Gene Expression Data Analysis
Pathway Studio enables gene expression data analysis within the biological context of protein-protein interactions, pathways and pathway components.
- Are there key regulators that explain my gene expression modulation?
- Is the regulation acting coordinately on downstream targets?
- What key biological processes are regulated in my experiment?
- What proteins should I look at? What do they do?
- What kinases and phosphatases modify proteins in my gene list?
These and many other questions you can answer with Pathway Studio, starting with either raw gene expression data, or a list of significant genes.
Pathway Studio helps you to easily find common regulators for your most differentially expressed genes.
With Pathway Studio you can:
- Find pathways and gene ontology groups affected in an experiment
- Overlay expression data on canonical pathways and visualize the effects
- Identify significant genes from a network relevance prospective
- Build new pathways/regulation networks using molecular and functional relationship information extracted from publicly available literature
Pathway Studio has convenient data import tools and algorithms that allow you to identify pathways and biological processes affected in the experiment. With Pathway Studio you can build pathways and regulation networks, and identify mechanisms responsible for your observed phenotypic change.
DATA IMPORTER
- Pathway Studio can import data from Affymetrix, Illumina, and other major gene expression platforms, as well as text and Excel, GEO, .cdf files.
- Sample Correlation Viewer helps to group samples and identify potential outliers at the stage of data import.
RESULTS AND DISPLAYS
- Lists of affected pathways or significant genes, with numerical measure of confidence and score
- Ready-for-publication pathway diagrams; automatic layouts help to arrange upstream and downstream events, or display entities according to their cellular localization
- Multiple Expression viewer displays multiple channels of experimental data on a network diagram
DATA ANALYSIS ALGORITHMS
- Gene Set Enrichment Analysis (GSEA) finds pathways affected in the excrement
- Subnetwork Enrichment Analysis helps to identify expression targets, binding partners, disease regulators, etc. to create pathways for the most differentially expressed genes using data from the ResNet database
- Fisher Exact Test helps to find the most relevant groups affected in the experiment
- Build Dense Expression Networks searches the ResNet database for dense expressed clusters (only available in Pathway Studio Enterprise)
