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Disease modules identification in heterogenous diseases with WGCNA method
Högskolan i Skövde, Institutionen för biovetenskap. (Bioinformatics)
2019 (engelsk)Independent thesis Advanced level (degree of Master (One Year)), 20 poäng / 30 hpOppgave
Abstract [en]

The widely collected and analyzed genetic data help in understanding the underlying mechanisms of heterogeneous diseases. Cellular components interact in a network fashion where genes are nodes and edges are the interactions. The failure in individual genes lead to dys-regulation of sub-groups of genes which causes a disease phenotype, and this dys-functional region is called a disease module. Disease module identification in complex diseases such as asthma and cancer is a huge challenge. Despite the development of numerous sophisticated methods there is a still no gold standard. In this study we apply different parameter settings to test the performance of a widely used method for disease module detection in multi-omics data called Weighted Gene Co-expression Network Analysis (WGCNA). A systematic approach is used to identify disease modules in asthma and arthritis diseases. The accuracy of obtained modules is validated by a pathway scoring algorithm (PASCAL) and GWAS SNP enrichment. Our results differ between the tested data sets and therefore we cannot conclude with recommendations for an optimal setting that could perform best for multiple data sets using this method.

sted, utgiver, år, opplag, sider
2019. , s. 52
Emneord [en]
disease module, WGCNA, parameter settings
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-16692OAI: oai:DiVA.org:his-16692DiVA, id: diva2:1295665
Fag / kurs
Bioinformatics
Utdanningsprogram
Bioinformatics - Master’s Programme
Veileder
Examiner
Tilgjengelig fra: 2019-03-26 Laget: 2019-03-12 Sist oppdatert: 2019-03-26bibliografisk kontrollert

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