Abstract
The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.
Highlights
The complex downstream analysis of proteomic data requires the integration of bioinformatics, statistics, network analysis, and, frequently, machine learning
We provide a GitHub repository where the given examples are available for download, as well as a list of already existing plugins of varying complexity and where to find them online (Table 1)
We provide the basic steps for generating Python-only plugins through the command line style interface. The code for this example is available at https:// github.com/ JurgenCox/ perseus-plugin-programming/ blob/ master/ scripts/ head.py
Summary
The complex downstream analysis of proteomic data requires the integration of bioinformatics, statistics, network analysis, and, frequently, machine learning. In addition to the annotation columns, annotation rows can be defined to specify column grouping parameters such as biological conditions and technical replicates This structure makes Perseus very flexible, allowing statistical analysis for a considerable variety of experimental designs and thereby facilitating hypothesis generation (Rudolph & Cox, 2019). All custom tools originally scripted in R can be used within Perseus In this basic protocol, a simple example of an R-only plugin, extracting the head (top rows) of a matrix, will be presented to illustrate how the data transfer between Perseus and R functions. A simple example of an R-only plugin, extracting the head (top rows) of a matrix, will be presented to illustrate how the data transfer between Perseus and R functions This example will be run through the command line style interface. A computer running Windows 8 (64 bit) or higher, or Windows Server 2008 or higher
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