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Related Experiment Video

Updated: Jun 30, 2025

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

Joint inference of clonal structure using single-cell genome and transcriptome sequencing data.

Xiangqi Bai¹, Zhana Duren², Lin Wan^3,4

¹Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.

NAR Genomics and Bioinformatics

|March 15, 2024

Summary

This summary is machine-generated.

A new computational framework, CCNMF, integrates single-cell genome (scDNA) and transcriptome (scRNA) data to identify cell clones. This method links copy number and gene expression, revealing clonal structures in complex biological samples.

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Last Updated: Jun 30, 2025

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Transcriptome Analysis of Single Cells

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Published on: April 25, 2011

Area of Science:

Genomics
Transcriptomics
Computational Biology

Background:

High-throughput single-cell sequencing (scDNA and scRNA) allows cell-resolved tissue clone investigation.
Integrating heterogeneous scRNA and scDNA data from the same specimen for clonal analysis remains a challenge.

Purpose of the Study:

To develop a computational framework (CCNMF) for joint inference of clonal structure from matched scDNA and scRNA data.
To couple multi-omics single cells by linking copy number and gene expression profiles.

Main Methods:

Developed Coupled-Clone Non-negative Matrix Factorization (CCNMF).
CCNMF links scDNA copy number alterations with scRNA gene expression profiles.
Validated using simulated data and real-world cancer samples (ovarian, gastric).

Main Results:

CCNMF successfully resolved coexisting clones by correlating clonal genomes and transcriptomes.
Demonstrated high accuracy and robustness in both simulated and real-world datasets.
Enabled simultaneous resolution of genomic and transcriptomic clonal architecture.

Conclusions:

CCNMF is a powerful tool for integrating multi-omics single-cell data.
Facilitates understanding of gene expression changes alongside clonal genome alterations.
Sheds light on subclone genomic differences contributing to tumor evolution.