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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: Nov 2, 2025

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

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Published on: July 10, 2020

Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field

Yishu Wang¹, Xuehan Tian², Dongmei Ai^1,3

¹School of Mathematics and Physics, University of Science & Technology Beijing, China.

Biomed Research International

|June 7, 2021

Summary

This summary is machine-generated.

This study introduces a novel method to analyze single-cell RNA sequencing data, revealing cell heterogeneity in circulating tumor cells and triple-negative breast cancer. The approach enhances understanding of tumor composition and diversity for improved diagnostics.

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Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Single-cell RNA sequencing (scRNA-seq) advances the study of cell heterogeneity and gene coexpression networks.
Circulating tumor cells (CTCs) offer insights into tumor heterogeneity, particularly in triple-negative breast cancer (TNBC), but thorough characterization remains limited.
TNBC exhibits significant inter- and intratumor heterogeneity, with unclear links to immune functions.

Purpose of the Study:

To introduce and validate a new computational scheme for characterizing genotypic heterogeneity in single-cell data.
To apply this scheme to scRNA-seq data from CTCs and TNBC to reveal cellular and network relationships.
To improve the understanding of tumor composition and diversity through the analysis of coexpression gene modules.

Main Methods:

Utilized a mixture exponential family graph model for cell-cell network partitioning.
Employed a Markov random field model for flexible network rewiring.
Identified cell heterogeneity and network relationships based on high coexpression gene modules within distinct cell subsets.

Main Results:

The proposed scheme effectively models cell clusters and biological enrichment gene clusters.
Demonstrated the ability to infer differences in tumor composition and diversity by analyzing internal coexpression genes of cell clusters.
Provided a robust framework for dissecting heterogeneity in CTC and TNBC scRNA-seq data.

Conclusions:

The developed scheme offers a reasonable and effective approach for analyzing single-cell heterogeneity.
This method facilitates a deeper understanding of tumor composition and diversity by identifying key coexpression patterns.
The findings contribute to characterizing the complex heterogeneity of TNBC and CTCs, potentially informing clinical outcomes.