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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: Sep 27, 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

Processing single-cell RNA-seq datasets using SingCellaR.

Guanlin Wang^1,2, Wei Xiong Wen^1,2, Adam J Mead^1,3

¹MRC Molecular Haematology Unit, MRC WIMM, University of Oxford, Oxford OX3 9DS, UK.

|April 8, 2022

Summary

This summary is machine-generated.

This study introduces SingCellaR, an R package for analyzing complex single-cell RNA sequencing data. It offers a comprehensive protocol for interrogating multiple datasets, simplifying transcriptome analysis for researchers.

Keywords:

Bioinformatics RNAseq Single Cell Stem Cells Systems biology

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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:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) generates highly complex data.
Analyzing multiple scRNA-seq datasets presents significant computational challenges.
Existing platforms may not adequately address multi-dataset integration.

Purpose of the Study:

To present a comprehensive analytical protocol for interrogating multiple scRNA-seq datasets.
To introduce and demonstrate the utility of the SingCellaR R package.
To facilitate general single-cell transcriptome analyses.

Main Methods:

Development and application of the SingCellaR analysis package in R.
Implementation of bespoke pipelines for data analysis and visualization.
Integration with existing computational tools for enhanced analysis.

Main Results:

A detailed protocol for multi-dataset interrogation using SingCellaR is provided.
Demonstration of data analysis and visualization steps for human hematopoietic stem and progenitor cells.
SingCellaR is shown to be applicable to general single-cell transcriptome analyses.

Conclusions:

SingCellaR offers a robust solution for the computational challenges in multi-dataset scRNA-seq analysis.
The protocol enables comprehensive analysis and visualization of complex transcriptomic data.
This tool supports researchers in advancing single-cell genomics studies.