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

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3334 ◽  
Author(s):  
Laurence de Torrente ◽  
Samuel Zimmerman ◽  
Deanne Taylor ◽  
Yu Hasegawa ◽  
Christine A. Wells ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Genomics ◽  
Statistical Tests ◽  
Mechanistic Model ◽  
New Method ◽  
Regulatory Control ◽  
Functional Interpretation ◽  
Expression Variability ◽  
Gene Sets ◽  
Phenotypic Group

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 presentpathVar,a new method that provides functional interpretation of gene expression variability changes at the level of pathways and gene sets.pathVaris 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 frompathVarare 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 howpathVarcan be used to gain insight into expression variability of single cell versus bulk samples, different stem cell populations, and cancer versus normal tissue comparisons.

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

2016 ◽  
Author(s):  
Laurence de Torrente ◽  
Samuel Zimmerman ◽  
Deanne Taylor ◽  
Yu Hasegawa ◽  
Christine A Wells ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Genomics ◽  
Statistical Tests ◽  
Mechanistic Model ◽  
New Method ◽  
Regulatory Control ◽  
Functional Interpretation ◽  
Expression Variability ◽  
Gene Sets ◽  
Phenotypic Group

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.

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

2016 ◽  
Author(s):  
Laurence de Torrente ◽  
Samuel Zimmerman ◽  
Deanne Taylor ◽  
Yu Hasegawa ◽  
Christine A Wells ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cancer Genomics ◽  
Statistical Tests ◽  
Mechanistic Model ◽  
New Method ◽  
Regulatory Control ◽  
Functional Interpretation ◽  
Expression Variability ◽  
Gene Sets ◽  
Phenotypic Group

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.

scholarly journals Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

2015 ◽  
Author(s):  
Jinting Guan ◽  
Ence Yang ◽  
Jizhou Yang ◽  
Yong Zeng ◽  
Guoli Ji ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Genetic Heterogeneity ◽  
Gene Expression Analysis ◽  
Autism Spectrum ◽  
Data Set ◽  
Expression Variability ◽  
Aberrant Gene Expression ◽  
Gene Sets ◽  
Aberrant Gene

AbstractAutism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method—aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70% classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.

scholarly journals Overdispersed gene expression characterizes schizophrenic brains

2018 ◽  
Author(s):  
Guangzao Huang ◽  
Daniel Osorio ◽  
Jinting Guan ◽  
Guoli Ji ◽  
James J. Cai
Keyword(s):  
Gene Expression ◽  
Brain Development ◽  
Genetic Disorders ◽  
Functional Gene ◽  
Functional Changes ◽  
Expression Variability ◽  
Gene Sets ◽  
Common Genetic Variants ◽  
Causal Variants ◽  
Endothelial Growth Factor

AbstractSchizophrenia (SCZ) is a severe, highly heterogeneous psychiatric disorder with varied clinical presentations. The polygenic genetic architecture of SCZ makes identification of causal variants daunting. Gene expression analyses have shown that SCZ may result in part from transcriptional dysregulation of a number of genes. However, most of these studies took the commonly used approach—differential gene expression analysis, assuming people with SCZ are a homogenous group, all with similar expression levels for any given gene. Here we show that the overall gene expression variability in SCZ is higher than that in an unaffected control (CTL) group. Specifically, we applied the test for equality of variances to the normalized expression data generated by the CommonMind Consortium (CMC) and identified 87 genes with significantly higher expression variances in the SCZ group than the CTL group. One of the genes with differential variability, VEGFA, encodes a vascular endothelial growth factor, supporting a vascular-ischemic etiology of SCZ. We also applied a Mahalanobis distance-based test for multivariate homogeneity of group dispersions to gene sets and identified 19 functional gene sets with higher expression variability in the SCZ group than the CTL group. Several of these gene sets are involved in brain development (e.g., development of cerebellar cortex, cerebellar Purkinje cell layer and neuromuscular junction), supporting that structural and functional changes in the cortex cause SCZ. Finally, using expression variability QTL (evQTL) analysis, we show that common genetic variants contribute to the increased expression variability in SCZ. Our results reveal that SCZ brains are characterized by overdispersed gene expression, resulting from dysregulated expression of functional gene sets pertaining to brain development, necrotic cell death, folic acid metabolism, and several other biological processes. Using SCZ as a model of complex genetic disorders with a heterogeneous etiology, our study provides a new conceptual framework for variability-centric analyses. Such a framework is likely to be important in the era of personalized medicine. (313 words)

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