Data Analysis Pro Plotting - Version 0.1.2

A Feature-Focused Visualization for High-Dimensional Biological Data

Data Analysis Pro Plotting is a Python library designed for advanced data analysis, visualization, and interpretation of complex datasets, including omics data.

This update includes a minor metadata correction: an updated CITATION.cff file and version alignment.

Citation:

Once published on Zenodo, please cite this release as:

Manzone Rodriguez C., Angiolini S.C., Sotomayor C.E., Iribarren P. (2026).
Data Analysis Pro Plotting (Version 0.1.2). Zenodo.
https://zenodo.org/records/18824150

Concept DOI (represents all versions):
https://doi.org/10.5281/zenodo.18819795

Key Features:

• Flexible and publication-ready plotting for barplots, Venn diagrams, PCA, heatmaps, dendrograms, volcano plots, and GSEA analysis.
• Variable-level highlighting to mark specific genes, proteins, or other features directly within visualizations.
• Fully customizable aesthetics including colors, styles, labels, sizes, and layouts.
• Integrated workflow built upon established Python libraries, enabling PCA computation, clustering, hierarchical heatmaps, and GSEA, with extended and customized visualization capabilities.
• Example notebook included to demonstrate all major functionalities using modified versions of the GEO dataset GSE109297 (Gene Expression Omnibus). The data were intentionally altered for methodological demonstration and are not intended for biological interpretation.

Installation:

• Requires Python 3.11 or higher.
• Install dependencies using pip install -r requirements.txt.

Citation:

Once published on Zenodo, please cite this release as:

Manzone Rodriguez C., Angiolini S.C., Sotomayor C.E., Iribarren P. (2026).
Data Analysis Pro Plotting (Version 0.1.0). Zenodo.
https://doi.org/10.5281/zenodo.18819796

Concept DOI (represents all versions):
https://doi.org/10.5281/zenodo.18819795

This release aims to support reproducible research by providing high-quality figures and analysis tools suitable for publication and exploratory data analysis.