Estimating the proportion of microarray probes expressed in an RNA sample

Shi, Wei, de Graaf, Carolyn A., Kinkel, Sarah A., Achtman, Ariel H., Baldwin, Tracey, Schofield, Louis, Scott, Hamish S., Hilton, Douglas J., and Smyth, Gordon K. (2010) Estimating the proportion of microarray probes expressed in an RNA sample. Nucleic Acids Research, 38 (7). pp. 2168-2176.

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Abstract

A fundamental question in microarray analysis is the estimation of the number of expressed probes in different RNA samples. Negative control probes available in the latest microarray platforms, such as Illumina whole genome expression BeadChips, provide a unique opportunity to estimate the number of expressed probes without setting a threshold. A novel algorithm was proposed in this study to estimate the number of expressed probes in an RNA sample by utilizing these negative controls to measure background noise. The performance of the algorithm was demonstrated by comparing different generations of Illumina BeadChips, comparing the set of probes targeting well-characterized RefSeq NM transcripts with other probes on the array and comparing pure samples with heterogenous samples. Furthermore, hematopoietic stem cells were found to have a larger transcriptome than progenitor cells. Aire knockout medullary thymic epithelial cells were shown to have significantly less expressed probes than matched wild-type cells.

Item ID: 35864
Item Type: Article (Research - C1)
ISSN: 1362-4962
Additional Information:

© The Author(s) 2010. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Funders: National Health and Medical Research Council (NHMRC)
Projects and Grants: NHMRC Program Grant 490037
Date Deposited: 31 Oct 2014 01:27
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1103 Clinical Sciences > 110309 Infectious Diseases @ 50%
11 MEDICAL AND HEALTH SCIENCES > 1107 Immunology > 110799 Immunology not elsewhere classified @ 50%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970111 Expanding Knowledge in the Medical and Health Sciences @ 100%
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