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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: Jul 12, 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

Bayesian-frequentist hybrid inference framework for single cell RNA-seq analyses.

Gang Han¹, Dongyan Yan², Zhe Sun²

¹Epidemiology & Biostatistics, 212 Adriance Lab Rd, 1266 TAMU College Station, TX 77843.

Research Square

|October 27, 2023

Summary

This study introduces a Bayesian-frequentist hybrid (BFH) framework to enhance gene discovery in single-cell RNA sequencing (scRNA-seq) data. The BFH approach improves power for identifying key genes in complex diseases like idiopathic pulmonary fibrosis.

Keywords:

Bayesian-frequentist hybrid inference informative prior single-cell RNA-seq

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Last Updated: Jul 12, 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

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Published on: December 10, 2012

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is vital for understanding cell-specific disease mechanisms.
Identifying key genes in scRNA-seq data is challenging.
Pseudo-bulk methods, while common, may lack statistical power due to small sample sizes and high costs associated with scRNA-seq.

Conclusions:

The Bayesian-frequentist hybrid (BFH) framework offers a powerful and flexible approach for scRNA-seq data analysis.
BFH improves the identification of biologically relevant genes, aiding in disease mechanism research.
This method provides a valuable tool for future explorations of single-cell transcriptomics.