Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges
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...
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MDPI AG
2020-04-01
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| Online Access: | https://www.mdpi.com/1099-4300/22/4/427 |
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| author | Samarendra Das Craig J. McClain Shesh N. Rai |
| author_facet | Samarendra Das Craig J. McClain Shesh N. Rai |
| author_sort | Samarendra Das |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:33:23Z |
| format | Article |
| id | doaj.art-d9ae74a811be4d41bfc7e8d1595eb177 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T20:33:23Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-d9ae74a811be4d41bfc7e8d1595eb1772023-11-19T21:14:22ZengMDPI AGEntropy1099-43002020-04-0122442710.3390/e22040427Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future ChallengesSamarendra Das0Craig J. McClain1Shesh N. Rai2Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, IndiaDepartment of Medicine, University of Louisville, Louisville, KY 40202, USASchool of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY 40292, USAOver 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.https://www.mdpi.com/1099-4300/22/4/427gene set analysismicroarraysRNA-sequencinggenome wide association studycompetitiveself-contained |
| spellingShingle | Samarendra Das Craig J. McClain Shesh N. Rai Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges Entropy gene set analysis microarrays RNA-sequencing genome wide association study competitive self-contained |
| title | Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges |
| title_full | Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges |
| title_fullStr | Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges |
| title_full_unstemmed | Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges |
| title_short | Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges |
| title_sort | fifteen years of gene set analysis for high throughput genomic data a review of statistical approaches and future challenges |
| topic | gene set analysis microarrays RNA-sequencing genome wide association study competitive self-contained |
| url | https://www.mdpi.com/1099-4300/22/4/427 |
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