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Identifier

000419724

Title

ExpaNET: a pathway level analysis tool through graph expansion using Markov Chains and random walks

Alternative Title

ExpaNET: εργαλείο ανάλυσης βιολογικών μονοπατιών με χρήση Markov Chains

Author

Φίκας, Νικόλαος Π.

Thesis advisor

Νικολάου, Χριστόφορος

Reviewer

Αϊβαλιώτης, Μιχαήλ
Παυλίδης, Παύλος

Abstract

Moving from protein deregulation-level statistical analysis to the ones that take into account the deregulation levels of functional protein groups and pathways, the statistical power of the results increases and a systemic approach towards understanding the biological question is offered. This approach, 20 years after its birth, resulted in the creation of a variety of statistical approaches like GSEA, PAGE, GAGE etc. These approaches belong to the gene-set analysis category, which use at their basis, the lists of biological processes and pathways offered by online databases like KEGG, MSigDB, Reactome, BioCyc, etc, and analyze data from micro-arrays, next generation sequencing and recently proteomics methods. One major drawback of all these approaches is that they do not take into account the interactions between proteins of different pathways because neither topological, nor dynamic information of the analyzed networks is fed into their algorithms. In order to surpass the above disadvantage, this work aimed to develop a new package in R, based on the work of Dupont et al. [18], where by modeling limited random walks in graph using Markov Chain properties a relevant sub-network extraction achieved. These extracted relevant sub-networks represent expanded forms of the known biological pathways that when compared between different conditions obtain a pathway-level deregulation score. In the current work, several gene-expression lymphoma data-sets were used for the validation and evaluation of the new tool. In addition, a small scale proteomic data-set from a currently running project in the lab in Plasmodium was analyzed by ExpaNET in order to evaluate its applicability in proteomic data.

Language

English

Subject

R Package

Αλυσίδες Markov

Πακέτο R

Issue date

2018-11-23

Collection

School/Department--School of Sciences and Engineering--Department of Biology--Post-graduate theses

Type of Work--Post-graduate theses

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