Linear Combination Test for Hierarchical Gene Set Analysis

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiaoming Wang ◽  
Irina Dinu ◽  
Wei Liu ◽  
Yutaka Yasui
Keyword(s):  
Gene Expression ◽  
Linear Combination ◽  
Covariance Matrix ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Hotelling's T2 ◽  
Gene Sets ◽  
Hotelling’S T2 ◽  
Combination Test ◽  
Linear Combination Test

Gene-set analysis (GSA) aims to identify sets of differentially expressed genes by a phenotype in DNA microarray studies. Challenges occur due to the salient characteristics of the data: (1) the number of genes is far larger than the number of observations; (2) gene expression measurements, especially within each gene set, can be highly correlated; and (3) the number of gene sets that can be examined is large and increasing rapidly. These challenges call for gene-set testing procedures that have both efficiency in computation for large GSAs and high power in the presence of the high correlation.We propose a new GSA approach called Linear Combination Test (LCT), incorporating the covariance matrix estimator of gene expression into the test statistic. The proposed LCT and two other GSA methods, a mod-ification of Hotelling’s T2 using a shrinkage covariance matrix and our SAM-GS (Dinu et. al. 2007), the two methods that have been reported by Tsai and Chen (2009) to perform best in terms of power, are evaluated in simulation studies and a real microarray study. The LCT method is more computationally efficient than the modified Hotelling’s T2 and approximates the superb power of the modified Hotelling’s T2. LCT is slightly faster than SAM-GS, but more powerful, due to incorporating the covariance matrix estimator. An extra step to enhance the interpretation of GSA results is also proposed in the form of a hierarchical LC (HLC) testing procedure, providing scientists useful hierarchical information on gene sets that LCT identified as differentially expressed.Availability: A free R-code to perform LCT-GSA and HLC test is available at http://www.ualberta.ca/~yyasui/homepage.html.

scholarly journals Linear combination test for gene set analysis of a continuous phenotype

2013 ◽  
Vol 14 (1) ◽  
Author(s):  
Irina Dinu ◽  
Xiaoming Wang ◽  
Linda E Kelemen ◽  
Shabnam Vatanpour ◽  
Saumyadipta Pyne
Keyword(s):  
Linear Combination ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Combination Test ◽  
Continuous Phenotype ◽  
Linear Combination Test

scholarly journals Longitudinal linear combination test for gene set analysis

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Elham Khodayari Moez ◽  
Morteza Hajihosseini ◽  
Jeffrey L. Andrews ◽  
Irina Dinu
Keyword(s):  
Linear Combination ◽  
Pathway Analysis ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Sets ◽  
Wide Range ◽  
Combination Test ◽  
Microarray Studies ◽  
Study Designs ◽  
Linear Combination Test

Abstract Background Although microarray studies have greatly contributed to recent genetic advances, lack of replication has been a continuing concern in this area. Complex study designs have the potential to address this concern, though they remain undervalued by investigators due to the lack of proper analysis methods. The primary challenge in the analysis of complex microarray study data is handling the correlation structure within data while also dealing with the combination of large number of genetic measurements and small number of subjects that are ubiquitous even in standard microarray studies. Motivated by the lack of available methods for analysis of repeatedly measured phenotypic or transcriptomic data, herein we develop a longitudinal linear combination test (LLCT). Results LLCT is a two-step method to analyze multiple longitudinal phenotypes when there is high dimensionality in response and/or explanatory variables. Alternating between calculating within-subjects and between-subjects variations in two steps, LLCT examines if the maximum possible correlation between a linear combination of the time trends and a linear combination of the predictors given by the gene expressions is statistically significant. A generalization of this method can handle family-based study designs when the subjects are not independent. This method is also applicable to time-course microarray, with the ability to identify gene sets that exhibit significantly different expression patterns over time. Based on the results from a simulation study, LLCT outperformed its alternative: pathway analysis via regression. LLCT was shown to be very powerful in the analysis of large gene sets even when the sample size is small. Conclusions This self-contained pathway analysis method is applicable to a wide range of longitudinal genomics, proteomics, metabolomics (OMICS) data, allows adjusting for potentially time-dependent covariates and works well with unbalanced and incomplete data. An important potential application of this method could be time-course linkage of OMICS, an attractive possibility for future genetic researchers. Availability: R package of LLCT is available at: https://github.com/its-likeli-jeff/LLCT

scholarly journals Excitatory/inhibitory imbalance in autism: the role of glutamate and GABA gene-sets in symptoms and cortical brain structure

2021 ◽  
Author(s):  
Viola Hollestein ◽  
Geert Poelmans ◽  
Natalie Forde ◽  
Christian F Beckmann ◽  
Christine Ecker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sensory Processing ◽  
Brain Structure ◽  
Symptom Severity ◽  
Expression Profiles ◽  
Brain Regions ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Gene Sets ◽  
Autism Symptomatology

