Gene set / pathway analysis from a systems biology preview (?)
Entering edit mode
3.8 years ago
arronar ▴ 260


On my way to read and run a pathway/gene set analysis on some microarray data, I realized that besides the fact that there are many different ways (statistical methods) to do the analysis, all of them (at least as far as I know) stays at statistic and as you already know returns a p-value or/and an enrichment score for each pathway/gene set.

Today, I was wondering if there is any approach ever implemented to do pathway analysis but to take into account also information from systems biology and not only on statistical inferences.

Such information could be the role of each gene inside the gene set, like inducer , inhibitor. Also it could be info about the topology of that specific gene set and maybe gene's inter-correlations.

Does anyone have in mind any research article on such an approach ? Any info or even idea is welcomed.

Thank you.

systems biology pathway analysis • 1.2k views
Entering edit mode
3.8 years ago
natasha.sernova ★ 3.9k

There ia a pahway review:

Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges

Purvesh Khatri , Marina Sirota,Atul J. Butte

There were some biostar-posts:

Tools For Pathway/Gene Set Analysis Of Gwas (Genome-Wide Association Study) Data

How Do You Deal With Biological Context During Pathway Analysis?

A: Comparison Of Pathways Between Microbial Genomes

There was this review:

Analysing biological pathways in genome-wide association studies

Kai Wang, Mingyao Li & Hakon Hakonarson


Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology Peter D. Karp, Suzanne M. Paley, Markus Krummenacker, Mario Latendresse, Joseph M. Dale, Thomas J. Lee, Pallavi Kaipa, Fred Gilham, Aaron Spaulding, Liviu Popescu, Tomer Altman, Ian Paulsen, Ingrid M. Keseler, Ron Caspi

Brief Bioinform. 2010 Jan; 11(1): 40–79. Published online 2009 Dec 2. doi: 10.1093/bib/bbp043

PeTTSy: a computational tool for perturbation analysis of complex systems biology models Mirela Domijan, Paul E. Brown, Boris V. Shulgin, David A. Rand

BMC Bioinformatics. 2016; 17: 124. Published online 2016 Mar 10. doi: 10.1186/s12859-016-0972-2


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