Using One condition and 4 times, I have compared each Time point and got a little surprising results - all comparisons have same pvalues and padj. Can anyone tell me if what I am doing is correct and my doubt it valuable
Indeed, I also figure out that baseMean in between two time points should not be the same. But I have it. And I really don't know why?
script in DESeq2:
> # design
> ExpDesign <- data.frame(row.names=colnames(ko), condition = design)
> ExpDesign
condition.label condition.Time
KO.T1.1 KO T1
KO.T1.2 KO T1
KO.T1.3 KO T1
KO.T2.1 KO T2
...
.......
> ##DESeqDataSet
> bckCDS <- DESeqDataSetFromMatrix(countData = ko, colData=ExpDesign, design= ~condition.Time)
> ddsLRT= DESeq(bckCDS, test ="LRT", fitType='local', reduced=~1)
> resultsNames(ddsLRT)
> resultsNames(ddsLRT)
[1] "Intercept" "condition.Time_T2_vs_T1" "condition.Time_T3_vs_T1"
[4] "condition.Time_T4_vs_T1"
> ## by default first (T1) and last (T4) comparison
> resLRT=results(ddsLRT, cooksCutoff=F, independentFiltering = F)
> resLRT$symbol <- mcols(ddsLRT)$symbol
> head(resLRT[order(resLRT$pvalue),],2)
log2 fold change (MLE): condition.Time T4 vs T1
LRT p-value: '~ condition.Time' vs '~ 1'
DataFrame with 4 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
mmu-miR-145b 35.51911 -0.4172776 0.3513721 56.82489 2.800864e-12 3.431058e-09
mmu-miR-181a-2-3p 144.74966 -0.8433754 0.5111826 42.11996 3.783699e-09 1.792311e-06
> sum(resLRT$padj < 0.05)
[1] 203
> ## for each time point
> lfc_resKO_T2_T1 <- results(ddsLRT,cooksCutoff=F, independentFiltering = F, contrast=c("condition.Time","T2","T1"))
> head(lfc_resKO_T2_T1[order(lfc_resKO_T2_T1$pvalue),],2)
log2 fold change (MLE): condition.Time T2 vs T1
LRT p-value: '~ condition.Time' vs '~ 1'
DataFrame with 4 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
mmu-miR-145b 35.51911 -2.6590521 0.4283991 56.82489 2.800864e-12 3.431058e-09
mmu-miR-181a-2-3p 144.74966 -3.2386468 0.5431705 42.11996 3.783699e-09 1.792311e-06
> sum(lfc_resKO_T2_T1$padj < 0.05)
[1] 203
> lfc_resKO_T3_T2 <- results(ddsLRT,cooksCutoff=F, independentFiltering = F, contrast=c("condition.Time","T3","T2"))
> head(lfc_resKO_T3_T2[order(lfc_resKO_T3_T2$pvalue),],2)
log2 fold change (MLE): condition.Time T3 vs T2
LRT p-value: '~ condition.Time' vs '~ 1'
DataFrame with 4 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
mmu-miR-145b 35.51911 2.75662374 0.3913252 56.82489 2.800864e-12 3.431058e-09
mmu-miR-181a-2-3p 144.74966 3.08362802 0.4757690 42.11996 3.783699e-09 1.792311e-06
> sum(lfc_resKO_T3_T2$padj < 0.05)
[1] 203
Thank you and any advice?
•
written
by
bioinforupesh2009.au •
1 answer
It appears you still have not read the documentation, though you have been asked to do so on previous threads (1, 2). This question is specifically discussed in ?results. Type into your R console, ?results, and then read the details.
The information which answers your question is also printed in your code above. For each comparison, the results table prints out: LRT p-value: '~ condition.Time' vs '~ 1'. So the p-value is from the likelihood ratio test of a model including time information and one without (only fitting an intercept).
The base mean is over all samples in the dataset, not the subset of samples specified by 'contrast'.
•
written
by
Michael Love
Log in to answer this question.