Gene Expression Analysis

Pathway Studio enables gene expression data analysis within the biological context of protein-protein interactions, pathways and pathway components.

Pathway Studio supports two approaches to analyze gene expression data. In the standard approach, currently supported in all Pathway Studio editions, it is intended that microarray data analysis be perfomed on the data set and then imported into Pathway Studio to identify relationships among differentially expressed genes.

Pathway Studio connects the identifiers in your experimental data to their respective protein counterparts contained in the Pathway Studio ResNet Database. All identifiers are recognized using Map Files. In addition, Pathway Studio contains a comprehensive list of aliases for each protein name, ensuring that all information on your proteins is included in the analysis. In addition, an automatic GO group analysis is performed to help identify biological processes.

Your gene expression data can either be overlayed on to exisiting pathways or Pathway Studio will construct de novo an interaction network(s), identifying common points of regulation.

The second approach offered in Pathway Studio uses Gene Set Enrichment Analysis (GSEA) and other algorithms to analyze all the probe data contained in your microarray experiment to look more closely at sometimes subtle changes in the biological response. In GSEA, ratios for each transcript presence are determined as in MDA. The ratios are then ordered and genes grouped together based on biological function into gene sets. In ordering the ratios they are overlayed on to their respective proteins in the gene sets and displayed based on their direct and indirect known protein-protein interactions drawn from the database. Up- and down-status changes are rendered in separate colors within each gene set. Using the ResNet database, gene sets can be constructed using, for example, transcription factors, pathways of interest, signaling networks and even cell processes.