[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.

[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

[zhang51]

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

Thanks

[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!

[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/

[NateP]

Glad to see contrasts appear in DESeq2, thanks!

One minor thing I noticed when running contrasts:
When you use the Cooks Outlier filtering on the base analysis, the outlier genes (with "NA" pvalues in the regular analysis) are no longer filtered when you do a contrast. I'm not sure if that is intentional or not?

[Michael Love]

You're right. Thanks for pointing this out.

I will work on a fix, probably moving the cooksCutoff argument to results() function, so it can be modified without rerunning the GLM fitting.

[Michael Love]

I committed this fix in version 1.1.31 of the devel branch. It made sense to move the outlier detection by Cook's distance as well as all p-value adjustment to the results() function.

[KHubbard]

baseMeanA and baseMeanB

Hello,

In DESeq, it was possible to view/export the baseMean values for each condition (baseMeanA and baseMeanB) in addition to the overall baseMean. In DESeq2, I have only been able to view/export the overall baseMean. Is it possible to acquire the baseMean values for each condition as well? Thanks!

[Michael Love]

We generalized the code, and as these columns are only appropriate for a 2 condition analysis, they were dropped.

You can get these with some custom code though:

baseMeanA <- rowMeans(counts(dds,normalized=TRUE)[,colData(dds)$condition == "A"])

More general:

baseMeanPerLvl <- sapply(levels(colData(dds)$condition), function(lvl) rowMeans(counts(dds,normalized=TRUE)[,colData(dds)$condition == lvl]))

[alisrpp]

Hi,

I'm new in DESeq2 and i'm having problems to create my dds.

I have a matrix with eXpress results (read counts) with 2 conditions (SP, EB) and 2 replicates/condition.

This is what i'm doing:

Cele_SPvsEB = read.csv (file.choose(), header=TRUE, row.names=1)
CeleDesign <- data.frame(
  row.names = colnames(Cele_SPvsEB),
  condition = factor(c("SP", "SP", "EB", "EB")))
dds <- DESeqDataSetFromMatrix(countData = Cele_SPvsEB,
                              colData = CeleDesign,
                              design = ~ condition)
dds

And i'm getting this error message:

Error in get(name, envir = asNamespace(pkg), inherits = FALSE) : 
  object '.setDummyField' not found

Any help?
Thanks.

[Michael Love]

Can you show the output of traceback()

and also can you show the output of head(Cele_SPvsEB)

[alisrpp]

Sure!

> traceback()
14: get(name, envir = asNamespace(pkg), inherits = FALSE)
13: methods:::.setDummyField
12: (function (value) 
    {
        if (missing(value)) 
            `.->data`
        else {
            methods:::.setDummyField(.self, ".->data", "SimpleList", 
                "data", FALSE, value)
            value
        }
    })(quote(<S4 object of class "SimpleList">))
11: assign(field, value, envir = env)
10: envRefSetField(.Object, field, classDef, selfEnv, elements[[field]])
9: methods::initRefFields(.Object, classDef, selfEnv, list(...))
8: initialize(value, ...)
7: initialize(value, ...)
6: methods::new(def, ...)
5: .ShallowSimpleListAssays$new(data = assays)
4: .local(assays, ...)
3: SummarizedExperiment(assays = SimpleList(counts = countData), 
       colData = colData, ...)
2: SummarizedExperiment(assays = SimpleList(counts = countData), 
       colData = colData, ...)
1: DESeqDataSetFromMatrix(countData = Cele_SPvsEB, colData = CeleDesign, 
       design = ~condition)

> head(Cele_SPvsEB)
                    C4_APR10_spermatic_cysts CRL_2APR10_spermatic_cysts CRL_1_15JUL11_embryos
comp100_c0_seq1                           21                         24                     0
comp100004_c0_seq1                        48                          0                   494
comp1000074_c0_seq1                       42                          0                    55
comp1000109_c0_seq1                       32                         18                     0
comp100011_c0_seq1                        40                          3                   298
comp100013_c0_seq1                        92                          0                    62
                    CRL_2_15JUL11_embryos
comp100_c0_seq1                         0
comp100004_c0_seq1                      0
comp1000074_c0_seq1                     0
comp1000109_c0_seq1                     0
comp100011_c0_seq1                      0
comp100013_c0_seq1                      0

[Michael Love]

Googling the setDummyField error I came to this thread

http://permalink.gmane.org/gmane.science.biology.informatics.conductor/48611

Where they suggest there might be a problem with using R 3.0.0 with packages built with R 3.0.1. Could this be the case? Can you paste your sessionInfo()

[alisrpp]

Done! I updated R and now is working perfecly.
Thanks Michael!