the transcripts in the joint assembly using the indevidual samples.

BTW, it sounds like you replicates are technical rather than biological replicates. It is generally not a good idea use technical replicates in DE analysis, as the models are explicitly designed to measure biological variance (which has a different distribution to technical variance). If you want to use your other run, I see no harm in pooling the two runs for each sample together.
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Old 10-10-2012, 01:37 AM   #42
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I see. The problem is that i cant pool them together and assemble. Too large sets. One of them is like 75M 2x75 PE and even soap-denovotrans seg faulted after loading more than 100M reads. So i had to remove some of the reads in order to assemble at all.The other sets are a bit smaller but still above 35M 75x2 reads each.

Seems that the first idea will do the job hopefully. It will take some time tho as the biggest contig set has like 40K contigs which are above 500bp. Well thats life i suppose
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Old 05-31-2013, 04:58 AM   #43
naman
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Default RDA Analysis

I have few queries regarding the RDA analysis to discuss. Although I see clear difference in the bacterial community between the samples, I am not able to relate the OTU abundance table with bacterial composition data and my factors (Mutations/Day effect in my case) on the RDA axis.

It would be great if it can be explained on mothur shared file.

Cheers,
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Old 05-31-2013, 05:09 PM   #44
sdriscoll
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Originally Posted by naman View Post
I have few queries regarding the RDA analysis to discuss. Although I see clear difference in the bacterial community between the samples, I am not able to relate the OTU abundance table with bacterial composition data and my factors (Mutations/Day effect in my case) on the RDA axis.

It would be great if it can be explained on mothur shared file.

Cheers,
i don't have an answer but may I suggest starting a new thread? this one's about Cuffdiff vs DESeq...most people aren't going to see your question unless they are first interested in Cuffdiff vs DESeq...
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Old 08-26-2013, 12:58 PM   #45
colaneri
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Default Using the genes.count-tracking file from cuffdiff in DESeq

DESeq needs count data in the form of rectangular table. My question is whether is correct or possible to use the genes.count_tracking file generated with cuffdiff as the counts table that DESeq requires?

I will appreciate your help
Alejandro
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Old 08-26-2013, 02:03 PM   #46
gringer
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Originally Posted by colaneri View Post
DESeq needs count data in the form of rectangular table. My question is whether is correct or possible to use the genes.count_tracking file generated with cuffdiff as the counts table that DESeq requires?
It is not correct, but it is possible to force DESeq to accept it. DESeq expects raw read counts, not normalised counts, as in the count_tracking files. As a warning, DESeq will complain if you try to feed it decimal values in the data table -- this is an indication that you're doing something you shouldn't.

If you're using cuffdiff to get the count data, why not use it to do the differential analysis as well?
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Old 08-26-2013, 02:18 PM   #47
colaneri
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Default combining tophat with DESeq

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Originally Posted by gringer View Post
It is not correct, but it is possible to force DESeq to accept it. DESeq expects raw read counts, not normalised counts, as in the count_tracking files. As a warning, DESeq will complain if you try to feed it decimal values in the data table -- this is an indication that you're doing something you shouldn't.

If you're using cuffdiff to get the count data, why not use it to do the differential analysis as well?
The reason is because I'm comparing the response of two genotype to two growing conditions, and I want to study the interactions of genotype x treatment

When using cuffdiff I just obtain a comparison of everything against everything. But most of that comparison are not usefull. DESeq can to two factor analysis.

The problem is I do not know how to create the entry table with good metadata about gene information, that is what I was thinking in use the gene.count tracking file. I like how tophat align the sequences also.

But now regarding what you told me of decimal numbers. I'm not talking to feed DESeq with RFPK values. It look to me that the gene.count traking files contains the raw number of reads mapped to each gene
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Old 08-27-2013, 12:19 AM   #48
dpryan
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Aren't the counts in the gene.count_tracking file scaled (i.e. they can be decimal)? Normally, one would just use htseq-count to generate a count table for DESeq. That's the simplest way to go about things and then you can easily check if the raw counts do in fact match those in the gene.count_tracking file.
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