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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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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

Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.

Xiaoru Dong¹, Jack R Leary¹, Chuanhao Yang¹

¹Department of Biostatistics, College of Public Health and Health Professions, University of Florida, Gainesville, FL 32610, United States.

Briefings in Bioinformatics

|May 10, 2024

Summary

Researchers can now better analyze single-cell RNA sequencing (scRNA-seq) data using Escort. This framework evaluates dataset suitability for trajectory inference and quantifies properties, reducing decision burden in developmental biology studies.

Keywords:

RNA-seq pseudotime inference single cell trajectory inference

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Published on: April 25, 2011

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Last Updated: Jun 26, 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

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Computational Biology
Genomics
Developmental Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for studying cellular dynamics in development and differentiation.
Selecting appropriate computational methods and parameters for trajectory inference remains a significant challenge for researchers.
The performance of trajectory inference methods is highly dependent on dataset-specific characteristics and preprocessing choices.

Purpose of the Study:

To develop a novel framework, Escort, for evaluating dataset suitability for trajectory inference.
To quantify trajectory properties influenced by specific analysis decisions in scRNA-seq data.
To reduce uncertainty and decision-making burden in single-cell trajectory analysis.

Main Methods:

Developed Escort, a framework for assessing trajectory inference suitability.
Utilized trajectory-specific metrics to evaluate analysis outcomes and processing choices.
Implemented Escort as an R package and R/Shiny application for accessibility.

Main Results:

Escort provides data-driven assessments to guide trajectory inference.
The framework quantifies how analysis decisions impact trajectory properties.
Escort reduces the complexity and uncertainty associated with choosing analytical methods.

Conclusions:

Escort empowers researchers to make more informed decisions in single-cell trajectory analysis.
The framework facilitates deeper insights into dynamic biological processes at single-cell resolution.
Escort enhances the reliability and reproducibility of scRNA-seq trajectory inference studies.