Simultaneous dimension reduction and adjustment for confounding variation.

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.

authors

Lin Z,Yang C,Zhu Y,Duchi J,Fu Y,Wang Y,Jiang B,Zamanighomi M,Xu X,Li M,Sestan N,Zhao H,Wong WH

doi

10.1073/pnas.1617317113

subject

Has Abstract

pub_date

2016-12-20 00:00:00

pages

14662-14667

issue

51

eissn

0027-8424

issn

1091-6490

pii

1617317113

journal_volume

113

pub_type

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