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Activity Number: 81
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #319455
Title: AC-PCA: Simultaneous Dimension Reduction and Adjustment for Confounding Variation
Author(s): Zhixiang Lin* and Can Yang and Hongyu Zhao and Wing Hung Wong
Companies: Stanford University and Hong Kong Baptist University and Yale University and Stanford University
Keywords: Dimension reduction ; Confounding Variation ; Transcriptome

Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biologically or technically originated. In this study, we extend Principal Component Analysis to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding variation. We show that AC-PCA can adjust for a) variations across individual donors present in a human brain exon array dataset, and b) variations of different species in a model organism ENCODE RNA-Seq dataset. Our approach is able to recover the anatomical structure of neocortical regions, and to capture the shared variation among species during embryonic development. For gene selection purposes, we extend AC-PCA with sparsity constraints, and propose and implement an efficient algorithm. The methods developed in this paper can also be applied to more general settings.

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