10.17863/CAM.28050
Richard, Arianne C
Lyons, Paul A
Peters, James E
Biasci, Daniele
0000-0003-3148-8152
Flint, Shaun M
Lee, James C
McKinney, Eoin F
Siegel, Richard M
Smith, Kenneth GC
Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.
Springer Science and Business Media LLC
2014
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
Apollo - University of Cambridge Repository
University of Cambridge
013meh722
2018-09-24
2018-09-24
2014-08-04
eng
Article
https://www.repository.cam.ac.uk/handle/1810/280685
10.1186/1471-2164-15-649
Attribution 4.0 International
open.access
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.
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