[BioC] EdgeR annotation

[Gordon K Smyth]

Dear Robin,

I don't think the problem is with edgeR.  You are setting

   genes=data[,0:1]

However there is no column 0, so 0:1 reduces to column 1, which is being 
set as annotation.  But you also input column 1 as a count:

   counts=data[,1:18]

These can't both be right.

I suspect you just want:

   y <- DGEList(counts=data)

Best wishes
Gordon

----- original message ------
[R] EdgeR annotation
Robin Mjelle robinmjelle at gmail.com
Sat Aug 24 13:50:55 CEST 2013

after updating R and edgeR I lost the annotations in the final
Diff.Expressed matrix (toptags) when running the edgeR pipeline. How do I
get the row.names from the data matrix into the topTag-matrix?

data <- read.table("KO_and_WT_Summary_miRNA_Expression.csv", row.names=1,
sep="", header=T)


keep <- rowSums(cpm(data)>2) >=2
data <- data[keep, ]
table(keep)
y <- DGEList(counts=data[,1:18], genes=data[,0:1])
y <- calcNormFactors(y)
y$samples
plotMDS(y,main="")
Time=c("0.25h","0.5h","1h","2h","3h","6h","12h","24h","48h","0.25h","0.5h","1h","2h","3h","6h","12h","24h","48h")
Condition=c("KO","KO","KO","KO","KO","KO","KO","KO","KO","WT","WT","WT","WT","WT","WT","WT","WT","WT")
design <- model.matrix(~0+Time+Condition)
rownames(design) <- colnames(y)
y <- estimateGLMCommonDisp(y, design, verbose=TRUE,
method="deviance",robust=TRUE, subset=NULL)
y <- estimateGLMTrendedDisp(y, design)
y <- estimateGLMTagwiseDisp(y, design)
fit <- glmFit(y, design)
lrt <- glmLRT(fit)
topTags(lrt)

Coefficient:  ConditionWT
      genes      logFC    logCPM        LR PValue FDR
189   5128 -11.028422  7.905804  4456.297      0   0
188  12271 -10.582267  9.061326  5232.075      0   0
167 121120  -9.831894 12.475576  5957.104      0   0
34  255235  -9.771266 13.592968  7355.592      0   0
168 311906  -9.597952 13.907951 10710.111      0   0
166 631262  -9.592550 14.932018 11719.222      0   0
79      79   9.517226 11.466696  7964.269      0   0
169   2512  -8.946429  6.758584  2502.548      0   0
448   3711  -7.650068  7.764682  2914.784      0   0
32  260769  -7.412197 13.633352  4906.198      0   0

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