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

PeerJ

article

Jessica C. Mar¹  Laurence de Torrente¹  Samuel Zimmerman¹  Deanne Taylor⁴  Yu Hasegawa¹  Christine A. Wells⁶ 

[1] Department of Systems and Computational Biology, Albert Einstein College of Medicine;Department of Epidemiology and Population Health, Albert Einstein College of Medicine;University of Queensland, Australian Institute for Bioengineering and Nanotechnology;Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia;Department of Pediatrics, University of Pennsylvania;Department of Anatomy and Neuroscience, University of Melbourne

关键词: Transcriptional regulation;    Gene expression variability;    Single cell analysis;    Bioinformatics;    Functional genomics;    Cellular heterogeneity;   

DOI  :  10.7717/peerj.3334

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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 and two methods of GSEA, and demonstrate that analyses using both statistics are useful for understanding transcriptional regulation. We also provide recommendations for the choice of variability statistic that have been informed through analyses on simulations and real data. Based on the datasets selected, we show how pathVar can be used to gain insight into expression variability of single cell versus bulk samples, different stem cell populations, and cancer versus normal tissue comparisons.

【 授权许可】

CC BY