Gene Expression to Hub Genes: Biomarker Discovery Using R, STRING & Cytoscape
Analyze real gene-expression datasets, identify differentially expressed genes, construct protein–protein interaction networks, discover hub genes, and prioritize disease-associated biomarkers using R, STRING, Cytoscape, and pathway-enrichment tools
Skills you will gain:
About Program:
This three-day hands-on workshop introduces participants to an integrated workflow for gene-expression analysis, network biology, hub-gene discovery, pathway enrichment, and biomarker prioritization.
Participants will work with a real gene-expression dataset, identify differentially expressed genes, construct protein–protein interaction networks, detect hub genes and molecular modules, and interpret enriched biological pathways.
The workshop is designed for researchers, Ph.D. scholars, academicians, and life-science professionals who want to apply computational biology techniques to disease research, biomarker discovery, and therapeutic-target identification.
Aim: To provide practical skills for analyzing gene-expression datasets, constructing molecular networks, identifying hub genes, and integrating pathway evidence for biomarker and therapeutic-target prioritization.
Program Objectives:
- Understand the fundamental principles of computational systems biology and network biology.
- Retrieve and analyse publicly available gene-expression datasets from GEO.
- Perform gene-expression preprocessing, normalisation, and differential-expression analysis using R and Bioconductor.
- Identify significantly upregulated and downregulated genes.
- Generate and interpret volcano plots, heatmaps, and gene-expression visualisations.
- Construct protein–protein interaction networks using STRING and Cytoscape.
- Analyse network topology using degree, betweenness, closeness, and MCC centrality measures.
- Identify hub genes using cytoHubba and functional molecular modules using MCODE.
- Perform Gene Ontology, KEGG, and Reactome pathway-enrichment analysis.
- Interpret disease-associated pathways and biological functions of candidate genes.
- Integrate gene-expression, network-centrality, and pathway evidence for biomarker prioritisation.
What you will learn?
📅 Day 1: Gene-Expression Analysis & DEG Identification
Core Objective: Identify significant disease-associated genes from gene-expression data.
Topics Covered
- Introduction to computational systems biology
- Gene-expression datasets and GEO
- Dataset retrieval and sample understanding
- Data preprocessing and normalization
- Differential-expression analysis
- Fold change, p-values, and adjusted p-values
- Identification of upregulated and downregulated genes
- Volcano plots and heatmaps
- Preparing gene lists for network analysis
🛠️ Hands-on Session
Participants will retrieve a public GEO dataset, perform differential-expression analysis in R, filter significant genes, and generate a volcano plot and heatmap.
Hands-on Workflow:
GEO Dataset → Preprocessing → Differential Expression → DEG Filtering → Volcano Plot → Heatmap
Tools: R, RStudio, GEOquery, Bioconductor, limma/DESeq2, ggplot2
📅 Day 2: PPI Networks & Hub-Gene Discovery
Core Objective: Construct biological networks and identify important hub genes and molecular modules.
Topics Covered
- Introduction to protein–protein interaction networks
- Nodes, edges, connectivity, and network topology
- Constructing PPI networks using STRING
- Importing networks into Cytoscape
- Network-topology analysis
- Degree, betweenness, closeness, and MCC centrality
- Hub-gene identification using cytoHubba
- Molecular-module detection using MCODE
- Biological interpretation of hub genes and clusters
🛠️ Hands-on Session
Participants will use the significant genes identified on Day 1 to construct a STRING PPI network, analyze it in Cytoscape, identify top hub genes, and detect significant molecular modules.
Hands-on Workflow:
DEGs → STRING → Cytoscape → Network Analysis → cytoHubba → MCODE → Hub Genes
Tools: STRING, Cytoscape, NetworkAnalyzer, cytoHubba, MCODE
📅 Day 3: Pathway Analysis & Biomarker Prioritization
Core Objective: Integrate hub genes and pathway evidence to prioritize candidate biomarkers and therapeutic targets.
Topics Covered
- Introduction to functional-enrichment analysis
- Gene Ontology enrichment
- KEGG and Reactome pathway analysis
- Functional interpretation of hub genes
- Disease-associated pathway identification
- Gene- pathway interaction networks
- Biomarker prioritization
- Therapeutic-target identification
- Integrating differential-expression, centrality, and pathway evidence
- Publication-quality network visualization
🛠️ Hands-on Session
Participants will perform enrichment analysis on hub genes, identify important disease-associated pathways, construct a gene–pathway network, and develop a candidate biomarker shortlist.
Hands-on Workflow:
Hub Genes → GO/KEGG/Reactome → Pathway Interpretation → Gene–Pathway Network → Biomarker Prioritization
Tools: Enrichr, g, KEGG, Reactome, ClueGO, Cytoscape
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Ph.D. scholars and researchers
- Biotechnology and bioinformatics students
- Molecular biology and genomics researchers
- Biomedical and pharmaceutical researchers
- Faculty and academicians
- Wet-lab researchers interested in computational biology
- Researchers working on transcriptomics, disease mechanisms, biomarkers, or therapeutic targets
Career Supporting Skills
Program Outcomes