Instructions here

This protocol describes a network analysis workflow in Cytoscape for a set of differentially expressed genes. Points covered:

Background

Ovarian serous cystadenocarcinoma is a type of epithelial ovarian cancer which accounts for ~90% of all ovarian cancers. The data used in this protocol are from The Cancer Genome Atlas, in which multiple subtypes of serous cystadenocarcinoma were identified and characterized by mRNA expression.

We will focus on the differential gene expression between two subtypes: Mesenchymal and Immunoreactive.

For convenience, the data has already been analyzed and pre-filtered, using log fold change value and adjusted p-value.

Network Retrieval

Many public databases and multiple Cytoscape apps allow you to retrieve a network or pathway relevant to your data. For this workflow, we will use the STRING app. Some other options include:

Retrieve Networks from STRING

To identify a relevant network, we will use the STRING database in two different ways:

  1. Query STRING protein with the list of differentially expressed genes.

  2. Query STRING disease for a keyword; ovarian cancer.

The two examples are split into two separate workflows on the following slides.

Example 1: STRING Protein Query Up-regulated Genes

STRING Network Up-regulated genes

Data Integration

Visualization

STRING Enrichment

STRING Protein Query Down-regulated Genes

Data Integration

Visualization

When I set the column to logFC and the color mapping, it doesn’t show up on the network. All the nodes are gray.

STRING Enrichment

Example 2: STRING Disease Query

Data Integration

Visualization

I didn’t have the red-blue colors option…

Other Analysis Options

  • Exploring networks: finding paths, hubs and modules (clusterMaker, MCODE, jActiveModules, NetworkAnalyzer)
  • Extending networks with Transcription Factors, miRNAs, etc using CyTargetLinker

Exploring Networks