If you use the "getOffset" function for your DGEList object and the

following function for your CountDataSet object, you will get offset

values that are directly comparable:

library(DESeq)

library(edgeR)

library(ggplot2)

getOffset.CountDataSet <- function(y) {

if (any(is.na(sizeFactors(y))))

stop("Call estimateSizeFactors first")

log(sizeFactors(y)) - mean(log(sizeFactors(y))) +

mean(log(colSums(counts(y))))

}

cds <- makeExampleCountDataSet()

cds <- estimateSizeFactors(cds)

dge <- DGEList(counts=counts(cds), group=pData(cds)$condition)

dge <- calcNormFactors(dge)

qplot(x=getOffset(dge), y=getOffset.CountDataSet(cds)) +

labs(title="Offsets, DESeq vs edgeR",

x="edgeR offset", y="DESeq offset") +

coord_equal() +

geom_abline(slope=1, intercept=0)

On Fri 15 Mar 2013 11:58:11 AM PDT, Simon Anders wrote:Hi Lucia

On 15/03/13 16:43, Lucia Peixoto wrote:

I am currently analyzing an RNASeq dataset, I have 3 samples with n=4

each.

I was exploring the performance of both EdgeR and DeSeq and I noticed

they

vary a lot on the dispersion of the normalization factors.

Using EdgeR calcNormFactors I get a distribution that varies from

0.9-1.2

while if I use DeSeq estimateSizeFactors the distribution varies from

0.4-1.7. Given that these are exactly the same libraries

why do the estimates vary so much? How will that impact the list of

DEgenes?

I know that the calculations are not performed in the same way, but

aren't

those two functions aimed at estimating the same phenomenon?

EdgeR's library factors are relative to the total read count, and

DESeq's aren't. Do, if you want to compare them, you have to multiply

the factors from edgeR with the total read counts and divide by some

suitable big number.

So, if sf is vector of size factors from DESeq, nf is a vector of

normalization factors from edgeR, and rs is the vector with the column

sums of the count matrix, I would expect that

plot( sf, rs * nm )

gives a plot with the points lying roughly on a straight line.

Simon

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