PT  - JOURNAL ARTICLE
AU  - Zhixiang Lin
AU  - Can Yang
AU  - Ying Zhu
AU  - John C. Duchi
AU  - Yao Fu
AU  - Yong Wang
AU  - Bai Jiang
AU  - Mahdi Zamanighomi
AU  - Xuming Xu
AU  - Mingfeng Li
AU  - Nenad Sestan
AU  - Hongyu Zhao
AU  - Wing Hung Wong
TI  - AC-PCA: simultaneous dimension reduction and adjustment for confounding variation
AID  - 10.1101/040485
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 040485
4099  - http://biorxiv.org/content/early/2016/04/19/040485.short
4100  - http://biorxiv.org/content/early/2016/04/19/040485.full
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.