Simultaneous dimension reduction and adjustment for confounding variation

Zhixiang Lin, Can YANG, Ying Zhu, John Duchi, Yao Fu, Yong Wang, Bai Jiang, Mahdi Zamanighomi, Xuming Xu, Mingfeng Li, Nenad Sestan, Hongyu Zhao*, Wing Hung Wong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

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 (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We show that AC-PCA can adjust for (i) variations across individual donors present in a human brain exon array dataset and (ii) variations of different species in a model organism ENCODE RNA sequencing 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. The R package and MATLAB source code are available at https://github.com/linzx06/AC-PCA.

Original languageEnglish
Pages (from-to)14662-14667
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume113
Issue number51
DOIs
Publication statusPublished - 20 Dec 2016

Scopus Subject Areas

  • General

User-Defined Keywords

  • Confounding variation
  • Dimension reduction
  • Transcriptome

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