hi Jon,

If you are new to R and Bioconductor, please take some time to read over
the vignettes that accompany every software package on Bioconductor. We try
to pack them full of useful information! In addition, there is a lot more
technical information available in the reference manuals.

from the command line you can type, e.g.

browseVignettes(package="DESeq2")

On the first page of the DESeq2 vignette, we discuss why you should only
use raw counts as input to our software, not rounded normalized values or
FPKM values.

Also to help with discussion, by 'count matrix', we refer to a matrix of
non-negative integers 0,1,2,..., which were produced by counting reads or
fragments, which are the units of evidence of expression in RNA-Seq. So we
would avoid referring to a 'count matrix of FPKM values', because these
counts have been divided by gene length and library size.

Mike



On Tue, Nov 26, 2013 at 4:37 PM, Jon Bråte <jon.brate@ibv.uio.no> wrote:

> Hi everyone,
>
> I have a count matrix of FPKM values and I want to estimate differentially
> expressed genes between two conditions. First I used DESeq2, but I realized
> that this is not good for FPKM values.
> I then transformed the counts using voom() in the limma package and then
> used:
>
> fit <- lmFit(myVoomData,design)
> fit <- eBayes(fit)
> options(digits=3)
> writefile = topTable(fit,n=Inf,sort="none", p.value=0.01)
> write.csv(writefile, file="file.csv")
>
> My problem is that all of the 6156 genes are differentially expressed
> (p-value 0.01). Only a few hundred were differentially expressed using
> DESe2, but I guess that can't be trusted.
>
> I am new to this type of analysis, and to R, but is it ok to simply
> transform the data by voom()? Can I use the transformed data in DESeq2? Any
> other ways I can use FPKM counts to estimate differentially expressed genes?
>
> Thank you,
>
> Jon Bråte
>
>
>
> > sessionInfo()
> R version 3.0.2 (2013-09-25)
> Platform: x86_64-apple-darwin10.8.0 (64-bit)
>
> locale:
> [1] C
>
> attached base packages:
> [1] grid      parallel  stats     graphics  grDevices utils     datasets
>  methods   base
>
> other attached packages:
>  [1] limma_3.18.3         cummeRbund_2.4.0     Gviz_1.6.0
> rtracklayer_1.22.0   GenomicRanges_1.14.3 XVector_0.2.0
>  [7] IRanges_1.20.6       fastcluster_1.1.11   reshape2_1.2.2
> ggplot2_0.9.3.1      RSQLite_0.11.4       DBI_0.2-7
> [13] BiocGenerics_0.8.0
>
> loaded via a namespace (and not attached):
>  [1] AnnotationDbi_1.24.0   BSgenome_1.30.0        Biobase_2.22.0
> Biostrings_2.30.1      Formula_1.1-1
>  [6] GenomicFeatures_1.14.2 Hmisc_3.13-0           MASS_7.3-29
>  RColorBrewer_1.0-5     RCurl_1.95-4.1
> [11] Rsamtools_1.14.2       XML_3.95-0.2           biomaRt_2.18.0
> biovizBase_1.10.4      bitops_1.0-6
> [16] cluster_1.14.4         colorspace_1.2-4       dichromat_2.0-0
>  digest_0.6.3           gtable_0.1.2
> [21] labeling_0.2           lattice_0.20-24        latticeExtra_0.6-26
>  munsell_0.4.2          plyr_1.8
> [26] proto_0.3-10           scales_0.2.3           splines_3.0.2
>  stats4_3.0.2           stringr_0.6.2
> [31] survival_2.37-4        tools_3.0.2            zlibbioc_1.8.0
>
>
> ----------------------------------------------------------------
> Jon Bråte
>
> Microbial Evolution Research Group (MERG)
> Department of Biosciences
> University of Oslo
> P.B. 1066 Blindern
> N-0316, Norway
> Email: jon.brate@ibv.uio.no
> Phone: 922 44 582
> Web: mn.uio.no/ibv/english/people/aca/jonbra/index.html
>
>
>
>
>
>         [[alternative HTML version deleted]]
>
>
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