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...

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Bibliographic Details
Published in:Biostatistics (Oxford, England) Vol. 13; no. 2; p. 204
Main Authors: Hansen, Kasper D, Irizarry, Rafael A, Wu, Zhijin
Format: Journal Article
Language:English
Published: England 01.04.2012
Subjects:
Algorithms
Analysis of Variance
Base Composition
Biostatistics
Databases, Nucleic Acid - statistics & numerical data
Gene Expression Profiling - statistics & numerical data
High-Throughput Nucleotide Sequencing - statistics & numerical data
Humans
Oligonucleotide Array Sequence Analysis - statistics & numerical data
Sequence Analysis, RNA - statistics & numerical data
ISSN:1468-4357, 1468-4357
Online Access:Get more information
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Summary: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 (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show that RNA-seq data demonstrate unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find guanine-cytosine content (GC-content) has a strong sample-specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here, we describe a statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content and quantile normalization to correct for global distortions.
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ISSN:1468-4357
1468-4357
DOI:10.1093/biostatistics/kxr054