Uncovering biomarkers and molecular heterogeneity of complex diseases: Utilizing the power of Data Science

Uncovering causal drivers of complex diseases is yet a difficult challenge. Unlike single-gene disorders complex diseases are heterogeneous and are caused by a combination of genetic, environmental, and lifestyle factors which complicates the identification of patient subgroups and the disease causal drivers. In order to study the dimensions of complex diseases analyzing different omics data is a necessity. The main goal of this thesis is to provide computational approaches for analyzing omics data of two complex diseases; mainly, Acute Myeloid Leukaemia (AML) and Systemic Lupus Erythematosus (SLE). Additionally, we aim at providing a method that would deal with integration issues that usually arise when combining complex diseases omics (specifically metabolomics) data from multiple data sources. AML is a cancer of the myeloid blood cells that is known for its heterogeneity. Patients usually respond to treatment and achieve a complete remission state. However, a majority of patients relapse or develop treatment resistance. In paper I, we focus on investigating recurrent genomic alterations in adult and pediatric relapsed and primary resistant AML that may explain disease progression. In paper II, we characterize changes in the transcriptome of AML over the course of the disease, incorporating machine learning analysis. SLE is a heterogeneous autoimmune disease characterized by unpredictable periods of flares. The flares are presented as different SLE disease activities (DA). Studies on the combinatorial effects of genes towards the manifestation of SLE DAs in patients’ subgroups have been limited. In paper III, we analyze gene expression data of pediatric SLE using interpretable machine learning. The aim was to study the co-predictive transcriptomic factors driving disease progression, discover the disease subtypes, and explore the relationship between transcriptomics factors and the phenotypes associated with the discovered subtypes. Recently, Metabolomics has been a crucial dimension in major multi-omics complex disease studies. Small-compound databases contain a large amount of information for metabolites. However, the existing redundancy of information in the databases leads to major standardization issues. In paper IV, we aim at resolving the inconsistencies that exist when linking and combining metabolomics data from several databases by introducing the new R package MetaFetcheR.