Motivation In RNA-seq differential expression analysis, investigators aim to detect those
genes with changes in expression level across conditions, despite technical and biological …

The benefit of integrating batches of genomic data to increase statistical power is often
hindered by batch effects, or unwanted variation in data caused by differences in technical …

Variations in sample quality are frequently encountered in small RNA-sequencing
experiments, and pose a major challenge in a differential expression analysis. Removal of …

A primary challenge in the analysis of RNA-seq data is to identify differentially expressed
genes or transcripts while controlling for technical biases. Ideally, a statistical testing …

A basic task in the analysis of count data from RNA-seq is the detection of differentially
expressed genes. The count data are presented as a table which reports, for each sample …

High-throughput sequencing of cDNA (RNA-seq) is used extensively to characterize the
transcriptome of cells. Many transcriptomic studies aim at comparing either abundance …

A popular approach for comparing gene expression levels between (replicated) conditions
of RNA sequencing data relies on counting reads that map to features of interest. Within …

Experimental variance is a major challenge when dealing with high-throughput sequencing
data. This variance has several sources: sampling replication, technical replication …

In comparative high-throughput sequencing assays, a fundamental task is the analysis of
count data, such as read counts per gene in RNA-seq, for evidence of systematic changes …

Normalization is an essential step with considerable impact on high-throughput RNA
sequencing (RNA-seq) data analysis. Although there are numerous methods for read count …