Metascape Gene List Analysis Report

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Heatmap Summary

Figure 1. Heatmap of enriched terms across input gene lists, colored by p-values.
Metascape only visualizes the top 20 clusters. Up to 100 enriched clusters can be viewed here.

The heatmap can be interactively viewed using JTreeView2 (.cdt, .gtr and .atr files can be found in the Zip package).

Gene Lists

User-provided gene identifiers are first converted into their corresponding H. sapiens Entrez gene IDs using the latest version of the database (last updated on 2021-05-01). If multiple identifiers correspond to the same Entrez gene ID, they will be considered as a single Entrez gene ID in downstream analyses. Each gene list is assigned a unique color, which is used throughout the analysis. The gene lists are summarized in Table 1.

Table 1. Statistics of input gene lists.
Name Total Unique Color Code
LN_meta 2842 2828
N0 1435 1419
The overlaps between these lists are shown in a Circos3 plot (Figure 2.a). Another useful representation is to overlap genes based on their functions or shared pathways. The overlaps between gene lists can be significantly improved by considering overlaps between genes sharing the same enriched ontology term(s)(Figure %d.b). Only ontology terms that contain less than 100 genes are used to calculate functional overlaps to avoid linking genes using very general annotation. (We do not want to link all genes, only genes that belong to specific biological processes.)
Figure 2. Overlap between gene lists: (a) only at the gene level, where purple curves link identical genes; (b) including the shared term level, where blue curves link genes that belong to the same enriched ontology term. The inner circle represents gene lists, where hits are arranged along the arc. Genes that hit multiple lists are colored in dark orange, and genes unique to a list are shown in light orange. The publication-quality version of the figures is included in the Zip package as a .svg file under the Overlap_circos folder (readable by popular web browsers and Adobe Illustrator).

Pathway and Process Enrichment Analysis

For each given gene list, pathway and process enrichment analysis has been carried out with the following ontology sources: KEGG Functional Sets, KEGG Pathway and Reactome Gene Sets. All genes in the genome have been used as the enrichment background. Terms with a p-value < 0.01, a minimum count of 3, and an enrichment factor > 1.5 (the enrichment factor is the ratio between the observed counts and the counts expected by chance) are collected and grouped into clusters based on their membership similarities. More specifically, p-values are calculated based on the accumulative hypergeometric distribution4, and q-values are calculated using the Banjamini-Hochberg procedure to account for multiple testings5. Kappa scores6 are used as the similarity metric when performing hierachical clustering on the enriched terms, and sub-trees with a similarity of > 0.3 are considered a cluster. The most statistically significant term within a cluster is chosen to represent the cluster.

When multiple gene lists are provided, all lists are merged into one list called "_FINAL". A term may be found enriched in several individual gene lists and/or in the _FINAL gene list, and the best p-value among them is chosen as the final p-value. The pathway/process clusters that are found to be of interest (either shared or unique based on specific list enrichment) are used to prioritize the genes that fall into those clusters (membership is presented as 1/0 binary columns in the Excel spreadsheet). Note that individual gene lists containing more than 3000 genes are ignored during the enrichment analysis to avoid superficial terms; this is because long gene lists are often not random and generally trigger too many terms that are not of direct relevance to the biology under study.

