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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: Aug 13, 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

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data.

Malindrie Dharmaratne¹, Ameya S Kulkarni^2,3, Atefeh Taherian Fard¹

¹Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, 4072, Australia.

|January 24, 2023

Summary

scShapes is a new statistical framework for analyzing single-cell RNA sequencing data. It identifies genes with varying expression patterns, offering deeper biological insights beyond average expression levels.

Keywords:

distribution shapes single-cell RNA-sequencing zero inflation

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Last Updated: Aug 13, 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

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Area of Science:

Genomics
Computational Biology
Biostatistics

Background:

Single-cell RNA sequencing (scRNA-seq) enables quantification of cell-to-cell variation by profiling individual cell transcriptomes.
Analyzing cell-cell variability in scRNA-seq data is crucial for identifying genes with homogeneous versus heterogeneous expression patterns, moving beyond average expression changes.

Purpose of the Study:

To introduce scShapes, a novel statistical framework for identifying differential distributions in scRNA-seq data.
To address limitations of mean-centric analyses by accounting for overdispersion and excess zeros inherent in scRNA-seq data.

Main Methods:

Utilized generalized linear models within the scShapes framework.
Quantified gene-specific cell-to-cell variability by testing for differences in expression distributions.
Flexibly adjusted for covariates as needed.

Main Results:

scShapes identified subtle expression variations independent of altered mean expression.
The framework detected biologically relevant genes missed by standard differential expression approaches.
Highlighted genes transitioning from unimodal to zero-inflated distributions, suggesting mechanisms like transcriptional bursting.

Conclusions:

scShapes expands understanding of gene expression's role in transcriptional regulation and cellular phenotypes.
The framework provides a powerful tool for analyzing complex single-cell expression patterns.
scShapes is available as a Bioconductor R package for broader accessibility.