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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: Jun 25, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

BASiCS workflow: a step-by-step analysis of expression variability using single cell RNA sequencing data.

Alan O'Callaghan¹, Nils Eling^2,3, John C Marioni^4,5

¹MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.

|May 23, 2024

Summary

This summary is machine-generated.

This study introduces BASiCS, a computational workflow for analyzing gene expression variability in single-cell RNA sequencing data. It robustly quantifies cell heterogeneity and identifies significant changes between cell groups, accounting for technical noise.

Keywords:

Bayesian bioinformatics differential expression testing expression variability heterogeneity scRNAseq single-cell RNA sequencing transcriptional noise

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Last Updated: Jun 25, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

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Published on: May 16, 2020

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

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Cell-to-cell gene expression variability is crucial in biological systems like immunity and development.
Single-cell RNA sequencing (scRNA-seq) quantifies this heterogeneity but suffers from technical noise.

Purpose of the Study:

To present a computational workflow using the BASiCS Bioconductor package for robustly quantifying gene expression variability in scRNA-seq data.
To enable identification of cell heterogeneity within and between cell populations while accounting for technical noise.

Main Methods:

Utilized the BASiCS Bioconductor package for integrated data normalization, technical noise quantification, and downstream analysis.
Employed a probabilistic decision rule to identify changes in expression variability between cell populations.
Integrated quality control and data exploration using scater and scran Bioconductor packages.

Main Results:

BASiCS effectively quantifies expression variability within and between cell groups, distinguishing highly and lowly variable genes.
The workflow successfully identified changes in expression variability between cell populations, robust against technical noise and abundance differences.
Demonstrated a complete pipeline using a public scRNA-seq dataset.

Conclusions:

The BASiCS workflow provides a robust framework for analyzing gene expression variability in scRNA-seq data.
This approach enhances the understanding of cellular heterogeneity in complex biological systems.
Ensured reproducibility through a Docker image for the computational pipeline.