Hi again,
I compared the data
a) using the rma-api and its provided z-scores
b) calculating z-score manually using the expression-levels obtain by b)
c) downloading expression-levels as you described and recalculating z-scores
d) same approach as c) but downloading z-scores directly
regardless of whether I download the log2-intensities or obtain them (then they are called expression-levels) using the rma-api, recalculating z-scores gives me the same results - but they differ from the pre-computed z-scores.
I checked this by flattening all probe-values and sorting them. if all values for each approach are equal, then the value-lists should be the same as well.
so:
- pre-computed z-scores of a) do not match the manually calculcated ones of b) and c)
- pre-computed z-scores of d) match the pre-computed ones of a), but hence also not b) and c)
- b) and c) are the same
you can check out my the zip-file I provide in the following. it contains the results and the code / pseudo-code I used:
Could you please check how to retrieve the same z-scores you provide using the expression-levels and expand on how you managed to do this? Thank you very much!
Furthermore, I wonder why the z-scores are calculated on a per-probe basis. according to the normalization white-paper (http://help.brain-map.org/download/attachments/2818165/Normalization_WhitePaper.pdf) expression-levels have been normalized across brain-regions and donors. wouldn’t the fold-change be more robust if expression-levels from all probes were taken into account? having high-variance expression-levels in a specific well, representing tissue from a specific region, would lead to skewed z-scores for the other samples of this probe, right?
Thanks in advance and kind regards
Christoph