Metascape Gene List Analysis Report
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Bar Graph Summary
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 2024-09-01). If multiple identifiers correspond to the same Entrez gene ID, they will be considered as a single Entrez gene ID in downstream analyses. The gene lists are summarized in Table 1.
Table 1. Statistics of input gene lists.
Name |
Total |
Unique |
MyList |
35 |
35 |
Gene Annotation
The following are the list of annotations retrieved from the latest version of the database (last updated on 2024-09-01) (Table 2).
Table 2. Gene annotations extracted
Name |
Type |
Description |
Gene Symbol |
Description |
Primary HUGO gene symbol. |
Description |
Description |
Short description. |
Biological Process (GO) |
Function/Location |
Descriptions summarized based on gene ontology database, where up to three most informative GO terms are kept. |
Kinase Class (UniProt) |
Function/Location |
Detailed kinase classes. |
Protein Function (Protein Atlas) |
Function/Location |
Protein Function (Protein Atlas) |
Subcellular Location (Protein Atlas) |
Function/Location |
Subcellular Location (Protein Atlas) |
Drug (DrugBank) |
Genotype/Phenotype/Disease |
Drug information for the given gene as target. |
Protein Functions (ChatGPT) |
Description |
Uncurated gene functions described by ChatGPT. |
Disease & Drugs (ChatGPT) |
Genotype/Phenotype/Disease |
Uncurated disease and drug associations described by ChatGPT. |
Canonical Pathways
|
Ontology |
Canonical Pathways
|
Hallmark Gene Sets
|
Ontology |
Hallmark Gene Sets
|
Pathway and Process Enrichment Analysis
For each given gene list, pathway and process enrichment analysis have been carried out with the following ontology sources: KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways, CORUM, WikiPathways, and PANTHER Pathway. 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 cumulative hypergeometric distribution
2, and q-values are calculated using the Benjamini-Hochberg procedure to account for multiple testings
3. Kappa scores
4 are used as the similarity metric when performing hierarchical 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.
Table 3. Top 3 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.
GO |
Category |
Description |
Count |
% |
Log10(P) |
Log10(q) |
GO:0048167 |
GO Biological Processes |
regulation of synaptic plasticity |
4 |
11.43 |
-3.92 |
0.00 |
GO:0070555 |
GO Biological Processes |
response to interleukin-1 |
3 |
8.57 |
-3.61 |
0.00 |
hsa04080 |
KEGG Pathway |
Neuroactive ligand-receptor interaction |
4 |
11.43 |
-3.08 |
0.00 |
To further capture the relationships between the terms, a subset of enriched terms has 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
Cytoscape5, where each node represents an enriched term and is colored first by its cluster ID (Figure 2.a) and then by its p-value (Figure 2.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 2. 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.
Protein-protein Interaction Enrichment Analysis
For each given gene list, protein-protein interaction enrichment analysis has been carried out with the following databases: STRING
6, BioGrid
7, OmniPath
8, InWeb_IM
9.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 3 and 500 proteins, the Molecular Complex Detection (MCODE) algorithm
10 has been applied to identify densely connected network components.
Quality Control and Association Analysis
Gene list enrichments are identified in the following ontology categories: Cell_Type_Signatures, DisGeNET, PaGenBase, Transcription_Factor_Targets. 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. The top few enriched clusters (one term per cluster) are shown in the Figure 3-6. The algorithm used here is the same as that is used for pathway and process enrichment analysis.
Figure 3. Summary of enrichment analysis in Cell Type Signatures11.
|
|
GO |
Description |
Count |
% |
Log10(P) |
Log10(q) |
M39068 |
MANNO MIDBRAIN NEUROTYPES HDA1 |
6 |
17.00 |
-4.30 |
0 |
M39070 |
MANNO MIDBRAIN NEUROTYPES HNBGABA |
6 |
17.00 |
-3.90 |
0 |
M39072 |
MANNO MIDBRAIN NEUROTYPES HSERT |
5 |
14.00 |
-3.80 |
0 |
M39067 |
MANNO MIDBRAIN NEUROTYPES HDA |
5 |
14.00 |
-3.60 |
0 |
M39069 |
MANNO MIDBRAIN NEUROTYPES HDA2 |
5 |
14.00 |
-3.50 |
0 |
M40149 |
DESCARTES FETAL ADRENAL SYMPATHOBLASTS |
3 |
8.60 |
-3.50 |
0 |
M39161 |
GAO LARGE INTESTINE ADULT CA ENTEROENDOCRINE CELLS |
4 |
11.00 |
-3.30 |
0 |
M40229 |
DESCARTES FETAL LIVER MYELOID CELLS |
3 |
8.60 |
-2.70 |
0 |
M39066 |
MANNO MIDBRAIN NEUROTYPES HNBML5 |
4 |
11.00 |
-2.70 |
0 |
M39168 |
MURARO PANCREAS ALPHA CELL |
4 |
11.00 |
-2.40 |
0 |
M39065 |
MANNO MIDBRAIN NEUROTYPES HRN |
3 |
8.60 |
-2.20 |
0 |
M39052 |
MANNO MIDBRAIN NEUROTYPES HOPC |
3 |
8.60 |
-2.10 |
0 |
|
Figure 4. Summary of enrichment analysis in DisGeNET12.
