Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges

Abstract

Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

Differential Expression of a Panel of Ten CNTN1-Associated Genes during Prostate Cancer Progression and the Predictive Properties of the Panel towards Prostate Cancer Relapse

“…The enrichment analysis was performed using a recently-developed FGSEA platform (https://www.biorxiv.org/content/10.1101/060012v2.full), a system similar to the most popular GSEA system developed by the Broad Institute [45]. However, gene set enrichment analysis is understandably associated with numerous limitations, including the lack of gold standard dataset to capture the complex nature of gene expression, a single-gene analysis method (the most popular one), and others [62,63]. This study has additional limitations.…”

Transcriptomic Analysis of Changes in Gene Expression During Flowering Induction in Sugarcane Under Controlled Photoperiodic Conditions

“…The RNA sequencing technique (RNA-Seq) established prominence in high-throughput analysis of transcriptomes for literally every organism, allowing the measurement of transcript expression levels much more accurately when compared with other methods, e.g., Northern blot, ESTs, and microarrays ( Wang et al, 2009 ; Nonis et al, 2014 ; Das et al, 2020 ). The RNA-Seq technique can also detect small sequence variations, such as SNPs and regulatory elements, such as non-coding and lncRNAs ( Vicentini et al, 2012 ; Ilott and Ponting, 2013 ; Cardoso-Silva et al, 2014 ; Veneziano et al, 2016 ).…”

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“…It should be mentioned that some authors have classified hybrid methods under competitive or self-contained methods based on whether they use a sample permutation or a gene sampling for significance assessment (Das et al, 2020 ). In self-contained methods, the calculated gene set score f ( G i ) for a gene set G i is defined based on the expression values of genes in G i .…”