R Shiny App for Comparing Quantified RNA Expression Datasets

Qingyuan Ma (Mentor: Dr. Matthew McCoy, Innovation Center for Biomedical Informatics, Department of Oncology and Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University)

August 28, 2018, 2:00pm, Room 1300, Harris Building

Differential expression (DE) is a common transcriptomic analysis method that compares the RNA expression between two experimental populations. DE analysis results in a list of genes with which are up or down regulated transcription relative to the other. Since transcription can be quantified through both array and sequencing based methods, and DE analysis can be applied in to a number of experimental frameworks, comparing the results of differential expression datasets can help generate hypothesis about systematic regulatory mechanisms. Using data derived from several different DE experiments investigating the role of HuR in the post transcriptional regulation of acute stress response in pancreatic cancer, an R Shiny app was built to compare the results of individual DE analysis to find genes with overlapping changes in mRNA expression. The app provides a non-bioinformatics researcher the ability to examine and compare results from historical array and sequence based datasets, and the overlap genes are displayed on heatmaps based on the quantified mRNA expression tables. Furthermore, the app also allows the users to map the results of individual DE analyses to KEGG pathways and user curated lists of genes. By enabling the exploration and comparison between different transcriptional quantification methods and experimental contexts, this analysis tool has the potential to identify previously unknown associations between the datasets and produce novel regulatory hypothesis to drive future experimental researchDifferential expression (DE) is a common transcriptomic analysis method that compares the RNA expression between two experimental populations. DE analysis results in a list of genes with which are up or down regulated transcription relative to the other. Since transcription can be quantified through both array and sequencing based methods, and DE analysis can be applied in to a number of experimental frameworks, comparing the results of differential expression datasets can help generate hypothesis about systematic regulatory mechanisms. Using data derived from several different DE experiments investigating the role of HuR in the post transcriptional regulation of acute stress response in pancreatic cancer, an R Shiny app was built to compare the results of individual DE analysis to find genes with overlapping changes in mRNA expression. The app provides a non-bioinformatics researcher the ability to examine and compare results from historical array and sequence based datasets, and the overlap genes are displayed on heatmaps based on the quantified mRNA expression tables. Furthermore, the app also allows the users to map the results of individual DE analyses to KEGG pathways and user curated lists of genes. By enabling the exploration and comparison between different transcriptional quantification methods and experimental contexts, this analysis tool has the potential to identify previously unknown associations between the datasets and produce novel regulatory hypothesis to drive future experimental research