10/31/14
Fri, 10/31/2014 at 3:40 am
Customer: I have got a list of selected genes revealed by microarray experiments that are differentially expressed in our experimental conditions. I am interested in performing molecular pathways analysis to the “genes of interest”.
Fri, 10/31/2014 at 10:15 AM
AccuraScience LB: Based on my current understanding of the description of your objectives, there are two potential approaches for you to consider: (1) performing an analysis of enriched pathways (e.g., Gene ontology terms, KEGG pathways - there are a few other ways of defining pathways that could potentially be used) for the genes that are significantly up- and/or down-regulated in your microarray analysis, and either list genes in those pathways for you to identify "genes of interest" manually, or, if you could specify some criteria to prioritize or sort these genes, we will implement them to produce a sorted list automatically. (2) performing data integration analysis between the gene lists you have (which resulted from the differential expression analysis of microarrays) and data from other sources, for example, protein-protein interaction data, tissue-specific expression data, or even other public microarray or sequencing datasets - and build a network using these different data types, and identify "genes of interest" as the ones possessing specific topological features in the network - e.g., "hub genes" (those nodes with many edges in the network), "bottleneck genes" (those with higher betweenness centrality, thus are more destructive than others if they are compromised) etc.
The main difference between these two ways described here is, pathway analysis - described in (1) - takes advantage of existing knowledge of biological pathways, but network analysis - described in (2) - does not, rather, it attempts to identify important genes based on integration of multiple data types.
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Note: LB stands for Lead Bioinformatician. An AccuraScience LB is a senior bioinformatics expert and leader of an AccuraScience data analysis team.
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