Overview of what I've been up to using R so far
Somehow I found myself preferring data analysis in Python (Pandas/NumPy/Matplotlib), but there are useful things for proteomics, transcriptomics, and genomics written in R. Bioconductor and such. We stand on the shoulders of giants of various sizes (sometimes when I see more complex code in R, I get dizzy from the size of those giants).
I have posted some R code on my Github that can serve as a guide or be directly used for proteomic analyses and meta-analyses. To a large extent, it can also be used for expression transcriptomic data.
https://github.com/hwllffrdd/proteomics
So far, you'll find there:
- Comparison for comparing and correlating two independent quantitative proteomic datasets
- Proteodata for creating a volcano plot
- Correlation for comparing and correlating proteomic and transcriptomic datasets
- sPLS-DA-PCA for analyzing proteomic data using sPLS-DA and PCA methods
https://github.com/hwllffrdd/convert_enrich
This is another modest pipeline that allows you to use the enrichPathway function on proteomic data.