Table 2. Top 20 clusters with their representative enriched terms (one per cluster). "Count" is the number of genes in the user-provided lists with membership in the given ontology term. "%" is the percentage of all of the user-provided genes that are found in the given ontology term (only input genes with at least one ontology term annotation are included in the calculation). "Log10(P)" is the p-value in log base 10. "Log10(q)" is the multi-test adjusted p-value in log base 10. __PATTERN__ shows the color code used for the gene lists where the term is found statistically significant, i.e., multiple colors indicate a pathway/process that is shared across multiple lists.
_PATTERN_ GO Category Description Count % Log10(P) Log10(q)
R-HSA-1280218 Reactome Gene Sets Adaptive Immune System 133 10.47 -41.45 -39.19
hsa04530 KEGG Pathway Tight junction 49 3.86 -25.46 -23.81
R-HSA-9675108 Reactome Gene Sets Nervous system development 165 12.99 -85.37 -81.85
R-HSA-1280215 Reactome Gene Sets Cytokine Signaling in Immune system 164 12.91 -69.20 -66.16
R-HSA-382551 Reactome Gene Sets Transport of small molecules 112 8.82 -29.72 -27.90
R-HSA-1474244 Reactome Gene Sets Extracellular matrix organization 82 6.46 -40.27 -38.06
hsa04660 KEGG Pathway T cell receptor signaling pathway 39 3.07 -25.36 -23.73
hsa04640 KEGG Pathway Hematopoietic cell lineage 36 2.83 -23.11 -21.60
hsa05418 KEGG Pathway Fluid shear stress and atherosclerosis 51 4.02 -30.66 -28.81
hsa04650 KEGG Pathway Natural killer cell mediated cytotoxicity 38 2.99 -19.73 -18.37
R-HSA-109582 Reactome Gene Sets Hemostasis 114 8.98 -37.66 -35.60
R-HSA-397014 Reactome Gene Sets Muscle contraction 60 4.72 -32.54 -30.63
hsa04060 KEGG Pathway Cytokine-cytokine receptor interaction 75 5.91 -37.49 -35.46
hsa05200 KEGG Pathway Pathways in cancer 105 8.27 -50.47 -47.65
R-HSA-390522 Reactome Gene Sets Striated Muscle Contraction 21 1.65 -18.87 -17.52
ko04658 KEGG Pathway Th1 and Th2 cell differentiation 38 2.99 -26.38 -24.67
hsa04810 KEGG Pathway Regulation of actin cytoskeleton 64 5.04 -34.42 -32.47
R-HSA-8957275 Reactome Gene Sets Post-translational protein phosphorylation 21 1.65 -7.87 -7.01
hsa04514 KEGG Pathway Cell adhesion molecules (CAMs) 46 3.62 -26.08 -24.38
hsa04145 KEGG Pathway Phagosome 49 3.86 -25.71 -24.05

To further capture the relationships between the terms, a subset of enriched terms have been selected and rendered as a network plot, where terms with a similarity > 0.3 are connected by edges. We select the terms with the best p-values from each of the 20 clusters, with the constraint that there are no more than 15 terms per cluster and no more than 250 terms in total. The network is visualized using Cytoscape7, where each node represents an enriched term and is colored first by its cluster ID (Figure 3.a) and then by its p-value (Figure 3.b). These networks can be interactively viewed in Cytoscape through the .cys files (contained in the Zip package, which also contains a publication-quality version as a PDF) or within a browser by clicking on the web icon. For clarity, term labels are only shown for one term per cluster, so it is recommended to use Cytoscape or a browser to visualize the network in order to inspect all node labels. We can also export the network into a PDF file within Cytoscape, and then edit the labels using Adobe Illustrator for publication purposes. To switch off all labels, delete the "Label" mapping under the "Style" tab within Cytoscape, and then export the network view.

Figure 3. Network of enriched terms: (a) colored by cluster ID, where nodes that share the same cluster ID are typically close to each other; (b) colored by p-value, where terms containing more genes tend to have a more significant p-value.

In the case of when multiple gene lists are provided, the nodes are represented as pie charts, where the size of a pie is proportional to the total number of hits that fall into that specific term. The pie charts are color-coded based on the gene list identities, where the size of a slice represents the percentage of genes under the term that originated from the corresponding gene list. This plot is particularly useful for visualizing whether the terms are shared by multiple lists or unique to a specific list, as well as for understanding how these terms associate with each other within the biological context of the meta study (Figure 4).

Figure 4. Network of enriched terms represented as pie charts, where pies are color-coded based on the identities of the gene lists.

Protein-protein Interaction Enrichment Analysis

For each given gene list, protein-protein interaction enrichment analysis has been carried out with the following databases: STRING8, BioGrid9, OmniPath10, InWeb_IM11.Only physical interactions in STRING (physical score > 0.132) and BioGrid are used (details). The resultant network contains the subset of proteins that form physical interactions with at least one other member in the list. If the network contains between 100 and 1000 proteins, the Molecular Complex Detection (MCODE) algorithm12 has been applied to identify densely connected network components.

Reference

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