Loading...

Identification of Driver Genes in Glioblastoma Based on Single-Cell Gene Expression Data Utilizing the Concept of Pseudotime and Phylogenetic Analysis

Mirza Abolhassani, Fatemeh | 2023

86 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56774 (02)
  4. University: Sharif University of Technology
  5. Department: Mathematical Sciences
  6. Advisor(s): Foroughmand Aarabi, Mohammad Hadi; Kavousi, Kaveh; Zare Mirakabad, Fatemeh
  7. Abstract:
  8. Genetic heterogeneity within a tumor, which occurs during cancer evolution, is one of the reasons for treatment failure and increased chances of drug resistance. Cancer cells initially derive from a mutated progenitor cell, resulting in shared mutated genes. Throughout the course of tumor formation and progression, the occurrence of new mutations is possible, leading to the generation of cancer cells with various mutated genes. An appropriate approach is to identify the sequence of mutations that have occurred in the tumor, which can be inferred from single-cell sequencing data. Singlecell data provides valuable information about branching events in the evolution of a cancerous tumor. In this study, we utilize single-cell data to infer the evolutionary tree of cancer. Consequently, cancer cells exhibit a diverse genetic variety. Therefore, we determine the pathway of cellular differentiation based on gene expression and order the cells using a pseudo-time metric. We then investigate whether the order of mutation events can also indicate the pathway of cellular differentiation. Additionally, we aim to identify genes that serve as representatives of tumor expansion
  9. Keywords:
  10. Genetic Mutation ; Gene Expression Data ; Glioblastoma Brain Tumor ; Pseudotime ; Phylogentic Trees ; Monocle Algorithm