pyVolcano

Volcano plot in python! 🌋
pyVolcano is a small module build over numpy, matplotlib and pandas that creates a Volcano plot from a dataset using the function volcano

1. Installation

pyVolcano can be used in 3 different ways.

1.1 Installation of packages in requirements.txt

pyVolcano is tested in Python 3.8 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.yml that can create the conda environment:

conda env create -f env.yml
conda activate volcano

1.3 Singularity container

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

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

if the user does not have admin rights, the --fakeroot flag can be used when building the singularity image:

singularity build --fakeroot pyVolcano.sif containers/pyVolcano.def
./pyVolcano.sif <module_parameters>

2. Usage

pyVolcano can be used as a command-line tool running pyVolcano.py or by loading it as a python module and using its function volcano.

2.1 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.

./pyVolcano.py in_file out_file --pval 0.01 --log2F 1 --gene_col gene
               --pval_col p-value --log_col log2F -n 0 --title 'Volcano plot'
               --up_color green --down_color red --width 8 --height 8

Parameters for command-line calling

• in_file : string. Path to the DE dataset. The dataset must contain the gene names, the p-values and the log2Fold values in columns as follows:
  

gene	log2Fold	p-value
ENSG00000162391	-2.569	0.0054
ENSG00000181965	1.559	0.0015
...	...	...

The names in the column genes are the ones to be shown in the plot for selected genes (see below n_names2show). The name of the columns do not need to be the same as in this example (see below gene_col, pval_col and log_col).

• out_file : string. Path to the file where the plot is going to be saved. The name of the file must have the desiderd format. Available formats are the ones supported by matplotlib, such as .pdf, .png, .svg, etc.
• pval : float (optional). P-value threshold that determines significance. A horizontal line will be drawn in the plot corresponding to this value. Defaults to 0.01.
• log2F : float (optional). Log2Fold value threshold that determines significance. Two vertical lines will be drawn in the plot corresponding to this value and its opposite. Defaults to 1.
• gene_col : string (optional). Name of the column corresponding to gene names in the DE dataset. Defaults to 'gene'.
• pval_col : string (optional). Name of the column corresponding to p-values in the DE dataset. Defaults to 'padj'.
• log_col : string (optional). Name of the column corresponding to log2Fold values in the DE dataset. Defaults to 'log2Fold'.
• n_names2show : int (optional). Number of top gene names to show. The genes are sorted by ascending p-value and by descending absolute value of log2Fold change, giving priority to the former. If the number of significant genes is higher than this number, only the names of significant genes are shown. Defaults to 0.
• title : string (optional). Title of the plot to be written on top of the plot. Defaults to 'Volcano plot'.
• up_color : string (optional). Color for the up-regulated genes. Defaults to 'green'.
• down_color : string (optional). Color for the down-regulated genes. Defaults to 'red'.
• width : int (optional). Width of the figure in inches. Defaults to 8.
• height : int (optional). Height of the figure in inches. Defaults to 8.

2.2 Importing as a module

A script or notebook running in the same directory as pyVolcano.py can import the function volcano as follows:

from pyVolcano import volcano

As the function volcano returns a matplotlib.axes.Axes object, a figure and an axes should be created to make the plot.

from matplotlib import pyplot as plot
fig,ax = plt.subplots()
ax = volcano(df,ax,pval=0.01,log2F=1,gene_col='gene',pval_col='p-val',
        log_col='log2F',n_names2show = 10,title = 'Volcano plot',
        up_color='green',down_color='red')
plt.show()

Parameters of python function

• df : pandas DataFrame holding the differential gene expression data with the same structure as the input file explained above. Other columns are ignored but allowed.
• ax : matplotlib.axes.Axes. Axes where to plot the Volcano plot.
• pval : float (optional). P-value threshold that determines significance. Defaults to 0.01.
• log2F : float (optional). Log2Fold value threshold that determines significance. Defaults to 1.
• gene_col : string (optional). Name of the column corresponding to gene names in the DE dataset. Defaults to 'gene'.
• pval_col : string (optional). Name of the column corresponding to p-values in the DE dataset. Defaults to 'padj'.
• log_col : string (optional). Name of the column corresponding to log2Fold values in the DE dataset. Defaults to 'log2Fold'.
• n_names2show : int (optional).Number of top gene names to show. Defaults to 0.
• title : string (optional). Title of the plot to be written on top of the plot. Defaults to 'Volcano plot'.
• up_color : string (optional). Color for the up-regulated genes. Defaults to 'green'.
• down_color : string (optional). Color for the down-regulated genes. Defaults to 'red'.