ExpaNET: εργαλείο ανάλυσης βιολογικών μονοπατιών με χρήση Markov Chains
ExpaNET: a pathway level analysis tool through graph expansion using Markov Chains and random walks
Φίκας, Νικόλαος Π.
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. , 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.