I am working with protein data at different time-points. After log transformation many proteins show a linear incorporation over time. Therefore I can use a simple linear model to study its trend.
For some proteins I have a 7 data for 7 time-points, but some others I have only data for 3 time-points (it was not detected at some time-points).
Since I am going to do many tests on the significance of the regression I thought I should be correcting my p-values to reduce the amount of false positives. I have been using the Bonferroni, Holm-bonferroni and permuted FDR methods. When I calculate the adjusted p-values I use the data from all the proteins, regardless if they have measurements for 3 or 7 time-points. This causes that many proteins with 3 and 4 time-points are not deemed significant by the Bonferroni and Holm-Bonferroni adjustments. The number of proteins that have 3 time-points is 5 times smaller than the number of proteins that have 7.
My question: should I perform the p-value adjustment within the time-point? Therefore calculating a different threshold for proteins with 3 timepoints, a different one for proteins with 4... etc.