[AsoBioInfo]

Are you analyzing RNA-seq data? If so the overwhelming opinion of the community is that the poisson model of DEGseq is invalid and you should use edgeR or DESeq instead.

Thanks for your reply!

Yup.. it is RNA-seq data.... Okay, I'll try DESeq and edgeR

Thanks once again....

[Xi Wang]

Thanks Xi for your reply!

The output score data looks like this:
"GeneNames" "value1" "value2" "log2(Fold_change)"
00000000000000 6 10 -0.736 -0.643
11111111111111 68 69 -0.02 0.072
22222222222222 1 1 0 0.095
33333333333333 NA NA NA NA NA NA NA NA FALSE
44444444444444 NA NA NA NA NA NA NA NA FALSE

Note: There are other scores also.

The fold change is calculated for only three rows. Although the matrix is having all values since it is giving output the whole matrix. The commands I used are:

-> library(DEGseq)
geneExpFile <- "D:/data/MyData.txt"
geneExpMatrix1 <- readGeneExp(file=geneExpFile, geneCol=1, valCol=c(7,9,11))
geneExpMatrix2 <- readGeneExp(file=geneExpFile, geneCol=1, valCol=c(8,10,12))
write.table(geneExpMatrix1[1:13,],row.names=FALSE)
write.table(geneExpMatrix2[1:13,],row.names=FALSE)

-> layout(matrix(c(1,2,3,4,5,6), 3, 2, byrow=TRUE))
par(mar=c(2, 2, 2, 2))
DEGexp(geneExpMatrix1=geneExpMatrix1, geneCol1=1, expCol1=c(2,3,4,5,6), groupLabel1="Label1",
geneExpMatrix2=geneExpMatrix2, geneCol2=1, expCol2=c(2,3,4,5,6), groupLabel2="Label2",
method="MARS")

Hope this helps!

Thanks!

Hi, By reading your code, I guess you were going to compare gene expression levels for two groups, each having 3 replicates. The expression values for Group1 were of Columns 7,9,11 in your MyData.txt file; whilst values for Group2 were of Columns 8,10,12 of MyData.txt. Is that right? So far, I understand you did a 3 versus 3 comparison. However, in the line starting with DEGexp, it seems you performed a 5 versus 5 comparison, as you listed 5 columns for each group. Perhaps, you were confused by "layout". As I said before, layout is to format the output figure but has nothing to do with your data matrix.

Besides, I'd like to make it clear that DEGseq works well with technical replicates from the same experiment manipulation. It has been shown in our paper that the detection variance in technical replicates can be almost totally explained by Poisson models.
In Hardcastle et al 2010, DEGSeq has been shown to have a better performance than other tools compared in a real world dataset (Figure 5 of Hardcastle et al 2010). The choice of methods/tools is your decision, but you'd better have a more comprehensive understanding of these tools as well as your data.

Any further questions please let me know.

Ref:
Hardcastle, T.J. and Kelly, K.A. (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data, BMC Bioinformatics, 11, 422.

[amdic2]

Print q-value with SamWrapper

Dear all,
I am using the samWrapper function from DEGseq.
I would like to be able to get the q-values in the output of the method, as I need them in order to make a volcano plot. The problem is that for low q-values (e.g. 10e-4) samWrapper outputs "0". Can anybody help?
Thank you,
Anne-Marie

[Xi Wang]

Thanks for your question. The q-values are calculated by function in 'samr' package, and we didn't change anything regarding the calculation of q-values. You may have to add a small number (say 1e-6) to make your volcano plot work.

[a0909]

z-scores

Hi Sol, Thanks for using DEGseq.

In the output file, there are 2 columns for fold-change: "log2(Fold_change)" and "log2(Fold_change) normalized". log2(Fold_change) = log(value1/value2), and the normalized value is got from the normalized value1 and value2. From the value of fold-change, you can judge this gene is up-regulated or down-regulated. For example, for a gene if its log2(Fold_change) > 0, which means value1 > value2, and if its signature = TRUE, this gene is significantly down-regulated in condition 2. Also, you can look into z-scores.

Hope this helps.

Hello Xi,
It is regarding the last line of the quoted answer ("you can look into z-scores").I would like to know whether the Zscore >0 is equivalent to log2(Fold_change) > 0, implying the negative Zscores are the down regulated genes in the condition 2 (as per the example quoted in your answer).
I would appreciate your help.

Thanks

[Xi Wang]

Hello Xi,
It is regarding the last line of the quoted answer ("you can look into z-scores").I would like to know whether the Zscore >0 is equivalent to log2(Fold_change) > 0, implying the negative Zscores are the down regulated genes in the condition 2 (as per the example quoted in your answer).
I would appreciate your help.

Thanks

1) Zscore >0 is equivalent to log2(Fold_change) > 0
2) negative Zscores, nagative log2(Fold_change), then expression in condition 1 < that in condition 2, thus up-regulated in condition 2.