Pathway enrichment analysis is an approach extensively used when analyzing high throughput data to identify pathways enriched within a group of differentially expressed genes. Furthermore, different methods utilizing the topology of the pathway offer a unique way of analyzing and interpreting gene expression data. These methods usually offer pathway topologies with a limited number of methods and visualization of results. Also, the use of different methods individually and comparison of their results can be very cumbersome, time-consuming and prone to errors due to the need for repeated data conversion and transfer. Packages that offer a common interface to multiple methods are therefore necessary, to provide a uniform way of calling these methods or specifying parameters, and making simultaneous application of the methods easier. In this study topology-based pathway enrichment analysis was performed by using the R packages EnrichmentBrowser and ToPASeq on a time series RNA-Seq data for cardiac hypertrophy in order to compare their usability. Additionally, different topology-based enrichment analysis methods included in the packages were compared with a non-topology-based pathway enrichment analysis method as well as the combination of their results in order to assess biological insights. Regarding usability, the available instructions for how to use both EnrichmentBrowser and ToPASeq were easy to understand and apply in the R workspace. Furthermore, both packages were easy to install and adjust to various parameters. However, ToPASeq returned errors when some parameters other than the default ones were used. Also, one of the differences between the tools was the flexibility of options for visualization and interpretation of the results, where EnrichmentBrowser had clear advantages. Regarding biological insights, the methods SPIA and DEGraph produced significant pathways linked to the phenotype cardiac hypertrophy, with a clear advantage for SPIA that performed well in both tested data setups. Finally, combining results from both SPIA and GSEA (non-topology-based pathway enrichment analysis method) improved individual ranking by increasing confidence in specific target pathways and eliminating irrelevant pathways.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:his-19465 |
| Date | January 2020 |
| Creators | Mbah-Mbole, Georgia Fru |
| Publisher | Högskolan i Skövde, Institutionen för biovetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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