Background: The excitatory/inhibitory (E/I) imbalance hypothesis posits that an imbalance between excitatory (glutamatergic) and inhibitory (GABAergic) mechanisms underlies the behavioral characteristics of autism spectrum disorder (autism). However, how E/I imbalance arises and how it may differ across autism symptomatology and brain regions is not well understood. Methods: We used innovative analysis methods - combining competitive gene-set analysis and gene-expression profiles in relation to cortical thickness (CT)- to investigate the relationship between genetic variance, brain structure and autism symptomatology of participants from the EU-AIMS LEAP cohort (autism=360, male/female=259/101; neurotypical control participants=279, male/female=178/101) aged 6 to 30 years. Competitive gene-set analysis investigated associations between glutamatergic and GABAergic signaling pathway gene-sets and clinical measures, and CT. Additionally, we investigated expression profiles of the genes within those sets throughout the brain and how those profiles relate to differences in CT between autistic and neurotypical control participants in the same regions. Results: The glutamate gene-set was associated with all autism symptom severity scores on the Autism Diagnostic Observation Schedule-2 (ADOS-2) and the Autism Diagnostic Interview-Revised (ADI-R) within the autistic group, while the GABA set was associated with sensory processing measures (using the SSP subscales) across all participants. Brain regions with greater gene expression of both glutamate and GABA genes showed greater differences in CT between autistic and neurotypical control participants. Conclusions: Our results suggest crucial roles for glutamate and GABA genes in autism symptomatology as well as CT, where GABA is more strongly associated with sensory processing and glutamate more with autism symptom severity. 

COMPARATIVE ANALYSIS OF SHRINKAGE COVARIANCE MATRIX USING MICROARRAYS DATA

2016 ◽  
Vol 78 (4-4) ◽  
Author(s):  
Suryaefiza Karjanto ◽  
Norazan Mohamed Ramli ◽  
Nor Azura Md Ghani
Keyword(s):  
Covariance Matrix ◽  
Activity Level ◽  
Test Statistic ◽  
Multivariate Test ◽  
Hotelling's T2 ◽  
Gene Sets ◽  
Sample Covariance ◽  
Hotelling’S T2 ◽  
Hotelling’S T2 Statistic ◽  
Multivariate Test Statistic

The DNA microarray technologies permit scientists to depict the expression of genes for related samples.  This relationship between genes is analysed using Hotelling’s T2 as a multivariate test statistic but the disadvantage of this test, when used in microarray studies is the number of samples is larger than the number of variables.  This study discovers the potential of the shrinkage approach to estimate the covariance matrix specifically when the high dimensionality problem happened.  Consequently, the sample covariance matrix in Hotelling’s T2 statistic is not positive definite and become singular thus cannot be inverted.  In this research, the Hotelling’s T2 statistic is combined with a shrinkage approach as an alternative estimation to estimate the covariance matrix to detect significant gene sets.  The multivariate test statistic of classical Hotelling's T2 is used to integrate the correlation when assessing changes in activity level across biological conditions.  The performances of the proposed methods were assessed using real data study.  Shrinkage covariance matrix approach indicates a better result for detection of differentially expressed gene sets as compared to other methods.

scholarly journals Distance-correlation based gene set analysis in longitudinal studies

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Jiehuan Sun ◽  
Jose D. Herazo-Maya ◽  
Xiu Huang ◽  
Naftali Kaminski ◽  
Hongyu Zhao
Keyword(s):  
Gene Expression ◽  
Disease Progression ◽  
Clinical Outcomes ◽  
Expression Profiles ◽  
Gene Set Analysis ◽  
Related Gene ◽  
Disease Etiology ◽  
Distance Correlation ◽  
Gene Set ◽  
Gene Sets

AbstractLongitudinal gene expression profiles of subjects are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene sets that have coordinated changes with relevant clinical outcomes over time from these data could provide significant insights into the molecular basis of disease progression and lead to better treatments. In this article, we propose a Distance-Correlation based Gene Set Analysis (dcGSA) method for longitudinal gene expression data. dcGSA is a non-parametric approach, statistically robust, and can capture both linear and nonlinear relationships between gene sets and clinical outcomes. In addition, dcGSA is able to identify related gene sets in cases where the effects of gene sets on clinical outcomes differ across subjects due to the subject heterogeneity, remove the confounding effects of some unobserved time-invariant covariates, and allow the assessment of associations between gene sets and multiple related outcomes simultaneously. Through extensive simulation studies, we demonstrate that dcGSA is more powerful of detecting relevant genes than other commonly used gene set analysis methods. When dcGSA is applied to a real dataset on systemic lupus erythematosus, we are able to identify more disease related gene sets than other methods.

