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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 6, 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

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, USA.

Biorxiv : the Preprint Server for Biology

|January 8, 2024

Summary

Researchers can now better analyze single-cell RNA sequencing data with Escort. This new framework helps select appropriate computational methods for trajectory inference, reducing uncertainty in developmental biology studies.

Keywords:

Pseudotime inference RNA-seq Trajectory inference single cell

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Published on: April 25, 2011

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Last Updated: Jul 6, 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
Developmental Biology
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for studying cellular development and differentiation.
Current trajectory inference methods face challenges due to method and parameter selection uncertainties.
Method performance in scRNA-seq analysis is highly dataset-specific.

Approach:

Developed Escort, a framework to evaluate dataset suitability for trajectory inference.
Escort quantifies trajectory properties influenced by analysis decisions.
Provides data-driven assessments to guide single-cell trajectory analysis.

Key Points:

Reduces uncertainty and decision burden in trajectory inference.
Enhances the selection of appropriate computational methods and parameters.
Facilitates informed decisions for analyzing dynamic biological processes.

Conclusions:

Escort offers an accessible R package and R/Shiny application.
Empowers researchers to gain new insights from scRNA-seq data.
Improves the reliability and interpretability of single-cell trajectory analyses.