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Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Richard, Arianne C 
Lyons, Paul A 
Peters, James E 
Flint, Shaun M 

Abstract

BACKGROUND: Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. RESULTS: Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. CONCLUSIONS: Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently.

Description

Keywords

Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis, Case-Control Studies, Gene Expression Profiling, Humans, Inflammatory Bowel Diseases, Leukocytes, Oligonucleotide Array Sequence Analysis, Organ Specificity, RNA, Statistics as Topic

Journal Title

BMC Genomics

Conference Name

Journal ISSN

1471-2164
1471-2164

Volume Title

15

Publisher

Springer Science and Business Media LLC
Sponsorship
Wellcome Trust (087007/Z/08/Z)
Wellcome Trust (080327/Z/06/Z)
Wellcome Trust (094227/Z/10/Z)
Medical Research Council (G0400929)
Wellcome Trust (100140/Z/12/Z)
Wellcome Trust (079895/Z/06/B)
Medical Research Council (MR/L019027/1)
Wellcome Trust (104064/Z/14/Z)