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Updated: Jul 20, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion.

Weixu Wang¹, Xiaolan Zhou¹, Jing Wang¹

¹State Key Laboratory of Genetic Engineering, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, Center for Evolutionary Biology, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai 200438, P.R. China.

Briefings in Bioinformatics

|August 2, 2023

Summary

This summary is machine-generated.

ENIGMA accurately deconvolutes bulk RNA sequencing data into cell-type resolution using single-cell RNA sequencing information. This method enables cost-effective analysis of large patient cohorts for biological insights.

Keywords:

ADMM cell-type resolution transcriptome estimation matrix completion proximal-point algorithm

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Last Updated: Jul 20, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is vital for understanding cellular heterogeneity but is limited by high costs for large-scale studies.
Analyzing bulk tissue RNA-seq data lacks cell-type resolution, hindering detailed biological insights.

Purpose of the Study:

To introduce ENIGMA, a novel computational method for deconvoluting bulk RNA-seq data.
To enable cell-type resolution analysis of large patient cohorts by leveraging scRNA-seq data.
To quantify cell-type proportions and reconstruct cell-type-specific transcriptomes from bulk data.

Main Methods:

ENIGMA utilizes a matrix completion strategy to deconvolute bulk RNA-seq data.
It integrates information from scRNA-seq data to achieve cell-type resolution.
The method minimizes the difference between observed bulk transcriptomes and weighted combinations of cell-type expression profiles.

Main Results:

ENIGMA accurately quantifies cell-type proportions in bulk tissue samples.
The method successfully reconstructs cell-type-specific transcriptomes.
Validation on simulated and real disease-related tissue datasets demonstrated ENIGMA's efficacy.

Conclusions:

ENIGMA overcomes the cost limitations of scRNA-seq for large cohort studies.
This method provides a powerful tool for uncovering novel biological insights from bulk RNA-seq data.
ENIGMA facilitates a deeper understanding of cellular composition in various biological contexts.