Thanks Simon,
Can you please recommend me on different tool for quantification that
suitable with DESeq?

Thanks,
Pap

On Mon, Jun 18, 2012 at 10:20 AM, Dror Hibsh <drordh@gmail.com> wrote:

> i am doing de novo assembly, that is why i am using raw reads(unaligned)
> Thanks!
>
>
> On Mon, Jun 18, 2012 at 8:24 AM, Dror Hibsh <drordh@gmail.com> wrote:
>
>> Hi Tim, Thanks for the response!
>> No i didnt..
>> Is there any recommended tool for removing the pcr duplicates from raw
>> reads?
>>
>> Thanks,
>> Pap
>>
>>
>>
>> On Sun, Jun 17, 2012 at 7:29 PM, Tim Triche, Jr. <tim.triche@gmail.com>wrote:
>>
>>> you say you are "new to this field" yet you seem to have done almost
>>> everything right.
>>>
>>> have you tried removing PCR dupes that might be skewing your results,
>>> before transcript assembly with RSEM and DE testing?
>>>
>>>
>>>
>>> On Sun, Jun 17, 2012 at 9:23 AM, papori [guest] <guest@bioconductor.org>wrote:
>>>
>>>>
>>>> Hi all,
>>>> First of all, I am new to this field so i am sorry if i am not clear..
>>>>
>>>> I will try to explain what is my aim, and what i did before DESeq.
>>>>
>>>> I am trying to do Differential expression analysis using DESeq for
>>>> De-Novo invertebrate .
>>>>
>>>> We had an experiment of 3 conditions with 3 biological replicate for
>>>> each.(total of 9 samples)
>>>> We used hiseq2000 50bp single end reads.
>>>> We had a different library size for each.(that was single cell
>>>> experiment so we had amplification step.. what yield variance in the
>>>> library sizes..)
>>>>
>>>> We reconstructed the transcriptome using Trinity.
>>>> Estimating counts with RSEM.
>>>>
>>>> And then i used DESeq..
>>>>
>>>> i have weird behavior of the data, and i dont know if it is because
>>>> something wrong that i did..
>>>>
>>>> i am always getting down-expression from condition 1 to condition 2 and
>>>> high-expression from condition 2 to condition 3.(for all the transcripts,
>>>> no out-layers..)
>>>>
>>>> The number of counts that got for each condition to reference
>>>> transcriptome was:
>>>> 32M, 27M, 40M respectively..
>>>> What made me to think that because cond 2 has lowest count it has a
>>>> behavior of down-expression from 1 to 2 and high-expression from 2 to 3..
>>>>
>>>> if my conclusion is right, i am in a big mass..(Normalization??)
>>>>
>>>> my DESeq script is:
>>>> Conditions = c("C1", "C2", "C3", "C1", "C2", "C3","C1", "C2", "C3")
>>>> Counts<-round(MultiGeneMat,0)
>>>> cds <- newCountDataSet(Counts,Conditions)
>>>> cds <- estimateSizeFactors(cds)
>>>> cds <-
>>>> estimateDispersions(cds,method="per-condition",sharingMode="maximum",fitType="local")
>>>>
>>>> res_1vs2 <- nbinomTest(cds,condA="C1",condB="C2")
>>>> sigDESeq_1vs2 <- res_1vs2[res_1vs2$padj <= 0.1, ]
>>>> sigDESeq_1vs2 <- na.omit(sigDESeq_1vs2)
>>>>
>>>> res_2vs3 <- nbinomTest(cds,condA="C2",condB="C3")
>>>> sigDESeq_2vs3 <- res_2vs3[res_2vs3$padj <= 0.1, ]
>>>> sigDESeq_2vs3 <- na.omit(sigDESeq_2vs3)
>>>>
>>>> res_1vs3 <- nbinomTest(cds,condA="1",condB="C3")
>>>> sigDESeq_1vs3 <- res_1vs3[res_1vs3$padj <= 0.1, ]
>>>> sigDESeq_1vs3 <- na.omit(sigDESeq_1vs3)
>>>>
>>>>
>>>>
>>>> Is there anything wrong here? or anywhere else??
>>>> If i wasnt clear enough so tell me in what and i will try to explain..
>>>> Any help will be appreciate here!
>>>> Thanks,
>>>> Pap
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>  -- output of sessionInfo():
>>>>
>>>> R version 2.14.0 (2011-10-31)
>>>> Platform: x86_64-unknown-linux-gnu (64-bit)
>>>>
>>>> locale:
>>>>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
>>>> LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
>>>>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=C
>>>>               LC_NAME=C
>>>>  [9] LC_ADDRESS=C               LC_TELEPHONE=C
>>>> LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>>>>
>>>> attached base packages:
>>>> [1] stats     graphics  grDevices utils     datasets  methods   base
>>>>
>>>> other attached packages:
>>>> [1] edgeR_2.4.6    limma_3.10.3   DESeq_1.6.1    locfit_1.5-8
>>>> Biobase_2.14.0
>>>>
>>>> loaded via a namespace (and not attached):
>>>>  [1] annotate_1.32.3       AnnotationDbi_1.16.19 DBI_0.2-5
>>>> genefilter_1.36.0     geneplotter_1.32.1
>>>>  [6] grid_2.14.0           IRanges_1.12.6        lattice_0.20-6
>>>>  RColorBrewer_1.0-5    RSQLite_0.11.1
>>>> [11] splines_2.14.0        survival_2.36-14      tools_2.14.0
>>>>  xtable_1.7-0
>>>> >
>>>>
>>>>
>>>> --
>>>> Sent via the guest posting facility at bioconductor.org.
>>>>
>>>> _______________________________________________
>>>> Bioconductor mailing list
>>>> Bioconductor@r-project.org
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>>>> Search the archives:
>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>>>
>>>
>>>
>>>
>>> --
>>> *A model is a lie that helps you see the truth.*
>>> *
>>> *
>>> Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
>>>
>>>
>>
>>
>> --
>> -----------------
>> Dror Hibsh
>> 0507-669599
>> ------------------
>>
>>
>
>
> --
> -----------------
> Dror Hibsh
> 0507-669599
> ------------------
>
>


-- 
-----------------
Dror Hibsh
0507-669599
------------------

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