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Related Experiment Video

Updated: Sep 5, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Deconvolution analysis of cell-type expression from bulk tissues by integrating with single-cell expression

Yutong Luo¹, Ruzong Fan^1,2

¹Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.

Genetic Epidemiology

|July 5, 2022

Summary

New mixed models accurately reconstruct cellular expression fractions from bulk tissue samples, outperforming traditional methods. These models enhance understanding of complex traits and disease susceptibility by analyzing cell-type compositions.

Keywords:

bulk tissues cellular abundances cellular expression patterns mixed-effect models scRNA-seq

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Last Updated: Sep 5, 2025

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Published on: October 25, 2018

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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

Area of Science:

Genomics
Computational Biology
Biostatistics

Background:

Understanding phenotypic variations and disease susceptibility requires deciphering cell-type tissue compositions.
Bulk tissue analysis can evaluate cellular abundances and gene expression patterns using transcriptome profiles.

Purpose of the Study:

To develop and evaluate statistical models for reconstructing cellular expression fractions from bulk tissue samples.
To compare the performance of fixed and mixed-effect models against established methods like CIBERSORTx.

Main Methods:

Development of fixed and mixed-effect models to estimate cellular expression fractions using single-cell RNA-sequencing (scRNA-seq) reference data.
Benchmark evaluations and real data analysis comparing proposed models with CIBERSORTx.
Simulation studies to assess model performance under data heterogeneity.

Main Results:

Mixed-effect models provide comparable or superior results to CIBERSORTx in reconstructing cellular expression fractions.
Mixed models outperform fixed models in both benchmark and real data analyses.
Mixed models with heterogeneous variance-covariance are advantageous when scRNA-seq data exhibits heterogeneity.

Conclusions:

The proposed mixed models offer a robust and complementary tool for dissecting bulk tissues using scRNA-seq data.
These models improve the accuracy of estimating cell-type abundances and gene expression profiles.
The findings contribute to a better understanding of complex traits and disease susceptibility by elucidating cellular compositions.