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Dear All,
I was trying to analyze my NGS data with DESeq package and have couple of questions with the same. Could you help me in answering these problems.
- After I apply nbinomTest, I get P values in range of 0-1. And fold changes of greater and lesser than 1.5. Assuming these as up and down-regulated how do you interpret P values for them. If the fold change is greater than 1.5 than it’s an upregulated gene what should be its expected P value (Normally if P value is less than 0.05 it’s significant but in my case all the P values are between 0-1).
- If I look at the adjusted P values for them most of them are equal to 1. So am confused if there is an up or down regulated gene which P value is significant and how do I report them in paper.
Thanks!
1. If your p-values are between 0 and 1 you also have p-values below 0.05. Or do you mean you have values above 0.05?
2.The p-value indicates the probability to find such values from a given null (H0) hypothesis. You can have a p-value of 0.5 and still the gene is of interest. To report in a paper, be clear about your decisions and motives and you'll be fine. Usually they are reported in a table.
Hello Lluís R, thanks for your reply.
1. From about 1000 genes, I get max 10 genes with P value less than 0.05, rest of them between 0.1 and 1, so am confused what should be my threshold for calling them significant genes.
2."The p-value indicates the probability to find such values from a given null (H0) hypothesis. You can have a p-value of 0.5 and still the gene is of interest. To report in a paper, be clear about your decisions and motives and you'll be fine. Usually they are reported in a table." This sounds reasonable, thanks.
1.If you change the threshold for the p-value now you will be doing p-value fishing. There are few genes under your threshold. Period. Probably you might want to analyze the problem, maybe you have very few samples and you need more power, maybe there is batch effect or other confounding factors, or you need to normalize better the samples, or maybe it is just that the samples are not that different.
No. It is correct that a p=0.5 does not show that the gene is uninteresting, but that does not mean that it can still be worth reporting. A high p values simply says that the experiment failed to provide any new insight into the gene, and hence that you cannot draw any conclusions from that data.