Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns

Overview

Journal Front Genet

Date 2018 Jun 19

PMID 29910825

Authors

Jinyu Chen

Shihua Zhang

Affiliations

Soon will be listed here.

Abstract

The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools that can reveal the relationships among different layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization (NMF), and partial least squares (PLS). This package can integrate diverse types of genomic data (e.g., copy number variation, DNA methylation, gene expression, microRNA expression profiles, and/or gene network data) to identify the underlying modular patterns by each method. Particularly, we demonstrate the differences between these two classes of methods, which give users some suggestions about how to select a suitable method in the MIA package. MIA is a flexible tool which could handle a wide range of biological problems and data types. Besides, we also provide an executable version for users without a MATLAB license.

References

Nguyen D, Rocke D . Multi-class cancer classification via partial least squares with gene expression profiles. Bioinformatics. 2002; 18(9):1216-26. DOI: 10.1093/bioinformatics/18.9.1216. View

Li Y, Ngom A . The non-negative matrix factorization toolbox for biological data mining. Source Code Biol Med. 2013; 8(1):10. PMC: 3736608. DOI: 10.1186/1751-0473-8-10. View

Kasar S, Kim J, Improgo R, Tiao G, Polak P, Haradhvala N . Whole-genome sequencing reveals activation-induced cytidine deaminase signatures during indolent chronic lymphocytic leukaemia evolution. Nat Commun. 2015; 6:8866. PMC: 4686820. DOI: 10.1038/ncomms9866. View

Le Cao K, Boitard S, Besse P . Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics. 2011; 12:253. PMC: 3133555. DOI: 10.1186/1471-2105-12-253. View

Boulesteix A, Strimmer K . Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach. Theor Biol Med Model. 2005; 2:23. PMC: 1182396. DOI: 10.1186/1742-4682-2-23. View

Le Cao K, Gonzalez I, Dejean S . integrOmics: an R package to unravel relationships between two omics datasets. Bioinformatics. 2009; 25(21):2855-6. PMC: 2781751. DOI: 10.1093/bioinformatics/btp515. View

Nguyen D, Rocke D . Tumor classification by partial least squares using microarray gene expression data. Bioinformatics. 2002; 18(1):39-50. DOI: 10.1093/bioinformatics/18.1.39. View

Devarajan K . Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput Biol. 2008; 4(7):e1000029. PMC: 2447881. DOI: 10.1371/journal.pcbi.1000029. View

Lee D, Seung H . Learning the parts of objects by non-negative matrix factorization. Nature. 1999; 401(6755):788-91. DOI: 10.1038/44565. View

10.

Liquet B, de Micheaux P, Hejblum B, Thiebaut R . Group and sparse group partial least square approaches applied in genomics context. Bioinformatics. 2015; 32(1):35-42. DOI: 10.1093/bioinformatics/btv535. View

11.

Kowalski J, Dwivedi B, Newman S, Switchenko J, Pauly R, Gutman D . Gene integrated set profile analysis: a context-based approach for inferring biological endpoints. Nucleic Acids Res. 2016; 44(7):e69. PMC: 4838358. DOI: 10.1093/nar/gkv1503. View

12.

Brunet J, Tamayo P, Golub T, Mesirov J . Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci U S A. 2004; 101(12):4164-9. PMC: 384712. DOI: 10.1073/pnas.0308531101. View

13.

Pascual-Montano A, Carmona-Saez P, Chagoyen M, Tirado F, Carazo J, Pascual-Marqui R . bioNMF: a versatile tool for non-negative matrix factorization in biology. BMC Bioinformatics. 2006; 7:366. PMC: 1550731. DOI: 10.1186/1471-2105-7-366. View

14.

Zhang S, Li Q, Liu J, Zhou X . A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics. 2011; 27(13):i401-9. PMC: 3117336. DOI: 10.1093/bioinformatics/btr206. View

15.

Zhang S, Liu C, Li W, Shen H, Laird P, Zhou X . Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res. 2012; 40(19):9379-91. PMC: 3479191. DOI: 10.1093/nar/gks725. View

16.

Wu S, Joseph A, Hammonds A, Celniker S, Yu B, Frise E . Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks. Proc Natl Acad Sci U S A. 2016; 113(16):4290-5. PMC: 4843452. DOI: 10.1073/pnas.1521171113. View

17.

Kim H, Park H . Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics. 2007; 23(12):1495-502. DOI: 10.1093/bioinformatics/btm134. View

18.

Li W, Zhang S, Liu C, Zhou X . Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics. 2012; 28(19):2458-66. PMC: 3463121. DOI: 10.1093/bioinformatics/bts476. View

19.

Ray S, Bandyopadhyay S . A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs. BMC Bioinformatics. 2016; 17:121. PMC: 4784399. DOI: 10.1186/s12859-016-0952-6. View

20.

Huang X, Pan W, Park S, Han X, Miller L, Hall J . Modeling the relationship between LVAD support time and gene expression changes in the human heart by penalized partial least squares. Bioinformatics. 2004; 20(6):888-94. DOI: 10.1093/bioinformatics/btg499. View