I have some data about protein expression levels in a cell. For each identified protein, there is an expression level and an associated p-value indicating the confidence that the protein was identified correctly. Two of the samples were technical replicates (i.e. a single sample was split into two parts and analysed separately). I now need to average the expression levels of the two technical replicates, and their corresponding p-values. I thought to use Fisher's method to combine them, which seems to me like the right thing to do. The problem is that I need to convert the result from a $\chi^2$ value into a p-value. Excel has a CHIDIST function which seems to do the trick, but, since I'm likely to be doing this sort of thing a lot in the near future, I thought to write a script to do it to plug into our analysis pipeline. I'm using python to write it, but can't find an equivalent function to CHIDIST. I realize that the process is basically finding the probability of getting the given $\chi^2$ score, so I'd like to check my thinking.
- Am I doing the right thing combining the p-values?
- Is Fisher's Method the appropriate process for combining the p-values correct?
- How many degrees of freedom? I'm thinking 2, as per the wikipedia page.
- The final p-value is one minus the integral from negative infinity to the $\chi^2$ score over the $\chi^2$ probability density function with one degree of freedom.
Thanks
