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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessArticle
Microarrays 2015, 4(4), 671-689; doi:10.3390/microarrays4040671

Mining the Dynamic Genome: A Method for Identifying Multiple Disease Signatures Using Quantitative RNA Expression Analysis of a Single Blood Sample

1
GeneNews Ltd., 445 Apple Creek Blvd. Unit 220, Markham, ON L3R 9X7, Canada
2
Shanghai Biomedical Laboratory, Shanghai 200436, China
3
GeneNews Diagnostics Sdn Bhd, 213 Macalister Road, Georgetown, 10400 Penang, Malaysia
*
Authors to whom correspondence should be addressed.
Academic Editor: Stephen J. Walker
Received: 26 September 2015 / Revised: 14 November 2015 / Accepted: 24 November 2015 / Published: 10 December 2015
View Full-Text   |   Download PDF [5228 KB, uploaded 10 December 2015]   |  

Abstract

Background: Blood has advantages over tissue samples as a diagnostic tool, and blood mRNA transcriptomics is an exciting research field. To realize the full potential of blood transcriptomic investigations requires improved methods for gene expression measurement and data interpretation able to detect biological signatures within the “noisy” variability of whole blood. Methods: We demonstrate collection tube bias compensation during the process of identifying a liver cancer-specific gene signature. The candidate probe set list of liver cancer was filtered, based on previous repeatability performance obtained from technical replicates. We built a prediction model using differential pairs to reduce the impact of confounding factors. We compared prediction performance on an independent test set against prediction on an alternative model derived by Weka. The method was applied to an independent set of 157 blood samples collected in PAXgene tubes. Results: The model discriminated liver cancer equally well in both EDTA and PAXgene collected samples, whereas the Weka-derived model (using default settings) was not able to compensate for collection tube bias. Cross-validation results show our procedure predicted membership of each sample within the disease groups and healthy controls. Conclusion: Our versatile method for blood transcriptomic investigation overcomes several limitations hampering research in blood-based gene tests. View Full-Text
Keywords: blood transcriptomics; genomics; microarray; methodology for data analysis; diagnostics blood transcriptomics; genomics; microarray; methodology for data analysis; diagnostics
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chao, S.; Cheng, C.; Liew, C.-C. Mining the Dynamic Genome: A Method for Identifying Multiple Disease Signatures Using Quantitative RNA Expression Analysis of a Single Blood Sample. Microarrays 2015, 4, 671-689.

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