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

• 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

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...