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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 17, 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, Donghoon Lee^1,2,3,4, Jaroslav Bendl^1,2,3,4

¹Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Research Square

|May 19, 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-associated genes in specific cell types, improving human disease research.

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Last Updated: Jun 17, 2026

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

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

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

Published on: September 18, 2021

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

Genomics
Computational Biology
Neuroscience

Background:

Single-cell and single-nucleus transcriptomics generate massive datasets, offering insights into human disease biology.
Analyzing differential gene expression across subjects in these large datasets is computationally challenging due to complex statistical modeling and scalability issues.

Conclusions:

Dreamlet provides a powerful and efficient tool for analyzing large-scale single-cell transcriptomics data.
Enables deeper understanding of cell type-specific gene expression in human diseases like Alzheimer's.
Facilitates the analysis of complex cohort studies in genomics and disease research.