scholarly journals TOXPANEL: A Gene-Set Analysis Tool to Assess Liver and Kidney Injuries

2021 ◽  
Vol 12 ◽  
Author(s):  
Patric Schyman ◽  
Zhen Xu ◽  
Valmik Desai ◽  
Anders Wallqvist
Keyword(s):  
Gene Expression ◽  
Kidney Injury ◽  
Fold Change ◽  
Gene Set Analysis ◽  
Analysis Tool ◽  
Gene Set ◽  
Physiological Range ◽  
Gene Sets ◽  
Liver And Kidney

Gene-set analysis is commonly used to identify trends in gene expression when cells, tissues, organs, or organisms are subjected to conditions that differ from those within the normal physiological range. However, tools for gene-set analysis to assess liver and kidney injury responses are less common. Furthermore, most websites for gene-set analysis lack the option for users to customize their gene-set database. Here, we present the ToxPanel website, which allows users to perform gene-set analysis to assess liver and kidney injuries using activation scores based on gene-expression fold-change values. The results are graphically presented to assess constituent injury phenotypes (histopathology), with interactive result tables that identify the main contributing genes to a given signal. In addition, ToxPanel offers the flexibility to analyze any set of custom genes based on gene fold-change values. ToxPanel is publically available online at https://toxpanel.bhsai.org. ToxPanel allows users to access our previously developed liver and kidney injury gene sets, which we have shown in previous work to yield robust results that correlate with the degree of injury. Users can also test and validate their customized gene sets using the ToxPanel website.

scholarly journals Disease and phenotype gene set analysis of disease-based gene expression in mouse and human

2010 ◽  
Vol 42A (2) ◽  
pp. 162-167 ◽  
Author(s):  
Supriyo De ◽  
Yongqing Zhang ◽  
John R. Garner ◽  
S. Alex Wang ◽  
Kevin G. Becker
Keyword(s):  
Gene Expression ◽  
Human Disease ◽  
Complex Disease ◽  
Population Based ◽  
Gene Set Analysis ◽  
Common Disease ◽  
Analysis Tool ◽  
Gene Set ◽  
Disease Phenotypes ◽  
Gene Sets

The genetic contributions to common disease and complex disease phenotypes are pleiotropic, multifactorial, and combinatorial. Gene set analysis is a computational approach used in the analysis of microarray data to rapidly query gene combinations and multifactorial processes. Here we use novel gene sets based on population-based human genetic associations in common human disease or experimental genetic mouse models to analyze disease-related microarray studies. We developed a web-based analysis tool that uses these novel disease- and phenotype-related gene sets to analyze microarray-based gene expression data. These gene sets show disease and phenotype specificity in a species-specific and cross-species fashion. In this way, we integrate population-based common human disease genetics, mouse genetically determined phenotypes, and disease or phenotype structured ontologies, with gene expression studies relevant to human disease. This may aid in the translation of large-scale high-throughput datasets into the context of clinically relevant disease phenotypes.

scholarly journals Statistical Approach of Gene Set Analysis with Quantitative Trait Loci for Crop Gene Expression Studies

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 945
Author(s):  
Samarendra Das ◽  
Shesh N. Rai
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Statistical Approach ◽  
Gene Set Analysis ◽  
Expression Data ◽  
Expression Study ◽  
Gene Set ◽  
Gene Sets ◽  
Expression Studies ◽  
Gene Expression Studies

Genome-wide expression study is a powerful genomic technology to quantify expression dynamics of genes in a genome. In gene expression study, gene set analysis has become the first choice to gain insights into the underlying biology of diseases or stresses in plants. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results from the primary downstream differential expression analysis. The gene set analysis approaches are well developed in microarrays and RNA-seq gene expression data analysis. These approaches mainly focus on analyzing the gene sets with gene ontology or pathway annotation data. However, in plant biology, such methods may not establish any formal relationship between the genotypes and the phenotypes, as most of the traits are quantitative and controlled by polygenes. The existing Quantitative Trait Loci (QTL)-based gene set analysis approaches only focus on the over-representation analysis of the selected genes while ignoring their associated gene scores. Therefore, we developed an innovative statistical approach, GSQSeq, to analyze the gene sets with trait enriched QTL data. This approach considers the associated differential expression scores of genes while analyzing the gene sets. The performance of the developed method was tested on five different crop gene expression datasets obtained from real crop gene expression studies. Our analytical results indicated that the trait-specific analysis of gene sets was more robust and successful through the proposed approach than existing techniques. Further, the developed method provides a valuable platform for integrating the gene expression data with QTL data.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

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