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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: Jan 3, 2026

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

CellBench: R/Bioconductor software for comparing single-cell RNA-seq analysis methods.

Shian Su^1,2, Luyi Tian^1,2, Xueyi Dong^1,2

¹Epigenetics and Development Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia.

Bioinformatics (Oxford, England)

|November 29, 2019

Summary

This summary is machine-generated.

Benchmarking single-cell RNA sequencing analysis pipelines is crucial. CellBench R software automates method comparisons, enabling effective evaluation of complex bioinformatics workflows for improved single-cell data analysis.

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Last Updated: Jan 3, 2026

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

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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Published on: September 18, 2021

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Single-cell gene expression data analysis is rapidly advancing with numerous bespoke methods.
Consensus on optimal methods for single-cell analysis is still emerging.
Benchmarking existing methods and multi-step analysis pipelines is critical for reliable results.

Purpose of the Study:

To develop a software solution for effective benchmarking of single-cell analysis pipelines.
To facilitate reproducible and extensible evaluation of various combinations of bioinformatics methods.
To address the infeasibility of manual coding for exhaustive method combination testing.

Main Methods:

Developed CellBench, an R software package for method comparison.
Implemented task-centric and combinatorial approaches for pipeline evaluation.
Integrated automated execution of method combinations, timing, and tidyverse-compatible output.

Main Results:

CellBench enables comprehensive benchmarking of single-cell RNA sequencing (scRNA-seq) normalization, imputation, clustering, trajectory analysis, and data integration methods.
Performance metrics were obtained using data with available ground truth.
The software facilitates efficient comparison of various bioinformatics tools and pipelines.

Conclusions:

CellBench provides a robust and scalable platform for evaluating single-cell analysis methods and pipelines.
The software enhances reproducibility and extendibility in bioinformatics benchmarking.
CellBench is adaptable for benchmarking diverse bioinformatics analysis tasks beyond scRNA-seq.