XplainTDA: Explaining the topological structure of single-cell multi-omics data through perturbation
Abstract
Single-cell experiments have improved our ability to study biological systems by profiling thousands of cells simultaneously. The data can be viewed as a point cloud in a proper experimental space, whose shape is defined by the gene regulatory network. Persistent homology has been proposed as a method for studying the intrinsic structure of data and has shown the potential to reveal novel insights. Existing approaches focus on detecting topological features or groups of cells, without a focus on explainability and interpretability. Here, we introduce an explainable topological data analysis framework for single-cell data (XplainTDA) that identifies molecular determinants underlying the topological structure of the data via a perturbation-based approach. Across omics and data sets, we show that the method is stable, reproducible, and interpretable, extending persistent homology from topological description to molecular interpretation.
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BibTeX
@misc{giray2026xplaintda,
title = {{XplainTDA: Explaining the topological structure of single-cell multi-omics data through perturbation}},
author = {I. Giray and G. Malagoli and K. Schmid and M. Colome-Tatche},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.14.759129},
url = {https://www.biorxiv.org/content/10.64898/2026.10.14.759129v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - XplainTDA: Explaining the topological structure of single-cell multi-omics data through perturbation
AU - I. Giray
AU - G. Malagoli
AU - K. Schmid
AU - M. Colome-Tatche
PY - 2026
DA - 2026/10/14
PB - bioRxiv
DO - 10.64898/2026.10.14.759129
UR - https://www.biorxiv.org/content/10.64898/2026.10.14.759129v1
AB - Single-cell experiments have improved our ability to study biological systems by profiling thousands of cells simultaneously. The data can be viewed as a point cloud in a proper experimental space, whose shape is defined by the gene regulatory network. Persistent homology has been proposed as a method for studying the intrinsic structure of data and has shown the potential to reveal novel insights. Existing approaches focus on detecting topological features or groups of cells, without a focus on explainability and interpretability. Here, we introduce an explainable topological data analysis framework for single-cell data (XplainTDA) that identifies molecular determinants underlying the topological structure of the data via a perturbation-based approach. Across omics and data sets, we show that the method is stable, reproducible, and interpretable, extending persistent homology from topological description to molecular interpretation.
ER -