ArticleBMC biotechnology2011
An adaptable method using human mixed tissue ratiometric controls for benchmarking performance on gene expression microarrays in clinical laboratories.
Article in BMC biotechnology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.Microbiome · 2020Article
- Cell-based reference samples designed with specific differences in microRNA biomarkers.BMC biotechnology · 2018Article
- Summarizing performance for genome scale measurement of miRNA: reference samples and metrics.BMC genomics · 2018Article
- Evaluation of the External RNA Controls Consortium (ERCC) reference material using a modified Latin square design.BMC biotechnology · 2016Article
- Article
- Using mixtures of biological samples as process controls for RNA-sequencing experiments.BMC genomics · 2015Article
- Synthetic spike-in standards improve run-specific systematic error analysis for DNA and RNA sequencing.PloS one · 2012Article
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3 authors.
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Abstract
backgroundMolecular biomarkers that are based on mRNA transcripts are being developed for the diagnosis and treatment of a number of diseases. DNA microarrays are one of the primary technologies being used to develop classifiers from gene expression data for clinically relevant outcomes. Microarray assays are highly multiplexed measures of comparative gene expression but have a limited dynamic range of measurement and show compression in fold change detection. To increase the clinical utility of microarrays, assay controls are needed that benchmark performance using metrics that are relevant to the analysis of genomic data generated with biological samples.
resultsRatiometric controls were prepared from commercial sources of high quality RNA from human tissues with distinctly different expression profiles and mixed in defined ratios. The samples were processed using six different target labeling protocols and replicate datasets were generated on high density gene expression microarrays. The area under the curve from receiver operating characteristic plots was calculated to measure diagnostic performance. The reliable region of the dynamic range was derived from log(2) ratio deviation plots made for each dataset. Small but statistically significant differences in diagnostic performance were observed between standardized assays available from the array manufacturer and alternative methods for target generation. Assay performance using the reliable range of comparative measurement as a metric was improved by adjusting sample hybridization conditions for one commercial kit.
conclusionsProcess improvement in microarray assay performance was demonstrated using samples prepared from commercially available materials and two metrics - diagnostic performance and the reliable range of measurement. These methods have advantages over approaches that use a limited set of external controls or correlations to reference sets, because they provide benchmark values that can be used by clinical laboratories to help optimize protocol conditions and laboratory proficiency with microarray assays.
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