Scatter 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 scatter 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 scatter 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
from pathlib import Path
from vuecore.plots.basic.scatter import create_scatter_plot
0.3. Create sample data#
We create a synthetic dataset that contains simulated gene expression values, p-values, regulation status, and significance scores for 8 genes across two cell types.
sample_df = pd.DataFrame(
{
"gene_expression": [1.2, 2.5, 3.1, 4.5, 5.2, 6.8, 3.9, 2.1],
"log_p_value": [0.5, 1.5, 2.0, 3.5, 4.0, 5.5, 1.8, 0.9],
"regulation": ["Up", "Up", "None", "Down", "Down", "Down", "None", "Up"],
"significance_score": [10, 20, 5, 40, 55, 80, 15, 25],
"gene_name": [
"GENE_A",
"GENE_B",
"GENE_C",
"GENE_D",
"GENE_E",
"GENE_F",
"GENE_G",
"GENE_H",
],
"cell_type": ["A", "B", "A", "B", "A", "B", "A", "B"],
}
)
sample_df
| gene_expression | log_p_value | regulation | significance_score | gene_name | cell_type | |
|---|---|---|---|---|---|---|
| 0 | 1.2 | 0.5 | Up | 10 | GENE_A | A |
| 1 | 2.5 | 1.5 | Up | 20 | GENE_B | B |
| 2 | 3.1 | 2.0 | None | 5 | GENE_C | A |
| 3 | 4.5 | 3.5 | Down | 40 | GENE_D | B |
| 4 | 5.2 | 4.0 | Down | 55 | GENE_E | A |
| 5 | 6.8 | 5.5 | Down | 80 | GENE_F | B |
| 6 | 3.9 | 1.8 | None | 15 | GENE_G | A |
| 7 | 2.1 | 0.9 | Up | 25 | GENE_H | B |
1. Basic Scatter Plot#
A basic scatter plot can be created by providing the x and y columns from the DataFrame, along with style options like title.
# Define output path for the basic png plot
file_path_basic_png = Path(output_dir) / "scatter_basic.png"
# Generate the basic scatter plot
scatter_plot_basic = create_scatter_plot(
data=sample_df,
x="gene_expression",
y="log_p_value",
title="Basic Gene Expression Scatter Plot",
file_path=file_path_basic_png,
)
scatter_plot_basic.show()
[VueCore] Plot saved to outputs/scatter_basic.png
2. Advanced Scatter Plot#
An example of an advanced scatter plot that includes grouping by a categorical variable, color mapping, text annotations, and adding several styling options.
# Define output file path for the HTML plot
file_path_adv_html = Path(output_dir) / "scatter_advanced.html"
# Generate advanced line plot
scatter_plot_adv = create_scatter_plot(
data=sample_df,
x="gene_expression",
y="log_p_value",
color="regulation",
size="significance_score",
text="gene_name",
title="Advanced Gene Expression Scatter Plot",
subtitle="Visualizing Gene Expression with Regulation and Significance",
labels={
"gene_expression": "Log2 Fold Change",
"log_p_value": "Log P-value",
"regulation": "Regulation Status",
"significance_score": "Significance Score",
"gene_name": "Gene Name",
},
color_discrete_map={"Up": "#508AA8", "Down": "#A8505E", "None": "#838383"},
opacity=0.8,
marker_line_width=1,
marker_line_color="darkgray",
width=900,
height=600,
file_path=file_path_adv_html,
)
scatter_plot_adv.show()
[VueCore] Plot saved to outputs/scatter_advanced.html