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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: Dec 7, 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

TWO-SIGMA: A novel two-component single cell model-based association method for single-cell RNA-seq data.

Eric Van Buren¹, Ming Hu², Chen Weng³

¹Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Genetic Epidemiology

|September 29, 2020

Summary

This summary is machine-generated.

We introduce TWO-SIGMA, a novel method for analyzing single-cell RNA sequencing (scRNA-seq) data. This flexible approach accurately detects differential expression (DE) by accounting for complex biological data structures.

Keywords:

differential expression random effects model single-cell RNA sequencing zero-inflated model

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) generates complex, high-dimensional data.
Differential expression (DE) analysis is crucial for understanding cellular heterogeneity.
Existing methods may struggle with the unique characteristics of scRNA-seq data, such as drop-out events and within-sample correlations.

Purpose of the Study:

To develop a flexible and robust statistical method for differential expression analysis in scRNA-seq data.
To address limitations of current methods in handling zero-inflation, overdispersion, and sample correlations.
To provide interpretable effect size estimates and enable generalized DE testing.

Main Methods:

Developed TWO-SIGMA (TWO-component SInGle cell Model-based Association), a regression-based method for scRNA-seq DE analysis.
Employs a mixed-effects logistic regression for drop-out modeling and a mixed-effects negative binomial regression for mean expression modeling.
Accommodates correlated cells within samples using random effects, handles unbalanced designs, and controls for covariates like batch effects.

Main Results:

TWO-SIGMA demonstrated superior performance over alternative regression-based methods in simulations, showing improved type-I error control and power, especially with moderate within-sample correlation.
A real data analysis on pancreas islet cells highlighted TWO-SIGMA's flexibility and the significant impact of including random effects on biological interpretations.
The method successfully handles overdispersed, zero-inflated count data without log-transformation.

Conclusions:

TWO-SIGMA offers a comprehensive and flexible framework for scRNA-seq differential expression analysis.
Accurate modeling of within-sample correlations is critical for reliable DE findings in scRNA-seq studies.
The implemented R package 'twosigma' facilitates the application of this advanced method in biological research.