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
• ISSN: 0888-7543; 1089-8646; 1089-8646
• DOI: 10.1016/j.ygeno.2012.08.003

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 methods require multiple samples to be processed in aggregate; consequently, each sample's output is influenced by other samples processed jointly. However, in personalized-medicine workflows, samples may arrive serially, so renormalizing all samples upon each new arrival would be impractical. We have developed Single Channel Array Normalization (SCAN), a single-sample technique that models the effects of probe-nucleotide composition on fluorescence intensity and corrects for such effects, dramatically increasing the signal-to-noise ratio within individual samples while decreasing variation across samples. In various benchmark comparisons, we show that SCAN performs as well as or better than competing methods yet has no dependence on external reference samples and can be applied to any single-channel microarray platform. ► The first completely intrinsic single-sample microarray normalization approach ► Designed for personalized medicine workflows where samples must be processed serially ► Outperforms existing methods on spike-in experiments and ‘real-world’ data sets ► Amplifies signal-to‐noise ratio by correcting for probe-level biases ► Supports combining data across different Affymetrix microarray platforms