Section2_intro.md

Section 2: Network Analysis

Part 1: Over-representation and Enrichment Analysis

Over Representation Analysis (ORA)

Over-representation analysis (ORA) is a method used to identify which predefined gene sets are disproportionately represented in a given set of genes compared to what would be expected by random chance (Huang et al., 2009). We recommend using Over-representation analysis (ORA) only when Gene Set Enrichment Analysis (GSEA) is not suitable. Although we are using the gseapy library for ORA in this the tutorial, it's important to note that ORA and GSEA are distinct methods.

ORA on gene expression network clusters

We use community detection algorithm to identify communities in the network. The greedy_modularity_communities function in NetworkX implements a community detection algorithm that optimises modularity using a greedy approach. It iteratively merges pairs of nodes or communities that result in the largest increase in modularity until no further improvement is possible. Modularity measures the density of links inside communities compared to links between communities, aiming to maximise this value to identify densely connected groups within the network.

Performing ORA on individual clusters can help in understanding distinct biological significance of each cluster, revealing how certain pathways or functions are associated with specific subsets of genes and potentially uncovering biological themes that may be missed.

Gene Set Enrichment Analysis (GSEA)

Gene Set Enrichment Analysis (GSEA) is a genome-wide expression analysis method designed to interpret expression profiles focusing on pre-defined gene sets (Subramanian et al., 2005). These gene sets are curated based on prior biological knowledge, such as published information about biochemical pathways or patterns of coexpression observed in previous experimental studies. The genes can be ordered in a ranked list, according to their differential expression between the classes. The primary objective of GSEA is to assess whether the genes within a given gene set tend to occur toward the top (or bottom) of the ranked list. This ranking is based on the correlation between gene expression and a particular phenotypic class distinction. By evaluating the distribution of gene set members within the ranked list, GSEA identifies whether the set is correlated with the phenotypic class, thus providing insights into underlying biological mechanisms. This method contrasts with traditional single-gene analysis by focusing on the collective behavior of gene sets, thereby uncovering biologically significant patterns that might be overlooked when examining individual genes in isolation. We use gseapy to perform GSEA with the gene set KEGG_2021_Human.

Visualising GSEA Results

Once you have performed GSEA, the next step is to visualise the results. Visualisation helps in interpreting the biological roles of the enriched gene sets. Here, we visualise GSEA results with Barcode Enrichment Plot, Heatmap, Clustermap, and Dot Plot.

• Barcode Enrichment Plot

  • Barcode Enrichment Plot shows the positions of members of a given gene set in a ranked list of enrichment scores for the top enriched terms. The scores are ranked left to right from smallest to largest. The ranked scores are represented by a shaded bar, forming a pattern like a barcode.

• Heatmap Visualisation

  • gseapy provides a heatmap function to visualise the expression levels of the leading-edge genes. The heatmap provides a visual representation of how these genes are expressed across different samples in relation to their assigned phenotypic classes.

• Clustermap Visualisation

  • The function clustermap from seaborn is used to create a clustered heatmap. It not only shows the expression levels of the leading-edge genes but also clusters them based on similarity, providing additional insights into gene expression patterns. The cluster map includes dendrograms, which show the hierarchical clustering of both genes and samples, helping to identify groups of co-expressed genes and similar samples.

• Dot Plot Visualisation

  • Use the dotplot function in gseapy to create a visual representation of the GSEA results. Here we use "FDR q-val" to determine the dot sizes, which represents the false discovery rate adjusted p-values. We display normalised enrichment score (NES) value as the x-axis.

GSEA on Clusters

Similarly to ORA, GSEA can also be performed on individual communities after clustering. This allows for a more granular analysis, revealing pathways and functions that are enriched within particular subgroups of the data.

Part 2: Similarity Network Fusion

In Part 1, we worked with a gene expression network, where each node represents a gene. In Part 2, our focus shifts to Patient Networks. In patient networks, each node represents an individual patient. Unlike gene networks that represent molecular interactions, patient networks encode similarities and differences between patients, based on various modalities of data such as gene expression profiles and DNA methylation patterns.

Similarity Network Fusion (SNF)

When working with patient data, we often want to combine information from multiple modalities. Here, we use Similarity Network Fusion (SNF) to integrate patient data in the space of samples (e.g., patients) rather than measurements (e.g., gene expression) (Wang et al.).

SNF consists of two main steps:

1. Construction of Patient-Similarity Networks (Section 1):

   • For each type of data (e.g., gene expression, DNA methylation), SNF constructs a patient-by-patient similarity matrix. This matrix represents a similarity network where nodes are samples (patients) and weighted edges represent the pairwise similarities between them.

2. Integration of Networks:

   • Using a nonlinear combination method based on message-passing theory, SNF iteratively updates each network with information from the others. This process aligns each network more closely to the other with every iteration. The iterative update continues until convergence, resulting in a single integrated similarity network.