MakeMyFigure: An Interactive Platform for Reproducible Quantitative Data Visualization, Analysis, and Scientific Figure Construction
- 1Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA
Abstract
Reproducible data analysis and visualization are essential for reliable biological and biomedical research, yet creating high-quality, publication-ready figures often requires moving data and results between multiple analysis, visualization, and graphics tools. This fragmented, multi-layered process can make it difficult to trace how individual figure panels were generated, preserve the underlying analytical decisions, and reproduce them later. Here, we introduce MakeMyFigure, a free and open-source platform for data visualization, analysis, and creation of multi-panel scientific figures. MakeMyFigure integrates data processing, statistical analysis, visualization, and figure assembly within a single workflow. It supports a range of quantitative data formats and structures, including feature-by-sample matrices and precomputed statistical results, and provides data-aware visualization recommendations to help users select appropriate plots from a library of 38 visualization types. These capabilities allow researchers to move directly from experimental measurements to commonly used statistical analyses and graphical representations without requiring programming expertise. Eighteen statistical procedures are implemented, and their results are drawn directly onto the plot families that support statistical annotation, keeping analytical results linked to the panels they generate. Importantly, MakeMyFigure uses machine-readable JSON specifications to record the identity and checksum of the source data, the processing steps, visualization settings, and statistical parameters used to generate each figure panel. It can also save a figure as a portable package, freezing the specifications together with the data. This allows figures to be regenerated from their recorded specifications and source data, or from the package alone on another computer, rather than relying on manually reconstructed workflows. Using published datasets from several biological fields, independent statistical validation in R, and a comparison with 15 representative analysis, visualization, and figure-generation tools, we show that MakeMyFigure combines accessible, code-free figure creation with panel-level computational reproducibility. Overall, MakeMyFigure provides a unified approach for creating, documenting, and reproducing scientific figures. Source code and documentation are available at https://github.com/surPoudel/make-my-figure, https://github.com/surPoudel/make-my-figure/releases/tag/v1.1.0.
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BibTeX
@misc{poudel2026makemyfigure,
title = {{MakeMyFigure: An Interactive Platform for Reproducible Quantitative Data Visualization, Analysis, and Scientific Figure Construction}},
author = {S. Poudel and H. K. Shrestha and J. C. Crawford and F. Demontis and D. R. Green},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.15.751916},
url = {https://www.biorxiv.org/content/10.64898/2026.09.15.751916v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - MakeMyFigure: An Interactive Platform for Reproducible Quantitative Data Visualization, Analysis, and Scientific Figure Construction
AU - S. Poudel
AU - H. K. Shrestha
AU - J. C. Crawford
AU - F. Demontis
AU - D. R. Green
PY - 2026
DA - 2026/09/15
PB - bioRxiv
DO - 10.64898/2026.09.15.751916
UR - https://www.biorxiv.org/content/10.64898/2026.09.15.751916v1
AB - Reproducible data analysis and visualization are essential for reliable biological and biomedical research, yet creating high-quality, publication-ready figures often requires moving data and results between multiple analysis, visualization, and graphics tools. This fragmented, multi-layered process can make it difficult to trace how individual figure panels were generated, preserve the underlying analytical decisions, and reproduce them later. Here, we introduce MakeMyFigure, a free and open-source platform for data visualization, analysis, and creation of multi-panel scientific figures. MakeMyFigure integrates data processing, statistical analysis, visualization, and figure assembly within a single workflow. It supports a range of quantitative data formats and structures, including feature-by-sample matrices and precomputed statistical results, and provides data-aware visualization recommendations to help users select appropriate plots from a library of 38 visualization types. These capabilities allow researchers to move directly from experimental measurements to commonly used statistical analyses and graphical representations without requiring programming expertise. Eighteen statistical procedures are implemented, and their results are drawn directly onto the plot families that support statistical annotation, keeping analytical results linked to the panels they generate. Importantly, MakeMyFigure uses machine-readable JSON specifications to record the identity and checksum of the source data, the processing steps, visualization settings, and statistical parameters used to generate each figure panel. It can also save a figure as a portable package, freezing the specifications together with the data. This allows figures to be regenerated from their recorded specifications and source data, or from the package alone on another computer, rather than relying on manually reconstructed workflows. Using published datasets from several biological fields, independent statistical validation in R, and a comparison with 15 representative analysis, visualization, and figure-generation tools, we show that MakeMyFigure combines accessible, code-free figure creation with panel-level computational reproducibility. Overall, MakeMyFigure provides a unified approach for creating, documenting, and reproducing scientific figures. Source code and documentation are available at https://github.com/surPoudel/make-my-figure, https://github.com/surPoudel/make-my-figure/releases/tag/v1.1.0.
ER -