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I was reading this manuscript https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287564/

They wrote that they have done statistical test and found that Wald test is best when there is no replicate I want to know if anyone could comment how to do this?

On the other hand, I have been reading a lot on web and everybody says something, one talk about permutation test, one says t test between two, one says fisher test , ......

I did something like this

I made a toy data where the first column is control and second is treated

df<- structure(list(col1A = c(1.64, 0.03, 0, 4.202, 2.981, 0.055, 
0, 0.002, 0.005, 0, 0.002, 0.649, 2.55, 2.762, 6.402, 0.91, 0.037, 
0, 5.757, 3.916, 0.022, 0, 0, 0.003, 0, 0.262, 0.136, 2.874, 
3.466, 5.003), col2A = c(2.635, 0.019, 0, 5.638, 3.542, 0.793, 
0.259, 0, 0.046, 0.004, 0.017, 0.971, 3.81, 3.104, 5.849, 1.027, 
0.021, 0, 4.697, 2.832, 0.038, 0.032, 0.001, 0.003, 0, 0, 0.317, 
2.743, 3.187, 6.455)), row.names = c("A", "AA", "AAA", "Aab", 
"buy", "yuyn", "gff", "fgd", "kil", "lilk", "hhk", "lolo", "fdd", 
"vgfh", "nghg", "gdtd", "ayad", "terer", "quwte", "nshdg", "ahaf", 
"eqew", "tars", "nshdt", "andydv", "oalkd", "jayqgd", "nahdgd", 
"nagdd", "hdydy"), class = "data.frame")


group<-factor(c("C","T"))
design<-model.matrix(~group)
data<-DGEList(counts=df,group=group)
data<-estimateGLMCommonDisp(d,design,method="deviance",robust="TRUE",subset=NULL)
data$common.dispersion=0.4
res <- exactTest(data)

I also check this post which seems to be interesting Testing linear restriction in R

So all said, I have two samples one control and one treated microarray. I want to find which genes are significantly different. any comment?

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