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Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape.

Michio Iwata¹, Hiroaki Mutsumine², Yusuke Nakayama²

¹Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.

Nature Computational Science

|January 4, 2024

Summary

A new computational method, TIGERS, uses tensor imputation to predict missing gene expression data in single cells, revealing drug responses and pathway changes. This advances understanding of drug mechanisms at the single-cell level.

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Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Computational biology
Genomics
Pharmacology

Background:

Identifying drug responses at the single-cell level is crucial for medical and pharmaceutical research.
Existing methods struggle with comprehensive genome-wide transcriptomic analysis of drug effects in diverse human cells.

Purpose of the Study:

To develop a computational method for revealing the drug-induced single-cell transcriptomic landscape.
To predict missing gene-expression data and identify regulated pathway trajectories considering intercellular heterogeneity.

Main Methods:

Developed TIGERS (tensor-based imputation of gene-expression data at the single-cell level).
Employed tensor imputation to predict unobserved drug-induced single-cell gene-expression data.
Analyzed pathway trajectories based on imputed gene-expression profiles.

Main Results:

TIGERS outperformed existing imputation methods in data completion.
The method provided cell-type-specific transcriptomic responses for unobserved drugs, accurately predicting marker gene expression in pancreatic islets.
Identified single-cell-specific drug activities and pathway trajectories reflecting drug-induced pathway regulation.

Conclusions:

TIGERS offers a robust approach for predicting single-cell gene-expression data and understanding drug responses.
The method enhances the identification of cell-type-specific drug effects and pathway dynamics.
Expected to deepen the understanding of single-cell drug mechanisms at the pathway level.