发布时间 :2016-11-30  阅读次数 :1225

报告题目:AC-PCA: Simultaneous Dimension Reduction and Adjustment for Confounding Variation

报  告 人: Zhixiang Lin, Postdoctoral Scholar,

Department of Statistics,Stanford University

报告时间: 12月05日10:30

报告地点: 闵行校区生物药学楼树华多功能厅(800号)

联  系 人: 王涛 This e-mail address is being protected from spambots. You need JavaScript enabled to view it.



Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. 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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