Getting started¶

mspypeline can be used in three ways. If mspypeline was installed in a virtual environment make sure to first activate the virtual environment (conda env activation, pip env activation).

starting the GUI via the command-line:

(see the GUI Qickstart for next steps)

# either: call the python module mspypeline and start the GUI
python -m mspypeline --gui

# or: call the python module mspypeline, start the GUI and receive information from the logger
python -m mspypeline --gui --loglevel DEBUG

using it in python by importing:

(see the Python Quickstart, Examples or API reference for next steps.)
```
import mspypeline
```
using it purely on the command-line:

(see the command-line quickstart for next steps.)
```
python -m mspypeline
```

Usage with GUI¶

Warning

All files to be analyzed need to be in a directory called txt (as originally created by MaxQuant)

The GUI is divided into different parts to guide you through the general Workflow.

Start by selecting a directory at the top under the “Dir to analyze” option. The directory must be or contain the txt output directory from MaxQuant, in which all required txt files are saved.
The “Yaml file” option selects where the Settings and Configurations for the analysis are from. This can either be the “default” settings, or the “file” settings, if they are available.
If a quality control report should be generated, click the “Create Report” button.
A normalization method can be selected that will be applied to the data. Plot options below the “Normalization plots” can help to decide for a normalizer.
Generally, the GUI is structured in columns and rows like a table. Create a plot by toggling the checkbox in the left column for the desired plot (see all available plots in analysis options). In the same row, select one or more intensities and levels for which the plot should be created. Currently selected intensity and level options in the drop down menu are indicated by a check mark.
The “Update” button writes the selected options to a configuration file and the “Start” button creates all plots that were selected.
For proper analysis, the naming convention has to be followed. If the naming convention was was violated an auxiliary file will be created as described in the here.

Usage with Python¶

For the usage of mspypeline in a python script, the package or modules of the package have to be first imported. An analysis can be created in different ways.

A simple analysis in which the MSPInitializer is called to read in the data and settings and to create a MaxQuantPlotter which enables the calculation and plotting of the data might look something like this:

from mspypeline import MSPInitializer, MaxQuantPlotter

# create an initializer and read the data (txt folder directory) and settings
init = MSPInitializer("path/to/data")
init.init_config()
init.read_data()

# create a plotter from an initializer instance and create all those results specified in the settings
plotter = MaxQuantPlotter.from_MSPInitializer(init)
plotter.create_results()

For more code examples see the Examples.

Usage with command-line¶

It is recommended to provide a yaml file, where most settings are already configured. It is not possible at the moment to specify all arguments via the command-line, and will only be implemented if there is any demand.