Dear Biostars,
I performed DEG analysis for mouse RNA-Seq data using DESeq2. I have the following problem. I hope you can help me. I have 3 different treatments and one control group. Each condition has 2 biological replicates. I have the following code.
## generate a metadata condition <- c('control','control','lif','lif','il6','il6', 'cytokine','cytokine').
sample <- c('con1','con2','lif1','lif2', 'il6.1','il6.2','cyto1','cyto2')
Metadata <- data.frame(sample,condition)
head(data)
con1 con2 lif1 lif2 il6.1 il6.2 cyto1 cyto2
ENSMUSG00000000001 3110 3141 3299 3148 3698 3214 3239 3402
I am not showing my codes to make dds object, as it is hard for me to format codes here. In summary, my code worked, and I tried to get results table using the above data with 8 samples. For result, I used the following code , by changing "il6" to other conditions.
IL6DEseqresult <- as.data.frame(results(dds, c("condition",
"il6","control"), alpha = 0.05))
However, when I performed the same analysis starting with less number of samples I got very different padj, which affected my significant gene lists. I mean I started by subsetting two contrasting groups, and then get the dds object with control, and lif group. In this was I just had four samples (control1, control2, lif1, lif2), and I repeated the same for other groups. I got very different padj value. So , I am not sure , which strategy is a correct way. Can you please help me?
Thank you
1 answer
Using different number of samples affects normalization and dispersion estimation, so seeing different results is normal and expected. Whether you want to make two separate dds objects or a single one depends on both the question you want to answer and on how the samples behave. If the samples are from the same experiment and behave similarily (e.g. in a PCA) then it is probably a good idea to make a single object and then use the contrast argument to get the pairwise comparisons you want. If the treatment groups behave very differently (like IL6 e.g. had huge dispersion and Lif had not) then it might make sense to conduct separate analysis.
I would start by checking how the data look. Make a single dds objects, run vst and conduct a PCA as described in the manual.
You can post the plot here if you want to get feedback.
I am sorry, this p-value problem might be the easy one, but I am as a biologist having difficulty explaining it to my PI(biologist). Here is my problem. I followed this code by changing treatment names.
res <- results( dds, contrast = c("treatment", "DPN", "Control") )
res
resSig <- res[ which(res$padj < 0.05 ), ]
So, my question is my PI is telling me for the significant gene I should have the same p-value, and padj in res and resSig. He thinks we could also just filter the data by p-value or padj value on Excel. I ran result function a few times , but I do not get the same p-value and padj for the sig gene. How can I explain this ? or as he is saying am I doing something wrong here ? Thank you for your help.
Hello, Here is my question. After I get significant gene list with padj < 0.05 cutoff, I see that stats(stat, p-value, padj )have changed. But, I was expecting that I should have the same stats for the same gene, and I just filtered these genes out of whole genes just based on padj value. Did I misunderstand here ? Is there anything wrong in getting significant genes just based on padj ?
Thank you very much.
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it's not that hard though: you can find it in the formatting bar (the
codeoption) Select your code and press that button.