Tensor Network based Gene Regulatory Network Inference for Single-Cell Transcriptomic Data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914027747147776 |
|---|---|
| author | Larrarte, Olatz Sanz Aizpurua, Borja Dastbasteh, Reza Otxoa, Ruben M. Martinez, Josu Etxezarreta |
| author_facet | Larrarte, Olatz Sanz Aizpurua, Borja Dastbasteh, Reza Otxoa, Ruben M. Martinez, Josu Etxezarreta |
| contents | Deciphering complex gene-gene interactions remains challenging in transcriptomics as traditional methods often miss higher-order and nonlinear dependencies. This study introduces a quantum-inspired framework leveraging tensor networks (TNs) to optimally map expression data into a lower dimensional representation preserving biological locality. Using Quantum Mutual Information (QMI), a nonparametric measure natural for tensor networks, we quantify gene dependencies and establish statistical significance via permutation testing. This constructs robust interaction networks where the edges reflect biologically meaningful relationships that are resilient to random chance. The approach effectively distinguishes true regulatory patterns from experimental noise and biological stochasticity. To test the proposed method, we recover a gene regulatory network consisted of six pathway genes from single-cell RNA sequencing data comprising over $28.000$ lymphoblastoid cells. Furthermore, we unveil several triadic regulatory mechanisms. By merging quantum physics inspired techniques with computational biology, our method provides novel insights into gene regulation, with applications in disease mechanisms and precision medicine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06891 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tensor Network based Gene Regulatory Network Inference for Single-Cell Transcriptomic Data Larrarte, Olatz Sanz Aizpurua, Borja Dastbasteh, Reza Otxoa, Ruben M. Martinez, Josu Etxezarreta Molecular Networks Quantum Physics Deciphering complex gene-gene interactions remains challenging in transcriptomics as traditional methods often miss higher-order and nonlinear dependencies. This study introduces a quantum-inspired framework leveraging tensor networks (TNs) to optimally map expression data into a lower dimensional representation preserving biological locality. Using Quantum Mutual Information (QMI), a nonparametric measure natural for tensor networks, we quantify gene dependencies and establish statistical significance via permutation testing. This constructs robust interaction networks where the edges reflect biologically meaningful relationships that are resilient to random chance. The approach effectively distinguishes true regulatory patterns from experimental noise and biological stochasticity. To test the proposed method, we recover a gene regulatory network consisted of six pathway genes from single-cell RNA sequencing data comprising over $28.000$ lymphoblastoid cells. Furthermore, we unveil several triadic regulatory mechanisms. By merging quantum physics inspired techniques with computational biology, our method provides novel insights into gene regulation, with applications in disease mechanisms and precision medicine. |
| title | Tensor Network based Gene Regulatory Network Inference for Single-Cell Transcriptomic Data |
| topic | Molecular Networks Quantum Physics |
| url | https://arxiv.org/abs/2509.06891 |