RT Journal Article
SR Electronic
T1 AC-PCA: simultaneous dimension reduction and adjustment for confounding variation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 040485
DO 10.1101/040485
A1 Zhixiang Lin
A1 Can Yang
A1 Ying Zhu
A1 John C. Duchi
A1 Yao Fu
A1 Yong Wang
A1 Bai Jiang
A1 Mahdi Zamanighomi
A1 Xuming Xu
A1 Mingfeng Li
A1 Nenad Sestan
A1 Hongyu Zhao
A1 Wing Hung Wong
YR 2016
UL http://biorxiv.org/content/early/2016/04/19/040485.abstract
AB 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.