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  • dpryan
    replied
    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.

    Leave a comment:


  • colaneri
    replied
    combining tophat with DESeq

    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

    Leave a comment:


  • gringer
    replied
    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?

    Leave a comment:


  • colaneri
    replied
    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

    Leave a comment:


  • sdriscoll
    replied
    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...

    Leave a comment:


  • naman
    replied
    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,

    Leave a comment:


  • kenietz
    replied
    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

    Leave a comment:


  • sudders
    replied
    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.

    Leave a comment:


  • kenietz
    replied
    Hi dvanic,
    thank you for the info.

    Well in fact the 3 samples were sequenced 2 times because of some trouble with the machine. The first run seems to be better tho. So i can say that i got replicates

    My samples are from fish but when i used zebra fish as ref genome for tophat, cufflinks, cuffdiff it seems that my species is not very close to zebra fish. There was another genome a bit closer but still not perfect case. So yeah i did denovo transcriptome assembly but with soapdenovo-trans.

    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.

    Leave a comment:


  • dvanic
    replied
    You have no reference genome or annotation, and no replicates? That is NOT a good place to be in, and I would strongly advise you to "get some replicates" if you can.

    However, the first problem I am assuming you have is doing de novo transcriptome assembly, for which I have had good experience with Trinity. The Trinity website also has a guide for how to do DE analysis in R (http://trinityrnaseq.sourceforge.net..._analysis.html). So, yes, you can do it, but with one replicate your tests would be very underpowered.

    Hope this helps.

    Leave a comment:


  • kenietz
    replied
    Hi guys,
    this thread was really informative to me. I have one question tho. I have PE rna-seq from a species without ref genome and annotation. I have only 3 samples coming from different conditions and no replicates.

    Is it possible to use DESeq for DE analysis?

    Thank you!

    Leave a comment:


  • billstevens
    replied
    Thanks so much areyes!

    I'm just checking out my data with EasyRNASeq and DESeq, and I'll post a comparison between that and Cuffdiff 2.0.2 and Cuffdiff 1.3.

    Leave a comment:


  • areyes
    replied
    5 of them are not!

    Alejandro

    ps. I just noted that I got a "thumbs down" in my last post", probably because extensive editing ? :P

    Leave a comment:


  • dpryan
    replied
    Out of curiosity, are the 21 findings from the all-treated-vs-untreated cuffdiff output also found in the DEXSeq results?

    Leave a comment:


  • areyes
    replied
    Dear billstevens and turnersd,

    Actually, what you guys mention about the new versions of cuffdiff was also asked by the reviewers, and that is the reason we included the most recent versions of cuffdiff (by the time we did the resubmission) in the comparisons with DEXSeq. Also, please note that all the data and code used for the DEXSeq paper (for now only in the preprint) is available online in the Supplement II (http://www-huber.embl.de/pub/DEXSeq/...ent_II_v2.html), and because is hard to track the new versions of cuffdiff, any contribution of comparisons (with this or other data) would be greatly appreciated!

    However, I just made the comparison using the latest version of cuffdiff (2.0.2) and this are the results of some of the comparisons (turnersd, this would be the continuation of the supplement table of the paper):

    Code:
    comparison                   cuffdiff_202_sp       DEXSeq_1.0.5
    all-treated-vs-untreated        21                          159
    untreated13-vs-untreated24      0                            8
    untreated14-vs-untreated23      0                            7
    The new cuffdiff seems to be very, very conservative.
    Last edited by areyes; 08-15-2012, 11:56 PM. Reason: edit table

    Leave a comment:

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