pyNetworkPlot

Network plot for visualization of immune cells receptor clonality.

pyNetworkPlot is a small Python module build over igraph, cairo and matplotlib that creates a Network plot from a sequence dataset. Ideal for clonality analysis of T cell/ B cell receptors (TCR/BRC), but can be extended to any analysis involving frequency of Strings.

1. Installation

pyNetworkPlot can be used in 3 different ways.

1.1 Installation of packages in requirements.txt

pyNetworkPlot is tested in Python 3.9 and its dependencies can be pip-installed:

pip install -r requirements.txt

1.2 Setting a conda environment

This repository contains a conda definition file env/env.yml that can create the conda environment:

conda env create -f env.yml
conda activate network

1.3 Singularity container

For Linux users, pyNetworkPlot can be run easily with a Singularity container. The container can be built with the definition file pyNetworkPlot.def and run as follows:

sudo singularity build pyNetwork.sif containers/pyNetwork.def
./pyNetwork.sif <module_parameters>

Usage

pyNetworkPlot can be used as a command-line tool running pyNetworkPlot.py

Using the command line

By calling the script with the corresponding parameters in the correct environment, the script will save the plot in the location given by the user and in the given format.

./pyNetworkPlot.py in_file out_file --seq_col Sequence --color_col Color --shape_col Shape
               --size_col Shape --similarity 0 -custom_color /path/to/file --layout FR
               --use-legend

Parameters for command-line calling

• in_file : string. Path to the DE dataset. The dataset must at least one column with the sequences. Additional columns for color, shape and size are optional.

Sequence	Color	shape
GATTACCA	#ff6db6	1
GATTACAA	#ff6db6	2
...	...	...

• out_path : string. Path to the file where the plot is going to be saved.
• seq_col : string (optional). Name of the column corresponding to the sequence in the dataset. Defaults to 'sequence'.
• color_col : string (optional). Name of the column corresponding to the color values in the dataset. Defaults to None.
• shape_col : string (optional). Name of the column corresponding to the shape values in the dataset. Defaults to None.
• size_col : string (optional). Name of the column corresponding to the size values in the dataset. Defaults to None.
• similarity : int (optional). Maximum difference in amino acids between sequences to consider them similar. If non-zero, identical sequences will be plotted red and similar sequences black. Defaults to zero (only plots edges between identical sequences).
• custom_color : string. Path to a file mapping elements of color_col to a hex color code. This file has one line per unique value of column color_col. Each line starts with the value, followed by a comma and the hex code for the color corresponding to that value. Defaults to None (use system default colors).
• layout : string (optional). Keyword of the drawing algorithm to use. The options are 'FR' (Fruchterman-Reingold), 'DH' (Davidson-Harel), 'GO' (Graphopt), DrL (Dr Layout), LgL (Large Graph Layout) or MDS (Multi-dimensional Scaling). Defaults to 'FR'.
• use-legend : flag. Include this flag to include a legend in the figure.