Conclusion and Summary
In this dissertation, how to improve algorithms for pathway analysis of transcriptome data were addressed. Furthermore, computational and deployment issues in development process with applied algorithms were also introduced.
In chapter 2, improved pathway clustering with additional network information was addressed. It was observed that including PPI to similarity provides additional biological insights. The resulting R package ‘GScluster’, provides useful functions and visualizations for further biological research.
In chapter 3, an enhanced algorithm for pathway analysis and resampling method were introduced. By incorporating additional network information, the resulting R package ‘netGO’ provided more relevant gene-sets compared to conventional tools. In particular, when a small number of genes were used,
‘netGO’ showed better performances. Also, an additional package to visualize graph and network structure, ‘shinyCyJS’ was developed and included in ‘netGO’.
In chapter 4, a novel strategy for analysis single-cell RNA sequencing data was presented. By using cell-level analysis, more detailed pathway analysis can be performed. Moreover, it highlighted new pathways related to various biological functions. The R package ‘CellEnrich’ provided additional utilities to investigate the pathways enriched in the cell-level and visualizations.
In chapter 5, common and specific issues related to the development of R packages were discussed.
There are various challenges in computational field, besides the design of algorithm. In addition, brief process and implemented result related with chapter 2 and 3 are described. Although these issues are not related directly with biology, development of analysis applications needs to be more studied from an engineering point of view.
The presented contents in this dissertation have academic uniqueness in both biological and computational perspectives, and are expected to be applied to further researches in pathway analysis and development of R / shiny.
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