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

BMC Genomics 2014 Aug 4;15:649. Epub 2014 Aug 4.

Cambridge Institute for Medical Research and Department of Medicine, University of Cambridge, Cambridge, UK.

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.

Download full-text PDF

Source
http://dx.doi.org/10.1186/1471-2164-15-649DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143561PMC
August 2014
16 Reads

Publication Analysis

Top Keywords

microarray
10
microarray users
8
microarray data
8
molecule counts
8
expression microarray
8
rna molecule
8
dilution datasets
8
gene expression
8
spike-in dilution
8
rna
5
expression
5
samples correlated
4
correlated well
4
system nanostring
4
measurements samples
4
gene measurements
4
technologies selected
4
nanostring technologies
4
selected genes
4
genes gene
4

Similar Publications