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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jun 24, 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

Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.

Khong-Loon Tiong¹, Dmytro Luzhbin¹, Chen-Hsiang Yeang²

¹Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.

BMC Bioinformatics

|June 12, 2024

Summary

This summary is machine-generated.

This study introduces backward deconvolution, a novel framework integrating bulk and single-cell RNA sequencing data to enhance transcriptomic heterogeneity analysis. It effectively selects deconvolution algorithms and models cell type composition in complex tissues.

Keywords:

Deconvolution Heterogeneity Probabilistic graphical models Single-cell RNASeq data

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Last Updated: Jun 24, 2025

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

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (sc-RNASeq) offers high resolution but suffers from noise and missing data.
Bulk RNA sequencing provides robust, complete data but lacks cell composition details.
Integrating these datasets can overcome individual limitations for comprehensive transcriptomic analysis.

Purpose of the Study:

To develop a novel framework, termed backward deconvolution, for integrating bulk and sc-RNASeq data.
To leverage the strengths of both data types for more accurate inference of transcriptomic heterogeneity.
To establish a robust method for model selection among deconvolution algorithms and hypothesis testing for cell type composition.

Main Methods:

Developed a probabilistic graphical model framework integrating bulk and sc-RNASeq data.
Employed multiple deconvolution algorithms to factorize bulk data and construct cell-level expression models.
Utilized log-likelihood scores for model comparison and developed a criterion for handling missing data in sc-RNASeq.
Validated the backward deconvolution approach across multiple in-silico and real-world biological datasets.

Main Results:

Backward deconvolution effectively selects deconvolution algorithms, showing strong correlations between log-likelihood scores and accuracy in both simulated and real data.
Analysis of autism spectrum disorder brains revealed higher astrocyte and lower NRGN-expressing neuron fractions compared to controls.
In tumor datasets (breast cancer, low-grade gliomas), a model of subtype dominance by a single cell type outperformed models assuming mixed cell populations.

Conclusions:

Backward deconvolution serves as a reliable model selection tool for deconvolution algorithms.
The framework aids in discerning hypotheses regarding cell type composition in heterogeneous biological specimens.
This integrated approach enhances the understanding of transcriptomic heterogeneity in complex tissues.