Expression and Quantification

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential...

A team led by researchers at the Fred Hutchison Cancer Research Center considers an increasingly popular study design where single-cell RNA-seq...

Total-RNA sequencing (total-RNA-seq) allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines...

Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Researchers at the Walter and Eliza Hall Institute of Medical Research present a major update to Glimma that brings improved interactivity and...

Researchers at the University of Alabama at Birmingham have developed Polar Gini Curve, a method for characterizing cluster markers by...

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target...

Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors, such as batch effects and biologically relevant variability. Existing batch effect removal methods typically...

A sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing..

Quantification of isoform abundance has been extensively studied at the mature-RNA level using RNA-seq but not at the level of precursor RNAs using nascent RNA sequencing. Researchers from Cold...

Single cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges...