Hi,
I have histopathologic response to neoadjuvant chemoradiation in 56 cancer samples. A total of 26 samples were classified as minor and 30 as major histopathologic responders (TRG1-2 and TRG4-5 respectively). I have done edgeR and wilcoxon test to find genes driving the difference of tumor samples of patients with major or minor response as below.
group= as.factor(c(rep ("TRG1-2",26), rep("TRG4-5", 30)))
> group
[1] TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2
[17] TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG1-2 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5
[33] TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5
[49] TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5 TRG4-5
Levels: TRG1-2 TRG4-5
> dim(df)
[1] 2560 56
> y <- DGEList(counts = df, group = condition)
> y <- estimateDisp(y)
Design matrix not provided. Switch to the classic mode.
> sqrt(y$common.dispersion)
[1] 0.6280918
> EdgeR <- exactTest(y)
> topTags(EdgeR)
Comparison of groups: TRG4-5-TRG1-2
logFC logCPM PValue FDR
PPBP -4.3340878 9.503884 3.564802e-11 9.125894e-08
CDK6 -1.5518198 8.712466 1.458599e-07 1.867006e-04
IL1B 1.7324695 9.178351 2.623373e-05 1.908504e-02
CXCL8 1.6455933 8.340310 3.129262e-05 1.908504e-02
EGR1 0.8468036 8.652308 4.432857e-05 1.908504e-02
IFIT2 0.8957873 7.535228 5.199642e-05 1.908504e-02
IL6 1.3926323 6.951407 5.218565e-05 1.908504e-02
BDNF 1.4176689 6.605966 7.471018e-05 2.134076e-02
PTGS2 1.4746062 8.352272 7.547266e-05 2.134076e-02
FOS 0.9891503 9.263358 8.336234e-05 2.134076e-02
And wilcoxon test as below
> library(GSALightning)
df1= cpm (df,log=TRUE)
> results <- wilcoxTest(df1,group, tests = "unpaired"))
There were 48 warnings (use warnings() to see them)
> head(results[,1:4])
p-value:up-regulated in TRG1-2 p-value:up-regulated in TRG4-5
ACTB 0.02007199 0.9799280
ATP5F1 0.51624724 0.4837528
DDX5 0.87211880 0.1278812
EEF1G 0.76612743 0.2338726
GAPDH 0.12111916 0.8788808
NCL 0.44491768 0.5550823
q-value:up-regulated in TRG1-2 q-value:up-regulated in TRG4-5
ACTB 0.9998235 0.9822301
ATP5F1 0.9998235 0.6930090
DDX5 0.9998235 0.4650225
EEF1G 0.9998235 0.5331378
GAPDH 0.9998235 0.9138647
NCL 0.9998235 0.7347522
>
The list of significant genes either up-regulated in TRG1-2 or TRG4-5 are 100% different with edgeR results. Please help me to know which results are wright and which is wrong
Thank you for any suggestion
Thanks a lot, this is edgeseq a sort of RNAseq that does not need RNA extraction. However I fed cpm normalized data after log by cpm function in edgeR into wilcoxon test and same group for edgeR. Is wilcoxon not wrong yet even with normalized read counts?
I saw people use mann withney for such data for I am not sure what to do
Thank you for any help
I used t test on normalized data but error saying no difference detected
This is very confusing. I don't recall seeing the cpm function in your original question.
Sorry, I just edited my post. I have used cpm log values for any t-test or non-parametric test