pathVar: a new method for pathway-based interpretation of gene expression variability

, , , , ,
1 Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA
2 Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
3 Pediatrics, University of Pennsylvania, Philadelphia
4 Department of Food Science and Technology, University of California, Davis, Davis, California, United States
5 Department of Anatomy and Neuroscience, University of Melbourne, Melbourne, Victoria, Australia
6 Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
DOI
10.7287/peerj.preprints.2585v1
Published
Accepted
Subject Areas
Bioinformatics, Cell Biology, Statistics
Keywords
transcriptional regulation, gene expression variability, single cell analysis, bioinformatics, functional genomics, cellular heterogeneity
Copyright
© 2016 de Torrente et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
de Torrente L, Zimmerman S, Taylor D, Hasegawa Y, Wells CA, Mar JC. 2016. pathVar: a new method for pathway-based interpretation of gene expression variability. PeerJ Preprints 4:e2585v1

Abstract

Identifying the pathways that control a cellular phenotype is the first step to building a mechanistic model. Recent examples in developmental biology, cancer genomics, and neurological disease have demonstrated how changes in the variability of gene expression can highlight important genes that are under different degrees of regulatory control. Simple statistical tests exist to identify differentially-variable genes; however, methods for investigating how changes in gene expression variability in the context of pathways and gene sets are under-explored. Here we present pathVar, a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets. pathVar is based on a multinomial exact test, or an asymptotic Chi-squared test as a more computationally-efficient alternative. The method can be used for gene expression studies from any technology platform in all biological settings either with a single phenotypic group, or two-group comparisons. To demonstrate its utility, we applied the method to a diverse set of diseases, species and samples. Results from pathVar are benchmarked against analyses based on average expression via GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation.

Author Comment

This is a submission to PeerJ for review.

Supplemental Information

Supplementary Data containing Supplementary Figures and Tables

DOI: 10.7287/peerj.preprints.2585v1/supp-1

Text reporting results from the simulations performed to evaluate method performance and an investigation of the power of the method

DOI: 10.7287/peerj.preprints.2585v1/supp-2

Text outlining more details of the analyses using pathVar on human ESC and iPSC data sets

DOI: 10.7287/peerj.preprints.2585v1/supp-3

Text outlining more details on the analyses performed on other data sets used to evaluate the pathVar method

DOI: 10.7287/peerj.preprints.2585v1/supp-4

Raw code for functions used in the pathVar method

DOI: 10.7287/peerj.preprints.2585v1/supp-5

Raw code for class definitions used in pathVar package

DOI: 10.7287/peerj.preprints.2585v1/supp-6

A zip file containing the raw code files used to generate results in this manuscript

Files have been organized into the folders corresponding to the analyses represented in the Results sections of the manuscript.

DOI: 10.7287/peerj.preprints.2585v1/supp-7