[BioC] DESeq2

aroa [guest] guest at bioconductor.org
Fri Apr 4 17:30:13 CEST 2014

We have and experiment to measure the differences in the milk transcriptome for two breeds. We have RNA-seq samples for these two breeds in 4 different time points, for each breed and time point we have three replicates.

	Day1	Day2	Day3	Day4
Breed1	3	3	3	3
Breed2	3	3	3	3

We want to use DESeq2 to extract the differential expressed (DE) genes for each time point between the two breeds and the DE genes for each breed comparing the different time points.
We have tested for the interaction (~breed+day+breed:day) and we cannot find interaction between the breeds.
Now we are running the model like this:
design=data.frame(row.names = colnames(milk), breed = c("breed1", "breed1", "breed1","breed2","breed2","breed2","breed1","breed1","breed1","breed2","breed2","breed2","breed1","breed1","breed1","breed2","breed2","breed2","breed1","breed1","breed1","breed2","breed2","breed2"), day = c("D1","D1","D1","D1","D1","D1","D2","D2","D2","D2","D2","D2","D3","D3","D3","D3","D3","D3","D4","D4","D4","D4","D4","D4"))
dds<- DESeqDataSetFromMatrix(countData= milk, colData= design, design= ~ breed + day)
dds$breed<- factor(dds$breed, levels=c("breed1","breed2"))
dds$day<- factor(dds$day, levels=c("D1","D2","D3","D4"))
dds<-DESeq(dds, betaPrior=FALSE)
[1] "Intercept"             "breed_breed2_vs_breed1" "day_D2_vs_D1"      
[4] "day_D3_vs_D1"       "day_D4_vs_D1"

We would like to know what is the meaning of the resultsNames?, we understand them like this:
Intercept: breed1D1
breed_breed2_vs_breed1: breed2-breed1
day_D2_vs_D1: (D2-D1)breed1
day_D3_vs_D1: (D3-D1)breed1
day_D4_vs_D1: (D4-D1)breed1

And how we can make the contrast to obtain the results that we want, that it is comparing the different breeds in each time point and the different time points in each breed?
Thank you in advance.

 -- output of sessionInfo(): 

R version 3.0.3 (2014-03-06)
Platform: i386-w64-mingw32/i386 (32-bit)

[1] LC_COLLATE=French_France.1252  LC_CTYPE=French_France.1252   
[3] LC_MONETARY=French_France.1252 LC_NUMERIC=C                  
[5] LC_TIME=French_France.1252    

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gplots_2.12.1             RColorBrewer_1.0-5        DESeq2_1.2.10            
 [4] RcppArmadillo_0. Rcpp_0.11.1               GenomicRanges_1.14.4     
 [7] XVector_0.2.0             IRanges_1.20.7            BiocGenerics_0.8.0       
[10] BiocInstaller_1.12.0     

loaded via a namespace (and not attached):
 [1] annotate_1.40.1      AnnotationDbi_1.24.0 Biobase_2.22.0       bitops_1.0-6        
 [5] caTools_1.16         DBI_0.2-7            gdata_2.13.2         genefilter_1.44.0   
 [9] grid_3.0.3           gtools_3.3.1         KernSmooth_2.23-10   lattice_0.20-27     
[13] locfit_1.5-9.1       RSQLite_0.11.4       splines_3.0.3        stats4_3.0.3        
[17] survival_2.37-7      tools_3.0.3          XML_3.98-1.1         xtable_1.7-3

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