DESeq2, no significantly DE genes
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@a-14337
Last seen 8 weeks ago
United Kingdom

Hi all, 

 

Was wondering if somebody could provide some input. I am struggling to look at DE using DESeq2 with data from a count matrix! I am applying the following code and I get no significant results whatsoever. I think that the creation of the coldata is incorrect. Here is my code for the design and then the output of DE (which shows no significance). I have looked around but cannot find the answer to my problem. Any input would be greatly appreciated and I will be happy to add any other code required. 

 

countdata <- RNAseq[ ,3:ncol(RNAseq)]
countdata<-as.matrix(countdata)
storage.mode(countdata) = "integer"
head(countdata)
colnames(countdata)
condition<- factor(c(rep("con1", 5), rep("con2", 7), rep("con3", 7)))
treatment<-factor(c(rep("WT", 5), rep("genot1", 7), rep("genot2", 7)))
coldata <- data.frame(row.names=colnames(countdata), condition, treatment)
coldata

condition treatment
SJMMNORM016986_G1      con1        WT
SJMMNORM016994_G1      con1        WT
SJMMNORM016996_G1      con1        WT
SJMMNORM016997_G1      con1        WT
SJMMNORM016999_G1      con1        WT
SJMMNORM016977_G1      con2    genot1
SJMMNORM016978_G1      con2    genot1
SJMMNORM016979_G1      con2    genot1
SJMMNORM016983_G1      con2    genot1
SJMMNORM016985_G1      con2    genot1
SJMMNORM016989_G1      con2    genot1
SJMMNORM016995_G1      con2    genot1
SJMMNORM016981_G1      con3    genot2
SJMMNORM016982_G1      con3    genot2
SJMMNORM016984_G1      con3    genot2
SJMMNORM016988_G1      con3    genot2
SJMMNORM016990_G1      con3    genot2
SJMMNORM016992_G1      con3    genot2
SJMMNORM016993_G1      con3    genot2

dds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design= ~treatment)dds

dds <- DESeq(dds)

res <- results(dds)

                     baseMean log2FoldChange     lfcSE          stat    pvalue      padj
                    <numeric>      <numeric> <numeric>     <numeric> <numeric> <numeric>
ENSMUSG00000064052  0.0000000             NA        NA            NA        NA        NA
ENSMUSG00000037169  2.2600347     1.10287749 0.6880776  1.6028387331 0.1089703 0.9998955
ENSMUSG00000077976  0.1544783     0.00299096 3.8710595  0.0007726463 0.9993835 0.9998955
ENSMUSG00000086031  0.0000000             NA        NA            NA        NA        NA
ENSMUSG00000000197  0.1026481    -1.15111679 3.8710595 -0.2973647864 0.7661880 0.9998955
...                       ...            ...       ...           ...       ...       ...
ENSMUSG00000093086 0.00000000             NA        NA            NA        NA        NA
ENSMUSG00000065511 0.00000000             NA        NA            NA        NA        NA
ENSMUSG00000076628 0.05757301    0.002998166   3.87106  0.0007745079  0.999382 0.9998955
ENSMUSG00000076626 0.05757301    0.002998166   3.87106  0.0007745079  0.999382 0.9998955
ENSMUSG00000077841 0.00000000             NA        NA            NA        NA        NA

Many thanks again!

deseq2 differential expression adjusted pvalue • 536 views
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@mikelove
Last seen 23 hours ago
United States

You should use “contrast” to see if there are differences between the first genotype and WT. See ?results

It is possible that there are not differences in expression though. Also check plotPCA. Have you seen our workflow? It’s linked from the abstract of the vignette and helps new users.

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Entering edit mode

Many thanks! and yep, was just going to these steps (PCA specifically) to check for variability, but decided to post as, on excel a colleague is getting significantly expressed genes between groups, hence why I thought, my design of coldata was fundamentally wrong and hence the "design" was not actually comparing between the 3 groups of replicates? con1 vs con2, con2 vs con3 etc. 

 

I am indeed following the workflow, just stopped after results(dds) as I am really concenred by the lack of significance (literally not a single gene)...

 

Many thanks again!

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