IRS_normalization
An exploration of internal reference scaling (IRS) normalization in isobaric tagging proteomics experiments. Also, examples of how IRS-normalized data affects statistical testing, and how to avoid using ratios in the analyses.
The IRS method was first described in this publication:
Plubell, D.L., Wilmarth, P.A., Zhao, Y., Fenton, A.M., Minnier, J., Reddy, A.P., Klimek, J., Yang, X., David, L.L. and Pamir, N., 2017. Extended multiplexing of tandem mass tags (TMT) labeling reveals age and high fat diet specific proteome changes in mouse epididymal adipose tissue. Molecular & Cellular Proteomics, 16(5), pp.873-890.
Contents:
four jupyter notebook files (R kernel)
- understanding_IRS.ipynb is Part 1 (normalizations)
- statistical_testing.ipynb is Part 2 (edgeR testing)
- statistical_testing_ratios.ipynb is Part 3 (taking ratios and using limma)
- statistical_testing_take2.ipynb is Part 4 (testing P0 vs P3)
Data from Kahn, et al.
- iovs_58-13-55-s01.csv
Sample information for design matrix
- design.csv
Saved results from the statisticl testing
- final_part3.csv (and final_part3.xlsx)
Added HTML renderings of the notebooks for those who just want to see the analysis steps and figures:
- understanding_IRS.html is Part 1 (needs the png file)
- irs_diagram.png is an image for the understanding_IRS.html script
- statistical_testing.html is Part 2
- statistical_testing_ratios.html is Part 3
- statistical_testing_take2.html is Part 4
Added R scripts extracted from the notebooks. These can be used in RStudio or modified for your own analyses.