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3 Control replicate and 2 test sample ,so Im comparing my Control which is my Progenitor cells to Monocyte which is mature stage

this is my code im using

condition <- factor(c(rep("Control", 3),rep("Test", 2)),levels=c("Control", "Test"))


(coldata <- data.frame(row.names=colnames(countdata), condition))
dds <- DESeqDataSetFromMatrix(countData=countdata, colData=coldata, design=~condition)

This is my deseq2 output

gene baseMean log2FoldChange lfcSE  stat pvalue padj CB1 CB2 CB3 GD5_1  GD5_2
ENSG00000144655 2030.6146124999 -5.0812043868   0.1183868766    -42.9203348727  0   0   3327.4890036209 3267.1877800456 3362.7407570783 98.5079578403   97.1475639145

Im not sure how come the pvalue and padj is 0? even though the sample value there is a difference and there is also fold change ,something wrong with the result ?

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    $\begingroup$ What's your sample design? How many biological reps do you have? $\endgroup$ – heathobrien Apr 18 '18 at 10:48
  • $\begingroup$ I have updated it $\endgroup$ – krushnach Chandra Apr 18 '18 at 11:00
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A p-value of 0 is expected in this case. The p-values are calculated by comparing the Wald stat to a standard normal, so you can confirm that this is correct like this:

> pnorm(-42.9203348727)
[1] 0

It really means p < 2.225074e-308 See here for a more details

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  • $\begingroup$ Yes i checked the post it has similar issue ,but pardon my ignorance but how come the deseq2 normalised value are so large CB1 CB2 CB3 GD5_1 GD5_2 3327.4890036209 3267.1877800456 3362.7407570783 98.5079578403 97.1475639145 yet these are not significant? can you explain me in simpler manner and relate with the biology , you can expand your current answer ..thank you in advance $\endgroup$ – krushnach Chandra Apr 18 '18 at 11:13
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    $\begingroup$ small p-values are more significant, so a p-values less than 2.3e-308 is very, very significant. It's as significant as you can get $\endgroup$ – heathobrien Apr 18 '18 at 11:34

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