A review of statistical methods for preprocessing oligonucleotide microarrays

Microarrays have become an indispensable tool in biomedical research. This powerful technology not only makes it possible to quantify a large number of nucleic acid molecules simultaneously, but also produces data with many sources of noise. A number of preprocessing steps are therefore necessary to...

Full description

Saved in:
Bibliographic Details
Published in:Statistical Methods in Medical Research 2009-12, Vol.18 (6), p.533-541
Main Author: Wu, Zhijin
Format: Article
Language:English
Subjects:
Algorithms
Biometry - methods
Computational Biology - methods
Data Interpretation, Statistical
Humans
Oligonucleotide Array Sequence Analysis - statistics & numerical data
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Microarrays have become an indispensable tool in biomedical research. This powerful technology not only makes it possible to quantify a large number of nucleic acid molecules simultaneously, but also produces data with many sources of noise. A number of preprocessing steps are therefore necessary to convert the raw data, usually in the form of hybridisation images, to measures of biological meaning that can be used in further statistical analysis. Preprocessing of oligonucleotide arrays includes image processing, background adjustment, data normalisation/transformation and sometimes summarisation when multiple probes are used to target one genomic unit. In this article, we review the issues encountered in each preprocessing step and introduce the statistical models and methods in preprocessing.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280209351924