[BioC] [ReportingTools] and DESeq2 results, publish DESeqDataSet using resultsNames other than the default

[Dimitra Alexopoulou]

Dear all,

I've been happily using ReportingTools to publish an html report of my 
DESeq2 results so far having only two conditions to compare (i.e. WT to KO).
I now have one Ctrl condition and three different mutations (say Mut1, 
Mut2 and Mut3):

levels(condition) # my 4 different conditions
# [1] "Ctrl"  "Mut1" "Mut2" "Mut3"

dds # my DESeqDataset, including the results
# class: DESeqDataSet
# dim: 24791 12
# exptData(0):
# assays(3): counts mu cooks
# rownames(24791): ENSMUSG00000000001 ENSMUSG00000000028 ... 
ENSMUSG00000093778 ENSMUSG00000093789
# rowData metadata column names(31): baseMean baseVar ... deviance maxCooks
# colnames(12): Ctrl_1 Ctrl_2 ... Mut3_2 Mut3_3
# colData names(2): condition sizeFactor

resultsNames(dds) # the results I have from DESeq2 saved in the dds
# [1] "Intercept"               "condition_Mut1_vs_Ctrl" 
"condition_Mut2_vs_Ctrl" "condition_Mut3_vs_Ctrl"


When I want to create the html report of the DESeq2 results obtained, I 
do the following:

# Creating the html report using ReportingTools:
desReport <- HTMLReport(shortName = "Blah_over_Ctrl_DEGs",
                         title = 'Blah detailed results',
                         basePath = myOutdir,
                         reportDirectory = "./DEreports"
                         )
publish(dds, desReport, pvalueCutoff=0.1, factor = colData(dds)$condition)
finish(desReport)

The above returns by default results for the last coefficient from 
resultsNames, in my case: "condition_Mut3_vs_Ctrl".

Is there a way to by-pass this default behaviour and output either:
a. all results in one report, or
b. each one of the results in a separate report, specifying this somehow 
to publish(), e.g.:
publish(dds,name="condition_Mut2_vs_Ctrl", desReport, pvalueCutoff=0.1, 
factor=colData(dds)$condition)
Or do I need to create a new dds_Mut2_over_Ctrl, subsetting the initial 
dds (which I don't know how to do and this would be my next question, 
especially to the DESeq2 people).
I would like to avoid breaking the analysis into 3 comparisons from the 
beginning (meaning constructing 3 different DESeqDataSets and running 
the differential expression analysis on them).

Thanks in advance,
Dimitra

-- 
Dimitra Alexopoulou, PhD
Deep Sequencing Group - SFB655
Affiliation:
DFG Research Center for Regenerative Therapies (CRTD)
Biotechnology Center (Biotec)
Technische Universität Dresden

postal address:
Deep Sequencing Group - SFB655
c/o
DFG-Center for Regenerative Therapies Dresden
Cluster of Excellence / TU Dresden
Fetscherstraße 105
01307 Dresden

Phone: +49 (351) 458 82362
Email: dimitraa(at)biotec.tu-dresden.de
Web: http://www.biotec.tu-dresden.de/

we are supported by:
SFB 655, CRTD, Biotec, EU FP7, EFRE