Can be possible to analyze in multiple sample without replicated in edgeR

Question

[jimmy0958073736]

Hi everyone I met some confusing issues in edgeR. and DEG Right now I have three samples without any replicated. I wonder to know to have any possibility to combine three samples for analysis (With the replicated sample, I used DEseq2 than before but right now I don't have any idea to analyze)

data <- read.csv("Vero.D2.normalisedReadCounts.csv")
bcv <- 0.2
 counts <- data
    y <- DGEList (counts=counts, group=1:2)
    et <- exactTest(y, dispersion=bcv^2)

If I can adjust upon code to achieve my goal.

[jared.andrews07]

So what's your question? What error/issue are you running into here?

This has been asked many times before on both Biostars and the Bioconductor support forum, so there are plenty of examples to view if you search for them. I presume you got this code from the edgeR manual.

[jimmy0958073736]

Hi Jared Sorry I didn't mention my question. Can I use upon DEG code to analyze in different three samples? And how I adjust the code?

[jared.andrews07]

Did you view the linked question? Have you read the edgeR User's Guide, in particular section 2.12? It is pretty explicit.

We cannot help you without a clear idea of what exact issues you are having. No matter what you do, this will be a descriptive analysis and the p-values will be largely meaningless.

Answer 1

[jimmy0958073736]

Hi Jared, thank you. After I read the manual, all the questions were fixed. With the three samples and no replicated result, I follow the manual to adjust the following code.

count <- read.table("D:/NDM-12-4/test/Vero.CH.CH-C100.normalisedReadCounts.txt", sep = "\t", header = T, row.names = 1)
bcv <- 0.2
counts <- matrix(rnbinom(39399, size=1/bcv^2, mu=10), 13133, 3)
rownames(counts) <- rownames(count)
y <- DGEList (counts=counts, group=1:3)
et <- exactTest(y, dispersion=bcv^2)

and the result shows

                    logFC       logCPM    PValue
ENSCSAG00000000002  0.16623377  6.386070  1.000000000
ENSCSAG00000000004  0.01619722  6.385977  1.000000000
ENSCSAG00000000006  -0.24474816 6.895967  0.788276775
ENSCSAG00000000007  -0.84486125 6.248021  0.403427191
ENSCSAG00000000008  0.23429072  6.248550  1.000000000

Also normalize result

                    Sample1 Sample2 Sample3
ENSCSAG00000000002       9      10       8
ENSCSAG00000000004       9       9       9
ENSCSAG00000000006      18      15       8
ENSCSAG00000000007      11       6       7
ENSCSAG00000000008       6       7      11

Thank you so much.