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

Dear Jon,

No, it is absolutely not ok to input FPKM values to voom. The results will be nonsense. Same goes for edgeR and DESeq, for the reasons explained in the documentation for those packages.

Note that a matrix of FPKM is not a matrix of counts.

It seems to me that you have three options in decreasing order of desirability:

1. Get the actual integer counts from which the FPKM were computed and do a proper analysis, for example using voom or edgeR.
2. Get the gene lengths and library sizes used to compute the FPKM and convert the FPKM back to counts.
3. If FPKM is really all you have, then convert the values to a log2 scale (y = log2(FPKM+0.1) say) and do an ordinary limma analysis as you would for microarray data, using eBayes() with trend=TRUE. Do not use voom, do not use edgeR, do not use DESeq. (Do not pass go and do not collect $200.) This isn't 100% ideal, but is probably the best analysis available. You make this method somewhat better by using arrayWeights() as well which, in this context, will attempt to estimate the library sizes the FPKMs were originally computed from. Nevertheless, the mean-variance trend estimated by limma from the logFPKMs will never be as smooth or as informative as the trend that would have been estimated had you had the real counts.

The third option is similar to the "limma-trend" analysis described in the limma preprint, except that it is applied to the logFPKM instead of logCPM. Statistically this will not perform as well as it would applied to the logCPM.

Best wishes
Gordon