Removing technical variability in RNA-seq data using conditional quantile normalization

Removing technical variability in RNA-seq data using conditional quantile normalization

The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade's worth of statistical methodology development. The recently developed RNA sequencing (RN...

CQN (Conditional Quantile Normalization)

An Integrated Approach for RNA-seq Data Normalization

BACKGROUND DNA copy number alteration is common in many cancers. Studies have shown that insertion or deletion of DNA sequences can directly alter gene expression, and significant correlation exists between DNA copy number and gene expression. Data normalization is a critical step in the analysis of gene expression generated by RNA-seq technology. Successful normalization reduces/removes unwant...

EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq

In this document, we show how to conduct Exploratory Data Analysis (EDA) and normalization for a typical RNA-Seq experiment using the package EDASeq . One can think of EDA for RNA-Seq as a two-step process: “read-level” EDA helps in discovering lanes with low sequencing depths, quality issues, and unusual nucleotide frequencies, while “gene-level” EDA can capture mislabeled lanes, issues with d...

The Impact of Normalization Methods on RNA-Seq Data Analysis

High-throughput sequencing technologies, such as the Illumina Hi-seq, are powerful new tools for investigating a wide range of biological and medical problems. Massive and complex data sets produced by the sequencers create a need for development of statistical and computational methods that can tackle the analysis and management of data. The data normalization is one of the most crucial steps ...