I have been given conflicting advice on how to analyse data.
I have cell counts for as a readout of growth viablility. Data is in duplicate, thousands of conditions, multiple cell lines - a large screening experiment.
I need to perform some form of Z-score analysis, plate by plate, to identify and rank hits.
I have performed a Z-score analysis on the raw data, and it looks fantastic. I have plotted distributions and generated hit lists - all fine.
However, I have been told I absolutely need to perform robust Z-score analysis instead, based on the MAD and the Abs values of the raw data - I have done this, checked it, and it looks terrible.
The Hit lists make no sense when compared to the raw data and the percent of control data which I am using as a sanity check - the hit lists barely overlap with my normal Z-scores, and the new hits look like garbage.
Furthermore, someone else has suggested I need to do the robust Z-score analysis from the log transformed raw values.
Does anyone have any input as to what I should be doing, why the Robust Zs might make no sense and be so radically different from the Z scores, and if Log transformation of raw data will help or is necessary to fix this.
Thanks in advance.