Recent technological advancements have enabled the profiling of a large number of genome-wide
features in individual cells. However, single-cell data present unique challenges that …

This article reviews the statistical theory underlying the edgeR software package for differential
expression of RNA-seq data. Negative binomial models are used to capture the quadratic …

Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in
different laboratories and at different times contain batch effects that may compromise the …

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of
individual cells. This provides biological resolution that cannot be matched by bulk RNA …

Motivation Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene
expression at the level of individual cells. However, preparing raw sequence data for further …

In recent years, RNA sequencing (RNA-seq) has become a very widely used technology for
profiling gene expression. One of the most common aims of RNA-seq profiling is to identify …

Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput
of single-cell transcriptomics studies. A key computational challenge when processing …

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely
used to identify binding sites for a target protein in the genome. An important scientific …

RNA sequencing (RNA-seq) is widely used to profile transcriptional activity in biological
systems. Here we present an analysis pipeline for differential expression analysis of RNA-seq …

A three-dimensional chromatin state underpins the structural and functional basis of the
genome by bringing regulatory elements and genes into close spatial proximity to ensure proper…