Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3814
Title: Determining Differentially Expressed Genes in Dengue Patients During Disease Progression
Authors: Coorey, H.
Jayatillake, R.
Jayathilaka, N.
Ambanpola, N.
Keywords: Dengue
Friedman test
Gene expression studies
Longitudinal data
Non-normality
Issue Date: 15-May-2024
Publisher: Faculty of Humanities and Sciences, SLIIT
Series/Report no.: SLIIT Journal of Humanities & Sciences (SJHS);12-22p. Volume 4 Issue (1) 2023
Abstract: Gene expression studies on gene transcription to synthesize functional gene products have been used extensively to understand biological differences between different disease conditions. Thus, this study determines differentially expressed genes in dengue infection during disease progression following the three phases: Febrile, Defervescence and Convalescent. Integrative data analysis of two publicly available longitudinal datasets in Gene Expression Omnibus (GEO) database has been employed to accomplish the prime objective of exploring temporal gene expression patterns. The Friedman test was given more emphasis due to the non-normality distributions of data. Repeated measures analysis of variance (ANOVA) and linear mixed models were also implemented to examine the potential of detecting differentially expressed genes despite non-normality. The Friedman test revealed significant differences in gene expression levels across different phases in dengue disease over time. This led to a notably higher count of genes showing differential expression compared to the other two methods: Repeated measures ANOVA and linear mixed models. The pathway analysis approach consists of significant differentially expressed genes derived from the Friedman test. The results identified upregulated pathways with any significant change in the overall expression of genes within pathways over time for the Febrile and Defervescence phases considering the Convalescent phase as a baseline. Moreover, genes available in pathways were not identified by the two parametric tests for non-normal data implying that the parametric approaches resulted in the least significance for data with non-normal distributions.
URI: https://rda.sliit.lk/handle/123456789/3814
ISSN: 2815 - 0120
Appears in Collections:SLIIT Journal of Humanities & Sciences (SJHS), Volume 4, Issue 1 2023

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