msreport 0.0.27

Post processing and analysis of quantitative proteomics data

MsReport

Introduction

MsReport is a python library that allows simple and standardized post processing of quantitative proteomics data from bottom up, mass spectrometry experiments. Currently working with label free protein quantification reports from MaxQuant and FragPipe is fully supported. Other data analysis pipelines can be added by writing a software specific reader function.

MsReport is primarily developed as a tool for the Mass Spectrometry Facility at the Max Perutz Labs (University of Vienna), to allow the generation of Quantitative Protein and PTM reports, and to facilitate project specific data analysis tasks.

Release

Development is currently in early alpha and the interface is not yet stable.

Scope

The reader module contains software specific reader classes that provide access to the outputs of the respective software. Reader instances allow importing protein and ion tables, and provide the ability to standardize column names and data formats during the import. To do so, reader classes must know the file structure and naming conventions of the respective software.

The qtable class allows storing and accessing quantitative data from a particular level of abstraction, such as proteins or ions, and an experimental design table that describes to which experiment a sample belongs to. The quantitative data are in the wide format, i.e. the quantification data of each sample is stored in a separate column. The Qtable allows convenient handling and access to quantitative data through information from the experimental design, and represents the data structure used by the analyze, plot, and export modules.

The analyze module provides a high-level interface for post-processing of quantitative data, such as filtering valid values, normalization between samples, imputation of missing values, and statistical testing with the R package LIMMA.

The plot module allows generation of quality control and data analysis plots.

Using methods from the export module allows conversion and export of quantitative data into the Amica input format, and generating contaminant tables for the inspection of potential contaminants.

Additional scripts

• The excel_report module enables the creation of a formatted excel protein report by using the XlsxReport library.
• The benchmark module contains functions to generate benchmark plots from multiple Qtable instances, and can be used for method or software comparison.

Install

If you do not already have a Python installation, we recommend installing the Anaconda distribution of Continuum Analytics, which already contains a large number of popular Python packages for Data Science. Alternatively, you can also get Python from the Python homepage. MsReport requires Python version 3.9 or higher.

You can use pip to install MsReport from the distribution file with the following command:

pip install msreport-X.Y.Z-py3-none-any.whl

To uninstall the MsReport library type:

pip uninstall msreport

Installation when using Anaconda

If you are using Anaconda, you will need to install the MsReport package into a conda environment. Open the Anaconda navigator, activate the conda environment you want to use, run the "CMD.exe" application to open a terminal, and then use the pip install command as described above.

Additional requirements

MsReport provides an interface to the R package LIMMA for differential expression analysis, which requires a local installation of R (R version 3.4 or higher) and the system environment variable "R_HOME" to be set to the R home directory. Note that it might be necessary to restart the computer after adding the "R_HOME" variable. The R home directory can also be found from within R by using the command below, and might look similar to "C:\Program Files\R\R-4.2.1" on windows.

normalizePath(R.home("home"))