
Hi Davis/Group:

I have a question when I was trying edgeR for the common.lib.size and the normalized counts for each library.  My RNA-seq data consists of seven conditions with two replicates each.  As the user's guide indicates the common.lib.size is expected for each library (approximately!), but what I got are quite different from each other. 

countsTable <- read.delim("Deep_seq_data.csv", header=T, stringsAsFactors=T)
d<-countsTable[, -1]     # Remove the first column which is FeatureID as AGI number
conditions<-rep(c("Zygote", "Octant", "Globular", "Heart", "Torpedo", "Bent", "Mature"), each=2)
d<-DGEList(counts=countsTable[,-1], group=conditions)
dim(d)     #check the counts

# Filter genes with >=1 counts per million, in at least 2 samples
d<-d[rowSums(1e+06*d$counts/expandAsMatrix(d$samples$lib.size, dim(d))>=1)>=2,]

dim(d)            #get the gene number satisfying the above condition

d<- calcNormFactors(d)

d$samples$lib.size
# "Zygote", "Zygote", "Octant", "Octant", "Globular", "Globular", "Heart", "Heart", "Torpedo", "Torpedo", "Bent", "Bent", "Mature", "Mature"
# 21012147 19924212   26002900  9660245    17139388    7649319    16430105  20101956 12920266   6306742    44241095 20094409 15166090 23203758

d$common.lib.size                   
[1] 16554344.47

colSums(d$pseudo.alt)
# Zygote1     Zygote2     Octant1     Octant2   Globular1   Globular2      Heart1      Heart2    Torpedo1    Torpedo2  
# 21523774.62 21638415.63 14533481.82 12046955.46 18920316.62 18439528.30 11754608.30 12759230.11 11248245.52 11410667.92 
# Bent1       Bent2     Mature1     Mature2 
# 16101723.65 17980670.24 26785396.02 27067289.80 

It looks within each condition (i.e. between replicates), the normalized counts are quite similar, but not between the conditions. 
I have tried different normalizations (TMM, quantile and RLE), the results are quite similar.  Also I tried to only include two conditions with 2 replicates each, say the Zygote and Octant, the result are the same.  Not sure if I missed anything, and I appreciate if you could have a look at my situation. 

Thanks a lot!

Yifang

Yifang Tan
 		 	   		  
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