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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Published on: March 5, 2022

ctPath: Demixing pathway crosstalk effect from transcriptomics data for differential pathway identification.

Xin-Ping Xie¹, Bin Gan², Wulin Yang³

¹School of Mathematics and Physics, Anhui Jianzhu University, Hefei, Anhui, China.

Journal of Biomedical Informatics

|July 31, 2017

Summary

This summary is machine-generated.

This study introduces ctPath, a novel method for identifying differentially expressed pathways (DEPs) in transcriptomics data. CtPath accounts for pathway crosstalk, improving the robust detection of DEPs and correcting pathway annotation errors.

Keywords:

Cancer Differential pathways Gene expression Non-negative matrix factorization

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Identifying differentially expressed pathways (DEPs) is crucial for understanding disease mechanisms and developing treatments.
Gene expression data is complex and noisy, making robust pathway analysis challenging.
Pathway crosstalk, where different biological pathways interact, is often overlooked in traditional analysis.

Purpose of the Study:

To develop a novel method, ctPath, for identifying DEPs by explicitly modeling pathway crosstalk.
To improve the accuracy and robustness of DEP identification from high-dimensional transcriptomics data.
To address limitations in current pathway analysis, including noise and inaccurate pathway annotations.

Main Methods:

Proposed a pathway crosstalk-based transcriptomics data analysis method (ctPath).
Modeled gene expression as a sparse non-negative linear combination of hidden pathway signals.
Mapped high-dimensional transcriptomics data to a low-dimensional pathway space to capture pathway activity and crosstalk effects.

Main Results:

CtPath effectively identifies DEPs by removing pathway crosstalk effects.
The method demonstrates superior performance compared to existing approaches on both simulated and real cancer data.
CtPath can correct incomplete and inaccurate pathway annotations found in public databases.

Conclusions:

CtPath offers a robust and accurate approach for identifying differentially expressed pathways.
The method enhances our understanding of tumor etiology and aids in clinical cancer treatment by revealing pathway activities.
CtPath provides a valuable tool for transcriptomics data analysis, with R code available for non-commercial use.