Simultaneous Dimension Reduction and Adjustment for Confounding Variation

Overview

Journal Proc Natl Acad Sci U S A

Specialty Science

Date 2016 Dec 9

PMID 27930330

Citations 20

Authors

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

Affiliations

Soon will be listed here.

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.

Citing Articles

PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.

Chen A, Clark K, Dewey B, DuVal A, Pellegrini N, Nair G PLoS Comput Biol. 2024; 20(7):e1012241.

PMID: 38985831 PMC: 11262650. DOI: 10.1371/journal.pcbi.1012241.

INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis.

Zhao K, Huang S, Lin C, Sham P, So H, Lin Z PLoS Genet. 2024; 20(3):e1011189.

PMID: 38484017 PMC: 10965063. DOI: 10.1371/journal.pgen.1011189.

Evaluating machine learning algorithms to Predict 30-day Unplanned REadmission (PURE) in Urology patients.

Welvaars K, van den Bekerom M, Doornberg J, van Haarst E BMC Med Inform Decis Mak. 2023; 23(1):108.

PMID: 37312177 PMC: 10262129. DOI: 10.1186/s12911-023-02200-9.

PLIN2-induced ectopic lipid accumulation promotes muscle ageing in gregarious locusts.

Guo S, Hou L, Dong L, Nie X, Kang L, Wang X Nat Ecol Evol. 2023; 7(6):914-926.

PMID: 37156891 DOI: 10.1038/s41559-023-02059-z.

Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches.

Liu X, Mei X Front Bioeng Biotechnol. 2023; 11:1156372.

PMID: 37139048 PMC: 10150883. DOI: 10.3389/fbioe.2023.1156372.

References

Das R, Dimitrova N, Xuan Z, Rollins R, Haghighi F, Edwards J . Computational prediction of methylation status in human genomic sequences. Proc Natl Acad Sci U S A. 2006; 103(28):10713-6. PMC: 1502297. DOI: 10.1073/pnas.0602949103. View

Kang H, Kawasawa Y, Cheng F, Zhu Y, Xu X, Li M . Spatio-temporal transcriptome of the human brain. Nature. 2011; 478(7370):483-9. PMC: 3566780. DOI: 10.1038/nature10523. View

Allen J, Davey H, Broadhurst D, Heald J, Rowland J, Oliver S . High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat Biotechnol. 2003; 21(6):692-6. DOI: 10.1038/nbt823. View

Risso D, Ngai J, Speed T, Dudoit S . Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol. 2014; 32(9):896-902. PMC: 4404308. DOI: 10.1038/nbt.2931. View

Zhang R, Lin Y . DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res. 2008; 37(Database issue):D455-8. PMC: 2686491. DOI: 10.1093/nar/gkn858. View

Gagnon-Bartsch J, Speed T . Using control genes to correct for unwanted variation in microarray data. Biostatistics. 2011; 13(3):539-52. PMC: 3577104. DOI: 10.1093/biostatistics/kxr034. View

Miller J, Ding S, Sunkin S, Smith K, Ng L, Szafer A . Transcriptional landscape of the prenatal human brain. Nature. 2014; 508(7495):199-206. PMC: 4105188. DOI: 10.1038/nature13185. View

Yang C, Wang L, Zhang S, Zhao H . Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping. Bioinformatics. 2013; 29(8):1026-34. PMC: 3624800. DOI: 10.1093/bioinformatics/btt075. View

Li J, Huang H, Bickel P, Brenner S . Comparison of D. melanogaster and C. elegans developmental stages, tissues, and cells by modENCODE RNA-seq data. Genome Res. 2014; 24(7):1086-101. PMC: 4079965. DOI: 10.1101/gr.170100.113. View

10.

Leek J, Johnson W, Parker H, Jaffe A, Storey J . The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012; 28(6):882-3. PMC: 3307112. DOI: 10.1093/bioinformatics/bts034. View

11.

Darmanis S, Sloan S, Zhang Y, Enge M, Caneda C, Shuer L . A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci U S A. 2015; 112(23):7285-90. PMC: 4466750. DOI: 10.1073/pnas.1507125112. View

12.

Parker H, Corrada Bravo H, Leek J . Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ. 2014; 2:e561. PMC: 4179553. DOI: 10.7717/peerj.561. View

13.

Johnson W, Li C, Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006; 8(1):118-27. DOI: 10.1093/biostatistics/kxj037. View

14.

Kulis M, Heath S, Bibikova M, Queiros A, Navarro A, Clot G . Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. Nat Genet. 2012; 44(11):1236-42. DOI: 10.1038/ng.2443. View

15.

Ringner M . What is principal component analysis?. Nat Biotechnol. 2008; 26(3):303-4. DOI: 10.1038/nbt0308-303. View

16.

Celniker S, Dillon L, Gerstein M, Gunsalus K, Henikoff S, Karpen G . Unlocking the secrets of the genome. Nature. 2009; 459(7249):927-30. PMC: 2843545. DOI: 10.1038/459927a. View

17.

Gerstein M, Rozowsky J, Yan K, Wang D, Cheng C, Brown J . Comparative analysis of the transcriptome across distant species. Nature. 2014; 512(7515):445-8. PMC: 4155737. DOI: 10.1038/nature13424. View

18.

Leek J, Storey J . Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 2007; 3(9):1724-35. PMC: 1994707. DOI: 10.1371/journal.pgen.0030161. View

19.

Sharov A, Piao Y, Matoba R, Dudekula D, Qian Y, VanBuren V . Transcriptome analysis of mouse stem cells and early embryos. PLoS Biol. 2003; 1(3):E74. PMC: 300684. DOI: 10.1371/journal.pbio.0000074. View

20.

Giordano T, Kuick R, Else T, Gauger P, Vinco M, Bauersfeld J . Molecular classification and prognostication of adrenocortical tumors by transcriptome profiling. Clin Cancer Res. 2009; 15(2):668-76. PMC: 2629378. DOI: 10.1158/1078-0432.CCR-08-1067. View