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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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Related Experiment Video

Updated: Sep 11, 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 semi-supervised Bayesian approach for marker gene trajectory inference from single-cell RNA-seq data.

Junchao Wang¹, Ling Sun¹, Nana Wei²

¹School of Mathematics and Statistics, Shandong University at Weihai, Weihai, Shandong 264209, China.

Bioinformatics (Oxford, England)

|August 13, 2025

Summary

BayesTraj, a new semi-supervised Bayesian framework, accurately reconstructs cell differentiation trajectories using prior knowledge. This method improves pseudotime inference and lineage assignment from single-cell RNA sequencing data.

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Last Updated: Sep 11, 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

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

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Published on: November 2, 2020

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

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

Published on: December 10, 2012

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Trajectory inference is crucial for understanding cell development from single-cell transcriptomic data.
Existing unsupervised methods struggle with noise and sparsity, leading to inaccurate lineage assignments.

Purpose of the Study:

To develop a robust semi-supervised Bayesian framework for reconstructing cellular differentiation trajectories.
To improve the accuracy of pseudotime inference and lineage assignment in single-cell RNA sequencing data analysis.

Main Methods:

Introduced BayesTraj, a semi-supervised Bayesian framework incorporating lineage topology and marker-gene expression.
Modeled cellular differentiation as a probabilistic mixture of latent lineages with parametric gene dynamics.
Utilized Hamiltonian Monte Carlo (HMC) for posterior inference of pseudotime, lineage proportions, and gene parameters.

Main Results:

BayesTraj demonstrated superior performance in pseudotime inference compared to state-of-the-art methods on simulated and real datasets.
Provided per-cell branch-assignment probabilities for quantifying differentiation potential.
Enabled detection of lineage-specific gene expression through Bayesian model comparison.

Conclusions:

BayesTraj offers a robust and accurate approach for reconstructing cellular differentiation trajectories.
The framework enhances the analysis of cell-fate transitions and developmental hierarchies.
BayesTraj facilitates a deeper understanding of gene regulation during cellular differentiation.