WGCNA Analysis Service

What is WGCNA?

Weighted Gene Co-expression Network Analysis (WGCNA) is a systems biology approach used to describe gene association patterns across different samples. This method identifies gene modules that exhibit coordinated expression patterns and examines their relationships with external phenotypes, aiding in the discovery of potential biomarkers and therapeutic targets.

Unlike traditional methods that focus on differentially expressed genes, WGCNA leverages a comprehensive range of gene expression data, including thousands of highly variable genes, to identify relevant gene modules. This approach simplifies the analysis by grouping genes into modules and analyzing their associations with phenotypes, thus addressing challenges related to multiple hypothesis testing.

WGCNA constructs weighted gene co-expression networks where nodes represent genes and edges reflect gene expression correlations, with weights adjusted by a power function to enhance biological relevance. The method includes calculating module connectivity, identifying hub genes, and using Topological Overlap Matrix (TOM) to reduce noise and improve the accuracy of gene association measurements. This network-based approach is particularly suited for complex data patterns and large datasets, offering a more holistic view of gene interactions and their biological significance.

Application of WGCNA Analysis

• Identification of Biomarkers: WGCNA helps in identifying gene modules that are associated with specific diseases or conditions, facilitating the discovery of potential biomarkers for diagnosis and prognosis.
• Understanding Disease Mechanisms: By uncovering gene modules that are co-expressed in disease states, WGCNA aids in understanding the underlying molecular mechanisms of complex diseases, such as cancer, neurodegenerative disorders, and cardiovascular diseases.
• Gene-Phenotype Relationship Analysis: WGCNA enables the investigation of connections between gene modules and clinical phenotypes, facilitating the identification of genes and pathways associated with specific traits or disease outcomes.
• Drug Target Identification: By uncovering critical regulatory genes and pathways, WGCNA supports the discovery of new drug targets and the development of potential therapeutic approaches.
• Gene Function Prediction: Through the analysis of gene modules and their interactions, WGCNA can predict the functions of uncharacterized genes based on their co-expression with well-known genes.
• Comparative Genomics: WGCNA can be employed to compare gene expression networks across different species or conditions, providing insights into evolutionary changes and functional similarities.
• Systems Biology Research: It is widely used in systems biology to understand the global architecture of gene networks, facilitating integrative studies of gene expression and regulation across various biological systems.
• Personalized Medicine: WGCNA supports the development of personalized treatment approaches by linking gene expression profiles to individual patient characteristics, enabling more tailored therapeutic interventions.

CD Genomics WGCNA Data Analysis Workflow

Sample Submission Guidelines

Bioinformatics Analysis Content

What Are the Advantages of Our Services?

Comprehensive Gene Co-expression Network Analysis

Our WGCNA services provide a thorough analysis of gene co-expression networks, enabling the identification of functionally related gene modules and their associations with clinical traits. We leverage the power of the WGCNA framework, known for its robustness in detecting and analyzing gene modules, to deliver insights that are critical for understanding complex biological processes.

Highly Specialized Analytical Pipelines

Our analytical pipelines are specifically designed to handle the complexities of WGCNA. These pipelines are finely tuned to efficiently process large-scale gene expression data, enabling precise module detection, eigengene computation, and correlation analysis. This specialization ensures that our analyses are both accurate and directly aligned with the specific research objectives of our clients.

Integration with Multi-Omics Data

We enhance the WGCNA framework by integrating it with multi-omics datasets, including proteomics, metabolomics, and epigenomics. This multi-layered approach facilitates a more comprehensive understanding of biological systems, enabling the identification of key regulatory networks and biomarkers that might be missed with a single-omics analysis.

Advanced Statistical Techniques

Our WGCNA services are underpinned by advanced statistical methodologies, including permutation testing, bootstrapping, and hierarchical clustering, to ensure the robustness of network construction and module detection. These techniques are crucial for identifying gene networks that are both biologically meaningful and statistically significant, providing deeper insights into gene regulation and expression patterns.

Customizable and Scalable Solutions

Our WGCNA services are designed to be highly customizable and scalable, catering to projects of any size or complexity. Whether you're working with small datasets or large-scale, multi-center studies, our solutions adapt to your specific research needs. This ensures that each project benefits from a bespoke analysis, fully leveraging the potential of your data.

Data Security and Compliance

Recognizing the importance of data security in genomic research, we adhere to stringent data protection protocols. Our infrastructure ensures that all client data is securely stored and processed in compliance with the highest standards of confidentiality and integrity. This commitment to data security not only safeguards sensitive information but also builds trust with our clients, ensuring a secure and reliable partnership throughout the research process.

