Hi, 

I am using following code to analyse two samples and differentially expressed gene in them. there are 3 replicates for each sample. after completing analysis I am seeing all the logFC values in negative, I don't know why? I actually went back and checked the raw count for them and they seem wrong. can you please tell me if I am doing anything wrong? also why pvalue is 0 (zero)?  Thank you 

here is the code that I am using -

#data_import
raw.data<-read.table("bcytvsgut.count",header=TRUE)

group<-c("Bcyt","Bcyt","Bcyt","mgut","mgut","mgut")
#make DGE list
cds<-DGEList(raw.data,group=group)
names(cds)        
head(cds$counts)
cds$samples
#normalize data using TMM
cds <- calcNormFactors( cds )
d$samples
#data filtering
cps <- cpm(cds)
k <- rowSums(cps>=10)>3
d <- cds[k,]

#reset the library size
d$samples$lib.size <- colSums(d$counts)
cols <- as.numeric(d$samples$group)
par(mfrow=c(2,2))

plotMDS(d,col=cols)

#setup desing
design <- model.matrix(~0+group, data=d$samples)
design

d <- estimateGLMCommonDisp(d,design,verbose=T)
d <- estimateGLMTrendedDisp(d,design)
d <- estimateGLMTagwiseDisp(d,design)
plotBCV(d)

plotMeanVar(d,show.raw=TRUE, show.tagwise=TRUE, show.binned=TRUE)
#calculate differentially expressed genes
fit <- glmFit(d,design)
lrt <- glmLRT(fit)

topTags(lrt)
names(lrt)
lrt$comparison
head(lrt$table)
#export data
saveme<-topTags( lrt, n = Inf , sort.by = "none" )
write.table(saveme,file="EdgeR_bcytvsgut_hiseq.txt",sep="\t",quote=F)
plotSmear(d)
abline(h = c(-2, 2), col = "blue")

ambiguities that i am seeing between data - 

Raw data - 

You're using a design matrix without an intercept. This means that the first column/coefficient represents the average log-expression across the "Bcyt" group, while the second column represents the average log-expression across the "mgut" group. By default, glmLRT will drop the last coefficient in the design matrix. This is equivalent to testing for whether the average log-expression across all "mgut" samples is equal to zero. This will not be true for the vast majority of genes, resulting in many genes with very low p-values and negative log-FC's (presumably, the average log-expression for these genes is less than zero when adjusted for library size).

I'm guessing that you actually want to test for differential expression between "Bcyt" and "mgut" groups. If so, you need to define a contrast vector. For simplicity, I'll redefine your design matrix here:

grouping <- factor(group)
design <- model.matrix(~0+grouping)
colnames(design) <- levels(grouping)

Your contrast vector can be defined with makeContrasts, as shown below:

con <- makeContrasts(Bcyt - mgut, levels=design)

This can be supplied to the contrast= argument of glmLRT to test for DE between groups. The log-fold changes in the resulting DE list represent the change in expression of the "Bcyt" group over the "mgut" group.