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An exploration of internal reference scaling (IRS) normalization in isobaric tagging proteomics experiments.

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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.

The analysis is of a mouse lens development time course (6 points 3 days apart from E15 to P9) where three replicates of the time points were done in 3 separate TMT labelings. The lens is a unique system that has been studied for many years and the prior knowledge can be used to guide some analysis steps. The data is from this publication:

Khan, S.Y., Ali, M., Kabir, F., Renuse, S., Na, C.H., Talbot, C.C., Hackett, S.F. and Riazuddin, S.A., 2018. Proteome Profiling of Developing Murine Lens Through Mass Spectrometry. Investigative Ophthalmology & Visual Science, 59(1), pp.100-107.

Contents:

Four jupyter notebook files (R kernel). If you click on the notebook files (*.ipynb extensions), they will render and display in your bowser. Please be patient as they can take a minute to render.

  • 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 (these may load faster):

Added R scripts extracted from the notebooks. These can be used in RStudio or modified for your own analyses.

Other repositories that may be of interest:

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