[Michael Love]

For making comparisons of multiple conditions (not only against the base level of a condition), we have recently implemented contrasts in the development branch. This allows one to fit a single model, then generate log2 fold change estimates, standard errors and tests of null hypotheses for other comparisons.

The functionality is described in section 3.2 of the vignette, 'Contrasts' and in the man page for ?results, for DESeq2 version 1.1.x which is paired with Bioc 2.13.

http://bioconductor.org/packages/2.13/bioc/vignettes/DESeq2/inst/doc/DESeq2.pdf

You can either try using the devel branch of Bioconductor...

http://bioconductor.org/developers/how-to/useDevel/

or this version will be released in Oct 15, 2013:

http://bioconductor.org/developers/release-schedule/

[haggardd]

Hello,

DESeq was great in that the nbinomTest() function allowed for me to specify which conditions I wanted to run the test on. However, DESeq2 appears to not have this functionality. I have data that was run through HTSeq and so am using the DESeqDataSetFromHTSeqCount() function. I am analyzing data that underwent two different exposure concentrations at different percentages of vehicle (i.e. 1% vehicle control, 10uM chemical in 1% vehicle, 0.1% vehicle control, and 1uM chemical in 0.1% vehicle). There are four total conditions for this experiment and I want to compare the expression between pairs with the same percentage of vehicle (i.e. 1% vehicle control versus 10uM chemical in 1% vehicle). However, when I run DESeq() the resultsNames() I get to choose from are only from four comparisons and not the pairwise comparisons I need. Is this possible in DESeq2 as it was in the original DESeq to specify which conditions I want to test? Or would it just be easier to initially have the comparisons I want to analyze already separated instead of how I am doing it currently? Thoughts?

Thanks!

[zhang51]

I figured it out. I did not run DESeq function first before making this plot. Now it works.

Thanks

[zhang51]

To Simon,

I'm running code in the file http://www.bioconductor.org/packages...doc/DESeq2.pdf

I encountered the same problem when running meanSdPlot function. It works only when normalized=FALSE. There is an error message when using normalized=TRUE. Please see below:

In is.na(sizeFactors(object)) :
is.na() applied to non-(list or vector) of type 'NULL'
Error in meanSdPlot(log2(counts(dds, normalized = TRUE)[notAllZero, ] + :
error in evaluating the argument 'x' in selecting a method for function 'meanSdPlot': Error in .local(object, ...) :
first calculate size factors, add normalizationFactors, or set normalized=FALSE

What would be the solution to this problem?

Thanks

[dpryan]

Dear Simon and everyone,

I have downloaded DESeq2 package with no trouble. But when I tried to run your example R code, I got trouble even with the first line "dds <- DESeqDataSet(se = se, design = ~ condition)". The error message is "error in evaluating the argument 'x' in selecting a method for function 'assays': Error: object 'se' not found".

I have run "library(DESeq2)" at the very beginning. I wonder what else I should do to make it work?

Assuming you're following the vignette, you missed a few lines, which would have setup "se".

library("parathyroidSE")
data("parathyroidGenesSE")
se <- parathyroidGenesSE
colnames(se) <- colData(se)$run

Edit: Just go to the next page of the vignette, I expect what you're looking at is the "Quick Start" section.

[Simon Anders]

Which example code are your running, i.e., what documentation are you reading?

[zhang51]

Dear Simon and everyone,

I have downloaded DESeq2 package with no trouble. But when I tried to run your example R code, I got trouble even with the first line "dds <- DESeqDataSet(se = se, design = ~ condition)". The error message is "error in evaluating the argument 'x' in selecting a method for function 'assays': Error: object 'se' not found".

I have run "library(DESeq2)" at the very beginning. I wonder what else I should do to make it work?

I used R-3.0.1 for the installation.

Thanks in advance.

Jenny

[trpc]

Hi
This is my DESeqDataSet object

> Genes
class: DESeqDataSet
dim: 21937 4
exptData(0):
assays(1): counts
rownames(21937): 0610007C21Rik 0610007L01Rik ... Zzef1 Zzz3
rowData metadata column names(0):
colnames(4): 13-5.bam 13-5-5.bam E15-2.sorted.bam E15-5.sorted.bam
colData names(2): fileName E13E15

Since I want to test only "13-5.bam" and "13-5-5.bam", so I set colData(Genes)$E13E15 like this:

>colData(Genes)$E13E15 <- factor(colData(Genes)$E13E15,levels=c("E13WT","E13KO"))
> colData(Genes)
DataFrame with 4 rows and 2 columns
fileName E13E15
<BamFileList> <factor>
13-5.bam ######## E13WT
13-5-5.bam ######## E13KO
E15-2.sorted.bam ######## NA
E15-5.sorted.bam ######## NA

However, when I change the DESeqDataSet object into Deseq object, it reports error:

> Genes<-DESeq(Genes)
estimating size factors
estimating dispersions
same number of samples and coefficients to fit, estimating dispersion by treating samples as replicates
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting generalized linear model

error: element-wise multiplication: incompatible matrix dimensions: 4x1 and 2x1

Error:

[FLYINGDOLPHIN]

Hi,

Does deseq development version better than the release version? The manual that i found on Bioconductor seems to aim for the development version since the "normalize=True" does not work. I am just wondering whether it is worthwhile to switch to a development version and where to find it?

Thanks.
Q

[Heisman]

As said above, you guys do great things. Question; do you believe that this package would be good for determining differentially methylated regions from enrichment data (ie, MBD-seq or MeDIP-seq)? I know there are some papers using DESeq for this purpose, so I imagine so, but I was curious if you have any specific thoughts or caveats in mind.

[FLYINGDOLPHIN]

Thanks!
Q

[Simon Anders]

If you have an old R, the BiocLite script will pull matching old Bioconductor packages. DESeq2 is new, and while you could install it manually on an old R, the recommended way is to use R 3.0.0, and then biocLite will find and install it automatically.

[FLYINGDOLPHIN]

oh, it is a R package from the core installation.
R version 2.11.1, biocinstall version 2.6.10.

Thanks.
Q

[chadn737]

I would first try making sure R is up to date.

[FLYINGDOLPHIN]

Hi,

I'd love to install deseq2. But I have a problem in installing in.


> source("http://bioconductor.org/biocLite.R")
BioC_mirror = http://www.bioconductor.org
Change using chooseBioCmirror().
> biocLite("DESeq2")
Using R version 2.11.1, biocinstall version 2.6.10.
Installing Bioconductor version 2.6 packages:
[1] "DESeq2"
Please wait...

Warning message:
In getDependencies(pkgs, dependencies, available, lib) :
package ‘DESeq2’ is not available

I have no problem in installing deseq though.

Thanks a lot!

Q