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Citation reference (copy and paste into your CV): 

"Proteomics data analysis in R for intermediate users". (November 9th, 2022). 2 hours workshop, Bioinformatics Support Hub, Cushing/Whitney Medical Library, Yale School of Medicine.  https://guides.library.yale.edu/proteomics_p2p 

Workshop Summary:

This workshop was part of the third season of the Peer-to-Peer Teaching sessions. These are taught by our own community members to fill knowledge gaps and keep up with the accelerated pace at which bioinformatics grows. This season we are pleased to announce sessions covering * Image analysis methodologies (imageJ and QuPath) * Omics analysis in R and/or Python * Biomedical Data analysis using the clusters * Statistical analysis software (Prism) *Science communication (Illustrator).

This session on "Proteomics data analysis in R for intermediate users" was designed and delivered by Shubham Misra, from the department of Neurology.

Summary: 

The use of high-throughput proteomics has increased over the past decade in clinical biomarker studies aiding in the discovery of proteins with diagnostic, prognostic, or drug-targeting potential. Therefore, the analysis of these large proteomics datasets becomes crucial in identifying the differentially expressed proteins. RStudio, a free and open-source platform, offers the environment to conduct these differential proteomics analyses.

In this session, we used simple R packages including “gplots”, “plyr”, “tidyverse”, “pheatmap”, to conduct basic proteomics data analysis and determine the differentially expressed proteins in our datasets, as well as visualize these differential expressed proteins using Volcano plots and heatmaps.

This workshop was designed for: 

Researchers with intermediate knowledge of R command line is a prerequisite. In this session we expect you to know the basic syntax of R.

This workshop covered: 

• Import excel files in RStudio.
• Install and Load R packages in RStudio.
• Use various R packages to identify differentially expressed proteins in your dataset
• Save processed data files in your computer as .csv files (e.g., protein fold changes, p-values).
• Perform basic proteomic data visualization (e.g., Volcano plots, heatmaps, histograms).
• Save the visualized plots/graphs as image files in your computer

Duration: 2 hrs

Instructor: Shubham Misra, PhD