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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 microarray-based...

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Updated: Jun 21, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet.

Gabriel E Hoffman^1,2,3,4, Donghoon Lee^1,2,3,5, Jaroslav Bendl^1,2,3,5

¹Center for Disease Neurogenomics.

Biorxiv : the Preprint Server for Biology

|March 30, 2023

Summary

This summary is machine-generated.

This study introduces dreamlet, an R package for analyzing large single-cell transcriptomics datasets. It efficiently identifies disease-related gene expression changes in specific cell types across many individuals.

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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

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Published on: July 29, 2022

Area of Science:

Genomics
Computational Biology
Neuroscience

Background:

Single-cell transcriptomics generates large datasets for disease research.
Analyzing differential gene expression across subjects is computationally challenging.
Existing methods struggle with large-scale data and complex statistical models.

Purpose of the Study:

To develop an efficient and statistically robust R package for differential gene expression analysis in large-scale single-cell transcriptomics studies.
To address the challenges of statistical modeling and scalability in analyzing complex human disease data.
To enable deeper insights into cell type-specific disease biology.

Main Methods:

Utilizes a pseudobulk approach combined with precision-weighted linear mixed models.
The open-source R package 'dreamlet' is designed for large cohort data.
Implements methods to control false positive rates and support complex statistical models.

Main Results:

Dreamlet demonstrates significantly faster computation and lower memory usage compared to existing workflows.
The package effectively identifies differentially expressed genes across cell clusters in large datasets.
Validated on published datasets and a novel dataset of 1.4 million single nuclei from Alzheimer's disease and control brains.

Conclusions:

Dreamlet provides a computationally efficient and statistically sound solution for analyzing large-scale single-cell transcriptomics data.
Facilitates the identification of cell type-specific gene expression changes associated with human diseases.
Offers a valuable tool for advancing our understanding of disease mechanisms using large cohort studies.