title: 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.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305.
authors.forename: Zhixiang
authors.initials: Z
authors.lastname: Lin
authors.affiliation: Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
authors.forename: Can
authors.initials: C
authors.lastname: Yang
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520.|Department of Neuroscience, Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510.
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305.|Department of Electrical Engineering, Stanford University, Stanford, CA 94305.
authors.forename: John
authors.initials: J
authors.lastname: Duchi
authors.affiliation: Program of Computational Biology & Bioinformatics, Yale University, New Haven, CT 06511.
authors.forename: Yao
authors.initials: Y
authors.lastname: Fu
authors.affiliation: Academy of Mathematics & Systems Science, Chinese Academy of Sciences, Beijing 100080, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305.
authors.forename: Bai
authors.initials: B
authors.lastname: Jiang
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305.
authors.forename: Mahdi
authors.initials: M
authors.lastname: Zamanighomi
authors.affiliation: Department of Neuroscience, Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510.
authors.forename: Xuming
authors.initials: X
authors.lastname: Xu
authors.affiliation: Department of Neuroscience, Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510.
authors.forename: Mingfeng
authors.initials: M
authors.lastname: Li
authors.affiliation: Department of Neuroscience, Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510.|Department of Genetics, Yale School of Medicine, New Haven, CT 06510.|Department of Psychiatry, Section of Comparative Medicine, Program in Cellular Neuroscience, Neurodegeneration and Repair, Yale School of Medicine, New Haven, CT 06510.
authors.forename: Nenad
authors.initials: N
authors.lastname: Sestan
authors.affiliation: Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520; hongyu.zhao@yale.edu whwong@stanford.edu.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305; hongyu.zhao@yale.edu whwong@stanford.edu.|Department of Health Research & Policy, Stanford University, Stanford, CA 94305.
authors.forename: Wing Hung
authors.initials: WH
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001921:Brain / Q000378:metabolism*; D001931:Brain Mapping; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005091:Exons; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D015233:Models, Statistical; D025341:Principal Component Analysis; D017423:Sequence Analysis, RNA; D012984:Software; D059467:Transcriptome
keywords: confounding variation; dimension reduction; transcriptome