pyEVA is a Python-based program developed for mass-spectrometry-based research, meant to aid in fidelity checking of EIC plots from feature tables. Initially written in C# and published by Guo et al. in 2021 as EVA¹, to aid in ease-of-use and multi-platform usage we have rewritten it in Python. Through pyEVA's use of deep learning tools, the process of identifying EIC plots as true or false can be automated, greatly speeding up the mass spectrometry pipeline of work.

While this is meant as a quick startup guide, we also feature a more detailed user setup guide in the repository, and a video guide for setup on Youtube.

Several other programs are necessary to install pyEVA. They are listed below:

1. Anaconda
2. PyCharm (Community or Professional editions both work)
3. R and R Studio

After installing all the programs, open the Anaconda Navigator and through it, PyCharm. Open the EVA_Python-master folder in PyCharm as a new project. In the terminal for PyCharm, type in conda env create --file environment.yml. When prompted, type conda env activate

Make sure that the version of PyCharm and VS Code used are the newest version as differences may occur with older versions.

In R Studio, install the related packages: "XCMS", "ggplot2", "scales", and "dplyr" (If you already have, please skip). Type the following code into R Studio as an R script, making sure that the version installed is up-to-date.

if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("xcms")
install.packages("ggplot2")
install.packages("scales")
install.packages("dplyr")

To ensure that all the necessary packages are properly installed, in the PyCharm terminal, type pip install -r requirements.txt, then run it.

In PyCharm, once the necessary packages are installed and the environment has been initialized, press CTRL+ALT+S, select the project, then Python Interpreter. From there, click Add Interpreter, Add Local Interpreter, Conda Environment, Use Existing Environment, and select pyEVA. In the terminal there should be a (pyEVA) beside your project location. This indicates that the system is ready to run pyEVA.

In input place a .csv file with the first two columns being m/z and rt (followed by the relevant data in successive columns), along with the related .mzXML or .mzML files.

In the PyCharm terminal, type: python main.py. This should start your program. Choose the level of smoothing (0, 1, 2), and wait for the EICs to be output to the EICplots folder under classifier. In output there should be a .csv named FinalTable.csv with the results of the analysis, and a .csv called PredictionOutcomes.csv with Boolean values for the data input.

We offer a set of data used for the demonstrations in the video here

For any issues or suggestions, please contact Tao Huan ([email protected]) or Ethan Wong-Ma ([email protected])

The utmost of thanks to Shaoyun Tong for developing the first version of pyEVA, which was debugged and improved upon to make this version. Without her help this would not be possible. Additionally, thanks to Yukai Wang for aiding in teaching me Python, which enabled further development of this project.