> sessionInfo()
R version 3.2.4 (2016-03-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=Norwegian (Bokmål)_Norway.1252  LC_CTYPE=Norwegian (Bokmål)_Norway.1252    LC_MONETARY=Norwegian (Bokmål)_Norway.1252 LC_NUMERIC=C                              
[5] LC_TIME=Norwegian (Bokmål)_Norway.1252    

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

other attached packages:
[1] edgeR_3.2.4        limma_3.26.3       DESeq_1.12.1       lattice_0.20-33    locfit_1.5-9.1     Biobase_2.20.1     BiocGenerics_0.6.0

loaded via a namespace (and not attached):
 [1] IRanges_1.18.4       XML_3.98-1.1         grid_3.2.4           xtable_1.7-4         DBI_0.3.1            stats4_3.2.4         RSQLite_0.11.4       genefilter_1.42.0   
 [9] annotate_1.38.0      RColorBrewer_1.0-5   tools_3.2.4          geneplotter_1.38.0   survival_2.38-3      AnnotationDbi_1.22.6
>

library(edgeR)

## Read in the data making the row names the first column
counttable <- read.table("allcounts.txt", header=T, row.names=1)

## Make metadata data.frame
meta <- data.frame(
  row.names=colnames(counttable),
  condition=c("Bad","Good")                               #repeated until n=5/4 "Good" and 5/6 "Bad" for Landrace/Duroc, according to counttable
meta$condition <- relevel(meta$condition, ref="Good")

## Make design matrix
condition <- relevel(factor(meta$condition), ref="Good")
edesign <- model.matrix(~condition)

## Make new DGEList, normalize by library size, and estimate dispersion allowing possible trend with average count size
e <- DGEList(counts=counttable)

dim(e)
keep <- rowSums(cpm(e)>1) >=n/2        #filtering: keep genes that achieve at least one count per million in at least half of the samples
e <- e[keep,]
dim(e)
e$samples$lib.size <- colSums(e$counts)

e <- calcNormFactors(e, method="TMM")
e <- estimateGLMCommonDisp(e, edesign)
e <- estimateGLMTrendedDisp(e, edesign)
e <- estimateGLMTagwiseDisp(e, edesign)

## Fit the model, testing the coefficient for the Good vs Bad comparison
efit <- glmFit(e, edesign)
efit <- glmLRT(efit, coef="conditionBad")

## Make a table of results
etable <- topTags(efit, n=nrow(e))$table
etable <- etable[order(etable$FDR), ]