Is it p value threshold or adj p value threshold?
I am doing microarray data analysis using R package. My doubt is regarding selection of DEG. Should I consider p VALUE < 0.05 or ADJUSTED p VALUE < 0.05 for selection of DEG? I get adj p values >0.3. What should I do now? On what basis the threshold of p value or adj p value should be set?
2 answers
You should use the adjusted p-value to account for multiple testing. You should also know what kind of adjustment was done, be it FDR or bonferroni. There is no absolute "best" p-value threshold, we usually just use 0.05 or 0.01 as a common practice
If you use adjusted p-value, then you should use 0.05 or 0.01. However, if you use the un-adjusted p-value, you should use an adjusted threshold which should be 0.05/(number of tests). This is the bonferroni correction
You can just use the add reply button instead of generating new results.
For any analysis you do, you should be able to get the p-value and the corresponding adjusted p-value. I am not sure what packages you use. But generally, you can just use the p.adjust function in R to get the adjusted p-value. You can use either the FDR or bonferroni correction
Yes, I got all those p values and adjusted p values. But the problem is, all adjusted p values are greater than 0.3. So how should I choose DEG?
It means that after corrected for multiple testing, you have no significance. Sometimes, there just isn't any signficant finding /DEGs in the study.
Or you can try to use FDR. If the multiple testing is based on Bonferroni, it will be more conserved and might therefore lead to very few findings
Thank you sir..
As Sam mentioned, if your smallest p-value is 0.3, then it means you do not have any significant DE genes. Did you perform any QC or normalization before calculating differential expression?
In our application, iPathwayGuide, we recommend DEG selection be based on adjusted p-value (and log2FC). Our default thresholds for DEG selection are Adj.pv < 0.05 and absolute value log2FC > 0.6.
If there are no DEGs, then it means you might need to re-run some of your experiments or that the two phenotypes are not dissimilar.
I hope this helps.
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Thank you. You helped me.