Independent Component Analysis to Remove Batch Effects from Merged Microarray Datasets

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9838)

Abstract

Merging gene expression datasets is a simple way to increase the number of samples in an analysis. However experimental and data processing conditions, which are proper to each dataset, generally influence the expression values and can hide the biological effect of interest. It is then important to normalize the bigger merged dataset regarding those batch effects, as failing to adjust for them may adversely impact statistical inference. In this context, we propose to use a “spatiotemporal” independent component analysis to model the influence of those unwanted effects and remove them from the data. We show on a real dataset that our method allows to improve this modeling and helps to improve sample classification tasks.

Keywords

Batch effect removal Expression data Spatio-temporal independent component analysis 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.ICTEAM InstituteUniversité catholique de LouvainLouvain-la-NeuveBelgium
  2. 2.Tools4PatientGosseliesBelgium

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