Paper ID sheet UCL-INMA-2013.05


Supervised normalization of large-scale omic datasets using blind source separation

Andrew E. Teschendorff, Emilie Renard, Pierre A. Absil
Biotechnological advances in genomics have heralded in a new era of quantitative molecular biology whereby it is now possible to routinely measure over tens of thousands of molecular features (e.g., gene expression levels) in hundreds if not thousands of patient samples. A key statistical challenge in the analysis of such large omic datasets is the presence of confounding sources of variation, which are often either unknown or only known with error. In this chapter, we present a supervised normalization method in which Blind Source Separation (BSS) is applied to identify the sources of variation, and demonstrate that this leads to improved statistical inference in subsequent supervised analyses. The statistical framework presented here will be of interest to biologists, bioinformaticians and signal processing experts alike.
Key words
In Ganesh R. Naik and Wenwu Wang (Eds.), "Blind Source Separation", Springer, pp. 465-497, 2014
BibTeX entry

booktitle={Blind Source Separation},
series={Signals and Communication Technology},
editor={Naik, Ganesh R. and Wang, Wenwu},
title={Supervised Normalization of Large-Scale Omic Datasets Using Blind Source Separation},
publisher={Springer Berlin Heidelberg},
author={Teschendorff, Andrew E. and Renard, Emilie and Absil, Pierre A.},