What Does WGCNA Analysis Show?

Data Matrices for WGCNA Analysis

The expression matrix serves as the foundational input for WGCNA, representing normalized gene expression levels across different samples. Depending on the source, this data might be derived from microarray platforms or RNA-Seq experiments. For RNA-Seq data, expression levels are typically normalized to RPKM or TPM values to account for differences in sequencing depth and gene length. This matrix forms the basis for constructing gene co-expression networks.

Table 1. Example of Gene Expression Matrix

The phenotype matrix includes the traits or experimental conditions associated with each sample. This matrix is essential for identifying relationships between gene modules and external traits. Numeric traits can be used directly in the analysis, while categorical traits need to be converted into numeric form, such as binary encoding, to facilitate correlation analysis.

Table 2: Example of Phenotype Matrix

Module-Trait Relationships

Gene modules were assessed for their correlation with external traits, such as clinical phenotypes or experimental conditions. This analysis aimed to identify which gene modules are most significantly associated with the traits of interest.

Figure 1: Heatmap of Module-Trait Relationships.

Correlation Between Gene Significance and Module Membership

An analysis was conducted to assess the correlation between module membership (MM) and gene significance (GS) for each gene within the module. A robust correlation was found, especially within the turquoise module. This suggests that genes with high significance related to cancer status are also pivotal within the turquoise module, underscoring their importance in cancer research.

Figure 2: Scatter plot showing the correlation between MM and GS. (Li, 2018)

Visualizing Key Module Networks in WGCNA

Network analysis of a key module revealed that miR-let-7c functions as a central hub miRNA, connecting with numerous other miRNAs. This centrality underscores miR-let-7c's pivotal role within the module and its significant impact on various biological processes.

Figure 3. Network visualization of the key module, highlighting miR-let-7c as the central hub and its connections to other miRNAs within the module. (Li, 2018)

Title: Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process

Publication: Scientific Reports

Main Methods: WGCNA, meta-analysis，pathway enrichment

Abstract: This study investigates the genetic mechanisms of lactation across various animal species using meta-analysis, WGCNA, and pathway enrichment techniques. Differential expression analysis identified 104, 85, and 26 genes specific to before peak (BP) vs. peak (P), BP vs. after peak (AP), and P vs. AP stages, respectively. GO and KEGG pathway analyses highlighted significant enrichment in ubiquitin-dependent ERAD and chaperone cofactor-dependent protein refolding. WGCNA revealed five functional modules associated with lactation, with GJA1, AP2A2, and NPAS3 emerging as key hub genes. These findings provide new insights into lactation regulation and suggest several candidate genes for future animal breeding programs. The integration of meta-analysis with WGCNA enhances the ability to predict important functional genes, offering valuable biomarkers for further research.

Research Results:

Co-expressed Modules Associated with the Lactation Process

A comprehensive analysis identified 13,591 meta-genes from datasets spanning three species: Bos taurus, Ovis aries, and Bubalus bubalis. To explore the relationships among these genes, a WGCNA was conducted. The analysis grouped these meta-genes into 17 distinct modules, each containing between 30 to 5,815 genes, using the dynamic tree cutting algorithm. The hierarchical clustering of meta-genes across the three species and different lactation periods, visualized through the TOM. This matrix highlights the co-expression network, where lighter colors indicate lower gene overlap and darker shades represent higher overlap.

Figure 4. WGCNA. (A) Hierarchical cluster tree of 13,591 meta-genes across three species, with branches and color bands indicating assigned modules. (B) Co-expression network modules represented in the TOM, where light colors denote low overlap and darker reds indicate higher overlap between genes. (Farhadian, 2021)

Correlation of Functional Modules with Lactation Periods

Seventeen functional modules were evaluated to determine their associations with different lactation periods. Notable correlations were observed with the BP, P, and AP periods. Specifically, the midnight-blue module exhibited a positive correlation with the BP period, whereas the green and tan modules were negatively correlated with the P period. Additionally, the green-yellow and turquoise modules showed negative correlations with the AP period.

