pathVar

A new method for pathway-based interpretation of gene expression variability

Laurence de Torrente, Samuel Zimmerman, Deanne Taylor, Yu Hasegawa, Christine A. Wells, Jessica C. Mar

Research output: Contribution to journalArticle

4 Citations (Scopus)

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 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.

Original languageEnglish (US)
Article numbere3334
JournalPeerJ
Volume2017
Issue number5
DOIs
StatePublished - 2017

Fingerprint

Gene expression
Genes
Gene Expression
gene expression
Statistics
genes
statistics
Statistical tests
Developmental Biology
Stem cells
methodology
neoplasms
Neoplastic Stem Cells
mechanistic models
Genomics
stem cells
Tissue
statistical analysis
testing
Technology

Keywords

  • Bioinfor-matics
  • Cellular heterogeneity
  • Functional genomics
  • Gene expression variability
  • Single cell analysis
  • Transcriptional regulation

ASJC Scopus subject areas

  • Neuroscience(all)
  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

de Torrente, L., Zimmerman, S., Taylor, D., Hasegawa, Y., Wells, C. A., & Mar, J. C. (2017). pathVar: A new method for pathway-based interpretation of gene expression variability. PeerJ, 2017(5), [e3334]. https://doi.org/10.7717/peerj.3334

pathVar : A new method for pathway-based interpretation of gene expression variability. / de Torrente, Laurence; Zimmerman, Samuel; Taylor, Deanne; Hasegawa, Yu; Wells, Christine A.; Mar, Jessica C.

In: PeerJ, Vol. 2017, No. 5, e3334, 2017.

Research output: Contribution to journalArticle

de Torrente, L, Zimmerman, S, Taylor, D, Hasegawa, Y, Wells, CA & Mar, JC 2017, 'pathVar: A new method for pathway-based interpretation of gene expression variability', PeerJ, vol. 2017, no. 5, e3334. https://doi.org/10.7717/peerj.3334
de Torrente L, Zimmerman S, Taylor D, Hasegawa Y, Wells CA, Mar JC. pathVar: A new method for pathway-based interpretation of gene expression variability. PeerJ. 2017;2017(5). e3334. https://doi.org/10.7717/peerj.3334
de Torrente, Laurence ; Zimmerman, Samuel ; Taylor, Deanne ; Hasegawa, Yu ; Wells, Christine A. ; Mar, Jessica C. / pathVar : A new method for pathway-based interpretation of gene expression variability. In: PeerJ. 2017 ; Vol. 2017, No. 5.
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