https://github.com/pwilmart/irs_normalization

An exploration of internal reference scaling (IRS) normalization in isobaric tagging proteomics experiments.
https://github.com/pwilmart/irs_normalization

ipynb irs-normalization jupyter-notebook mass-spectrometry normalization proteomics proteomics-data r tmt

Last synced: 3 months ago
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An exploration of internal reference scaling (IRS) normalization in isobaric tagging proteomics experiments.

• Host: GitHub
• URL: https://github.com/pwilmart/irs_normalization
• Owner: pwilmart
• License: mit
• Created: 2018-01-30T22:04:49.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-08-06T14:05:38.000Z (over 5 years ago)
• Last Synced: 2024-10-11T15:41:08.786Z (3 months ago)
• Topics: ipynb, irs-normalization, jupyter-notebook, mass-spectrometry, normalization, proteomics, proteomics-data, r, tmt
• Language: Jupyter Notebook
• Size: 32.1 MB
• Stars: 15
• Watchers: 4
• Forks: 9
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        
# 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](http://jupyter.org) 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):

* [understanding_IRS.html](https://pwilmart.github.io/IRS_normalization/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](https://pwilmart.github.io/IRS_normalization/statistical_testing.html) is Part 2

* [statistical_testing_ratios.html](https://pwilmart.github.io/IRS_normalization/statistical_testing_ratios.html) is Part 3

* [statistical_testing_take2.html](https://pwilmart.github.io/IRS_normalization/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.

## Other repositories that may be of interest:

* [TMT data analysis examples](https://github.com/pwilmart/TMT_analysis_examples.git)