A single-sample microarray normalization method to facilitate personalized-medicine workflows

Gene-expression microarrays allow researchers to characterize biological phenomena in a high-throughput fashion but are subject to technological biases and inevitable variabilities that arise during sample collection and processing. Normalization techniques aim to correct such biases. Most existing...

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Bibliographic Details
Published in:	Genomics (San Diego, Calif.) Vol. 100; no. 6; pp. 337 - 344
Main Authors:	Piccolo, Stephen R., Sun, Ying, Campbell, Joshua D., Lenburg, Marc E., Bild, Andrea H., Johnson, W. Evan
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier Inc 01.12.2012 Elsevier
Subjects:	Analysis of Variance Biological and medical sciences biological properties and phenomena Diverse techniques Fluorescence Fundamental and applied biological sciences. Psychology gene expression Gene Expression Profiling Gene Expression Profiling - methods Genes. Genome Genetics of eukaryotes. Biological and molecular evolution High-Throughput Screening Assays High-Throughput Screening Assays - methods Humans Linear model Method methods Microarray microarray technology Mixture model Molecular and cellular biology Molecular genetics Normalization Oligonucleotide Array Sequence Analysis Oligonucleotide Array Sequence Analysis - methods Precision Medicine Precision Medicine - methods researchers Sample Size Selection Bias Signal-To-Noise Ratio Single-sample technique Workflow Single-sample technique Mixture model Normalization Method Microarray Linear model Medicine Genomics Models Technique
ISSN:	0888-7543, 1089-8646, 1089-8646
Online Access:	Get full text
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