I am trying to find a package to use for analysis of protein microarray data. The experiment involved one control and 4 treatment conditions. A ProteomicProfiler microarray for 55 proteins was used to detect the effects of each treatment on the panel of 55 proteins. This array uses protein-specific antibodies to capture and fluorescence secondary antibody to identify. Each sample was spotted twice for each protein. All the arrays were set up at the same time, and read at the same time (one batch). The arrays were read using a LiCor instrument, and the results outputted to an excel file. Within the excel file I can do the background subtract, and check the "standards" for reliability of the runs. But what I need is to normalize/standardize the data, and model them correctly to look at fold changes, etc. I have done a lot of searching through Bioconductor for protein microarray analytical packages, but everything I find is specific to a specific microarray type (like the PAA package), requiring input of raw data from a specific type of analyzer. Is there one which will work with the generic data out of an excel file? Here is an example of the data I have:
PROTEIN CONTROL TREATMENT 1 TREATMENT 2 TREATMENT 3 TREATMENT 4
A 39 72 180 62 50
A 32 75 149 66 44
B 96 99 120 137 145
B 89 99 122 142 140
C 46 40 85 90 70
C 43 37 80 88 77
These are just fictional values, not the real data, but they are representative of the range and changes that I have seen. The units are fluorescence per pixel on the blot. Thanks for suggestions of appropriate packages to use.
- hcnbox
Yes, I use limma for all of my proteomics analyses.
Thanks very much Kevin. I'll figure it out, now that I'm on the right block.
Thanks Gordon. Very helpful. This answer confirms I must go back to limma and determine why I had troubles, rather than keep trying to find a magic algorithm written just for me. And you are correct; the LiCor data are per area, not pixel. Poor memory, and a tired brain. And now I can't find any "thumbs up" to upvote your answer as it deserves.