|
|
GO |
Description |
Count |
% |
Log10(P) |
Log10(q) |
C0001973 |
Alcoholic Intoxication, Chronic |
5 |
14.00 |
-3.30 |
0 |
C0013170 |
Drug habituation |
3 |
8.60 |
-2.80 |
0 |
C0017601 |
Glaucoma |
5 |
14.00 |
-2.70 |
0 |
C0038586 |
Substance Use Disorders |
3 |
8.60 |
-2.70 |
0 |
C0006012 |
Borderline Personality Disorder |
3 |
8.60 |
-2.70 |
0 |
C1510472 |
Drug Dependence |
3 |
8.60 |
-2.50 |
0 |
C0039483 |
Giant Cell Arteritis |
3 |
8.60 |
-2.50 |
0 |
C0007682 |
CNS disorder |
3 |
8.60 |
-2.20 |
0 |
C0007786 |
Brain Ischemia |
3 |
8.60 |
-2.10 |
0 |
C0279671 |
Cervical Squamous Cell Carcinoma |
3 |
8.60 |
-2.10 |
0 |
C0677932 |
Progressive Neoplastic Disease |
3 |
8.60 |
-2.00 |
0 |
C3539781 |
Progressive cGVHD |
3 |
8.60 |
-2.00 |
0 |
|
Figure 5. Summary of enrichment analysis in PaGenBase13.
|
|
GO |
Description |
Count |
% |
Log10(P) |
Log10(q) |
PGB:00032 |
Tissue-specific: Cerebellum |
3 |
8.60 |
-3.20 |
0 |
|
Figure 6. Summary of enrichment analysis in Transcription Factor Targets.
|
|
GO |
Description |
Count |
% |
Log10(P) |
Log10(q) |
M11934 |
SRF Q5 01 |
4 |
11.00 |
-3.90 |
0 |
M5479 |
SRF Q4 |
4 |
11.00 |
-3.90 |
0 |
M6885 |
AREB6 02 |
4 |
11.00 |
-3.70 |
0 |
M7023 |
CRX Q4 |
4 |
11.00 |
-3.60 |
0 |
M40745 |
ZNF140 TARGET GENES |
5 |
14.00 |
-3.40 |
0 |
M19642 |
CDPCR1 01 |
3 |
8.60 |
-3.40 |
0 |
M10046 |
ATF B |
3 |
8.60 |
-2.90 |
0 |
M11838 |
FOXD3 01 |
3 |
8.60 |
-2.80 |
0 |
M17997 |
TGACGTCA ATF3 Q6 |
3 |
8.60 |
-2.60 |
0 |
M5954 |
NFY C |
3 |
8.60 |
-2.50 |
0 |
M3109 |
CEBP Q3 |
3 |
8.60 |
-2.50 |
0 |
M11370 |
CREBP1 Q2 |
3 |
8.60 |
-2.50 |
0 |
M6517 |
AAAYWAACM HFH4 01 |
3 |
8.60 |
-2.50 |
0 |
M16699 |
CREBP1CJUN 01 |
3 |
8.60 |
-2.50 |
0 |
M9955 |
TGAYRTCA ATF3 Q6 |
4 |
11.00 |
-2.40 |
0 |
M2054 |
AP1 Q6 01 |
3 |
8.60 |
-2.40 |
0 |
M17180 |
CREB 01 |
3 |
8.60 |
-2.40 |
0 |
M4803 |
NF1 Q6 |
3 |
8.60 |
-2.40 |
0 |
M11244 |
AREB6 01 |
3 |
8.60 |
-2.40 |
0 |
M12915 |
NF1 Q6 01 |
3 |
8.60 |
-2.40 |
0 |
|
Reference
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