EconPapers    
Economics at your fingertips  
 

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

McGee Monnie and Chen Zhongxue
Additional contact information
McGee Monnie: Southern Methodist University
Chen Zhongxue: University of Texas Southwestern Medical Center

Statistical Applications in Genetics and Molecular Biology, 2006, vol. 5, issue 1, 1-27

Abstract: 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.

Date: 2006
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1237 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:24

Ordering information: This journal article can be ordered from
https://www.degruyter.com/view/j/sagmb

DOI: 10.2202/1544-6115.1237

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

 
Page updated 2021-05-07
Handle: RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:24