Box Plot#
VueCore is a Python package for creating interactive and static visualizations of multi-omics data. It is part of a broader ecosystem of tools—including ACore for data processing and VueGen for automated reporting—that together enable end-to-end workflows for omics analysis.
This notebook demonstrates how to generate box plots using plotting functions from VueCore. We showcase basic and advanced plot configurations, highlighting key customization options such as grouping, color mapping, text annotations, and export to multiple file formats.
Notebook structure#
First, we will set up the work environment by installing the necessary packages and importing the required libraries. Next, we will create basic and advanced box plots.
Credits and Contributors#
This notebook was created by Sebastián Ayala-Ruano under the supervision of Henry Webel and Alberto Santos, head of the Multiomics Network Analytics Group (MoNA) at the Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain).
You can find more details about the project in this GitHub repository.
0. Work environment setup#
0.1. Installing libraries and creating global variables for platform and working directory#
To run this notebook locally, you should create a virtual environment with the required libraries. If you are running this notebook on Google Colab, everything should be set.
# VueCore library
%pip install vuecore
0.2. Importing libraries#
# Imports
import pandas as pd
import numpy as np
from pathlib import Path
from vuecore.plots.basic.box import create_box_plot
0.3. Create sample data#
We create a synthetic dataset simulating gene expression levels across different patient samples and treatment conditions, with each data point representing a unique gene’s expression level under a specific treatment for a particular patient.
# Set a random seed for reproducibility of the synthetic data
np.random.seed(42)
# Parameters
num_samples = 200
sample_groups = ["Patient A", "Patient B", "Patient C", "Patient D"]
treatments = ["Control", "Treated"]
# Sample metadata
sample_ids = np.random.choice(sample_groups, size=num_samples)
treatment_assignments = np.random.choice(treatments, size=num_samples)
gene_ids = [f"Gene_{g}" for g in np.random.randint(1, 1500, size=num_samples)]
# Base expression values
base_expr = np.random.normal(loc=100, scale=35, size=num_samples)
# Treatment effect simulation
treatment_effect = np.where(
treatment_assignments == "Treated",
np.random.normal(loc=50, scale=30, size=num_samples),
0
)
# Small random per-gene offset for extra variability
gene_offset = np.random.normal(loc=0, scale=20, size=num_samples)
# Final expression
expr = base_expr + treatment_effect + gene_offset
# Construct DataFrame
gene_exp_df = pd.DataFrame({
"Sample_ID": sample_ids,
"Treatment": treatment_assignments,
"Gene_ID": gene_ids,
"Expression": expr
})
1. Basic Box Plot#
A basic box plot can be created by simply providing the x and y columns from the DataFrame, along with style options like title.
# Define output file path for the PNG plot
file_path_basic_png = Path(output_dir) / "box_plot_basic.png"
# Generate the basic box plot
box_plot_basic = create_box_plot(
data=gene_exp_df,
x="Treatment",
y="Expression",
title="Gene Expression Levels by Treatment",
file_path=file_path_basic_png,
)
box_plot_basic.show()
[VueCore] Plot saved to outputs/box_plot_basic.png
2. Advanced Box Plot#
Here is an example of an advanced box plot with more descriptive parameters, including color and box grouping, text annotations, hover tooltips, and export to HTML.
# Define the output file path for the advanced HTML plot
file_path_adv_html = Path(output_dir) / "box_plot_advanced.html"
# Generate the advanced box plot
box_plot_adv = create_box_plot(
data=gene_exp_df,
x="Treatment",
y="Expression",
color="Sample_ID",
boxmode="group",
notched=True,
title="Gene Expression Levels with Control and Treatment Condition",
subtitle="Distribution of gene expression across different treatments and patient samples",
labels={
"Treatment": "Treatment",
"Expression": "Gene Expression",
"Sample_ID": "Patient Sample ID",
},
color_discrete_map={
"Patient A": "#508AA8",
"Patient B": "#A8505E",
"Patient C": "#86BF84",
"Patient D": "#A776AF",
},
category_orders={"Sample_ID": ["Patient A", "Patient B", "Patient C", "Patient D"]},
hover_data=["Gene_ID"],
file_path=file_path_adv_html,
)
box_plot_adv.show()
[VueCore] Plot saved to outputs/box_plot_advanced.html