A Single-sample Microarray Normalization Method to Facilitate Personalized-medicine Workflows

Keywords

Method Normalization Microarray, Linear model, Mixture model, Single-sample technique

Abstract

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.

Original Publication Citation

Piccolo SR, Sun Y, Campbell JD, Lenburg ME, Bild AH, Johnson WE. “A single-sample microarray normalization method to facilitate personalized-medicine workflows.” Genomics 2012, 100:6, 337-344

Document Type

Peer-Reviewed Article

Publication Date

2012-08-19

Publisher

Elsevier

Language

English

College

Life Sciences

Department

Biology

University Standing at Time of Publication

Associate Professor

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