Jump to ContentJump to Main Navigation

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year


IMPACT FACTOR increased in 2014: 1.127
5-year IMPACT FACTOR: 1.537
Rank 47 out of 122 in category Statistics & Probability in the 2014 Thomson Reuters Journal Citation Report/Science Edition

SCImago Journal Rank (SJR): 0.875
Source Normalized Impact per Paper (SNIP): 0.540

VolumeIssuePage

Parameter Estimation for the Exponential-Normal Convolution Model for Background Correction of Affymetrix GeneChip Data

Monnie McGee1 / Zhongxue Chen2

1Southern Methodist University

2University of Texas Southwestern Medical Center

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 5, Issue 1, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1237, September 2006

Publication History

Published Online:
2006-09-23

There are many methods of correcting microarray data for non-biological sources of error. Authors routinely supply software or code so that interested analysts can implement their methods. Even with a thorough reading of associated references, it is not always clear how requisite parts of the method are calculated in the software packages. However, it is important to have an understanding of such details, as this understanding is necessary for proper use of the output, or for implementing extensions to the model.In this paper, the calculation of parameter estimates used in Robust Multichip Average (RMA), a popular preprocessing algorithm for Affymetrix GeneChip brand microarrays, is elucidated. The background correction method for RMA assumes that the perfect match (PM) intensities observed result from a convolution of the true signal, assumed to be exponentially distributed, and a background noise component, assumed to have a normal distribution. A conditional expectation is calculated to estimate signal. Estimates of the mean and variance of the normal distribution and the rate parameter of the exponential distribution are needed to calculate this expectation. Simulation studies show that the current estimates are flawed; therefore, new ones are suggested. We examine the performance of preprocessing under the exponential-normal convolution model using several different methods to estimate the parameters.

Keywords: oligonucleotide arrays; RMA; Bioconductor; ROC curve; spike-in experiments

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Michela Lizier, Lorenzo Bomba, Andrea Minuti, Fatima Chegdani, Jessica Capraro, Barbara Tondelli, Raffaele Mazza, Maria Luisa Callegari, Erminio Trevisi, Filippo Rossi, Paolo Ajmone Marsan, and Franco Lucchini
Genes & Nutrition, 2013, Volume 8, Number 5, Page 465
[2]
W. Shi, C. A. de Graaf, S. A. Kinkel, A. H. Achtman, T. Baldwin, L. Schofield, H. S. Scott, D. J. Hilton, and G. K. Smyth
Nucleic Acids Research, 2010, Volume 38, Number 7, Page 2168

Comments (0)

Please log in or register to comment.