Open AccessAn Imputation Approach for Oligonucleotide Microarrays
Author(s) -
Ming Li,
Yalu Wen,
Qing Lu,
Wenjiang Fu
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0058677
Subject(s) - dna microarray , imputation (statistics) , genotyping , computer science , computational biology , replicate , normalization (sociology) , oligonucleotide , snp genotyping , data mining , microarray , pattern recognition (psychology) , biology , artificial intelligence , genetics , gene , gene expression , statistics , missing data , genotype , machine learning , mathematics , sociology , anthropology
Oligonucleotide microarrays are commonly adopted for detecting and qualifying the abundance of molecules in biological samples. Analysis of microarray data starts with recording and interpreting hybridization signals from CEL images. However, many CEL images may be blemished by noises from various sources, observed as “bright spots”, “dark clouds”, and “shadowy circles”, etc. It is crucial that these image defects are correctly identified and properly processed. Existing approaches mainly focus on detecting defect areas and removing affected intensities. In this article, we propose to use a mixed effect model for imputing the affected intensities. The proposed imputation procedure is a single-array-based approach which does not require any biological replicate or between-array normalization. We further examine its performance by using Affymetrix high-density SNP arrays. The results show that this imputation procedure significantly reduces genotyping error rates. We also discuss the necessary adjustments for its potential extension to other oligonucleotide microarrays, such as gene expression profiling. The R source code for the implementation of approach is freely available upon request.