Figure 5. Module-trait relationships indicating the correlations between identified modules (y-axis) and lactation periods (x-axis): BP = before peak; P = Peak; AP = after peak. (Farhadian, 2021)

Gene Network Visualization of Meta-Genes in Lactation Stages

Betweenness Centrality (BC) values, ranging from 0 to 1, were used to assess the centrality of nodes, with a significance threshold set at BC ≥ 0.1. For the BP vs. P comparison, key hub genes identified included HSPA13, YWHAZ, PDIA3, TM9SF3, and CUL3. In the BP vs. AP network, RNASEL, MAPK4, SPI1, MYBL2, and MYBL1 emerged as prominent hub genes. For the P vs. AP comparison, the primary hub genes were HSPA8, ND5, ABCA2, GATA1, and CYTB. Network diagrams used a color gradient from bright green to dark red to represent varying levels of centrality, with yellow indicating intermediate values.

Figure 6. Gene network visualizations for different lactation stages: (A) BP vs. P, (B) BP vs. AP, and (C) P vs. AP. Node colors vary from green (highest centrality) to red (lowest centrality), with yellow indicating intermediate values. (Farhadian, 2021)

Decision Tree Classification of Lactation Stages

A decision tree model using the information gain criterion achieved an accuracy of 79%. This model highlights the importance of specific genes in distinguishing lactation stages based on meta-gene expression levels. The GJA1 gene, situated at the root of the decision tree, emerged as a key biomarker for lactation. Samples with GJA1 expression above 8.687 and AP2A2 expression above 10.144 were classified into the AP stage. Samples with GJA1 ≤ 8.687 and FBXW9 > 6.483 were categorized into the P stage; otherwise, they were assigned to the AP stage. The model underscored the relevance of genes such as GJA1, AP2A2, FBXW9, NPAS3, INTS1, CDKN2C, HOXC9, and SFI1, which are linked to various modules, including turquoise, tan, and green, emphasizing their pivotal roles in the lactation process.

Figure 7. Decision tree model using the information gain criterion for classifying lactation stages (BP, P, and AP) based on hub genes. (Farhadian, 2021)

Conclusion

The analysis of lactation-related meta-genes across species reveals distinct gene modules linked to different lactation stages. The decision tree model demonstrates that genes such as GJA1 and AP2A2 are critical for accurately classifying these stages, underscoring the potential for targeted interventions in lactation processes.

1. What is WGCNA and how can it benefit my research?

WGCNA is a powerful bioinformatics tool used to identify clusters (modules) of highly correlated genes. It helps researchers uncover key regulatory genes, understand gene interactions, and discover biomarkers. Utilizing WGCNA in your research can enhance your ability to identify potential therapeutic targets and improve your understanding of complex biological processes.

2. How is WGCNA different from other gene co-expression network analyses?

WGCNA stands out due to its ability to identify gene modules based on correlation patterns rather than just expression levels. Unlike other methods, it assigns a 'weight' to connections between genes, allowing for a more nuanced understanding of gene relationships and making it especially useful for identifying key drivers in complex networks.

3. What types of data are required for WGCNA analysis?

WGCNA typically requires high-throughput gene expression data, such as RNA-seq or microarray data. The data should be normalized and processed to ensure accurate module detection and gene network construction. Our service includes pre-processing steps to prepare your data for optimal WGCNA results.

4. Can WGCNA be used for both human and non-human species?

Yes, WGCNA is versatile and can be applied to any species with available gene expression data. Whether you're studying human diseases, plant biology, or animal genetics, WGCNA can be tailored to your specific research needs, helping you discover meaningful insights across different organisms.

5. How does WGCNA identify key driver genes in a network?

WGCNA identifies key driver genes by detecting hub genes within modules—those genes that are highly connected within a network. Hub genes are often critical regulators of biological processes, and their identification can lead to a better understanding of disease mechanisms or other biological phenomena.

6. What are the common applications of WGCNA in biomedical research?

WGCNA is widely used in cancer research, neurodegenerative diseases, cardiovascular studies, and more. It helps in identifying disease-associated gene modules, uncovering potential biomarkers, and understanding the molecular mechanisms underlying complex traits and diseases.

7. How long does it take to complete a WGCNA analysis?

The timeline for WGCNA analysis depends on the complexity of the data and the specific goals of the project. Typically, a basic analysis can take a few days to a week. Our service is designed to provide fast, reliable results with continuous support throughout the process.

References

1. Li, J.; et al. Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design. Scientific Reports. 2018, 8(1), 622.
2. Farhadian, M.; et al. Weighted Gene Co-expression Network Analysis Identifies Modules and Functionally Enriched Pathways in the Lactation Process. Scientific Reports. 2021, 11(1), 2367.