Basic Workflow

This is a minimal example. For details, please see following articles on the left of website.

Basic enrichment analysis involves performing a batch “dictionary lookup” for a set of genes to determine their associations. The most commonly used method is over-representation analysis (ORA). If you also have information on weights, then Gene Set Enrichment Analysis (GSEA) is another classic choice. After getting enriched result, a re-enrich analysis may help you to get more insightful result.

ORA

library(dplyr)
library(tibble)
1library(org.Hs.eg.db)
library(gt)
library(EnrichGT)
library(readr)

DEGexample <- read_csv("~/Documents/4Fun/EGTFun/DEG.csv")
DEGexample2 <- DEGexample |> dplyr::filter(pvalue<0.05)
DEGexample_UpReg <- DEGexample |> dplyr::filter(pvalue<0.05,log2FoldChange>0.7)
DEGs <- DEGexample_UpReg$...1
# The first example: 
ora_result <- egt_enrichment_analysis(
2                genes = DEGs,
3                database = database_Reactome(OrgDB = org.Hs.eg.db)
                )
# The second example: 
another_example <- egt_enrichment_analysis(
4                genes = genes_with_weights(DEGexample2$...1,DEGexample2$log2FoldChange),
5                database = database_GO_BP(org.Hs.eg.db))
# Ploting
6egt_plot_results(ora_result,showIDs = T,ntop = 20)

1

EnrichGT use AnnotationDbi for fetching most of databases and gene annotations. If throwing an error, please re-check this step;

2

ORA just need 2 input. The first is a character vector containg gene symbols like c("TP53","PLP1","FABP1","VCAM1");

3

The second is your favourite database. EnrichGT supports many. See Database usage;

4

Or you want input genes with direction. See enrichment details;

5

Enriching using Gene Ontology BP;

6

Showing enrichment result as figure. See more in visualization help.

Re-enrichment

This support the ORA output from both EnrichGT and clusterProfiler. So you can use your favourite tool to achieve this.

1re_enrichment_results <- egt_recluster_analysis(ora_result)
2re_enrichment_results
3egt_plot_results(re_enrichment_results,ntop = 3)
4egt_plot_umap(re_enrichment_results)
5re_enrichment_results |> egt_infer_act(DB = "collectri", species = "human")

1

Doing re-enrichment analysis (See Re-enrichment usage);

2

Showing GT HTML report, see more in visualization help;

3

Viewing re-enrichment result as dot plot, see more in visualization help;

4

Viewing re-enrichment result as UMAP plot, see more in visualization help;

5

Infering TF/pathway activity, experimential, might not be correct, see more in re-enrichment help.

GSEA

If you have a set of genes with known weight like log2FC, or the loading from PCA or NMF…

GSEAexample <- egt_gsea_analysis(
            genes = 
1              genes_with_weights(DEGexample2$...1,DEGexample2$log2FoldChange),
            database = 
2              database_from_gmt("gmt_file.gmt")
            )
egt_plot_results(GSEAexample) # showing GSEA result

1

GSEA is also supported, see enrichment details. genes_with_weights() is used to generate weighted genes;

2

Additional pathway information like GMT file or table is also supported. See Database usage.

Fusing ORA results

This function support the ORA output from both EnrichGT and clusterProfiler. So you can use your favourite tool to achieve this.

# Fusing results: 
1ora_result_A <- egt_enrichment_analysis(genes = DEGs, database = database_Reactome(OrgDB = org.Hs.eg.db))
2ora_result_B <- egt_enrichment_analysis(genes = DEGs, database = database_kegg(kegg_organism = "hsa",OrgDB = org.Hs.eg.db))
3fused_result <- egt_recluster_analysis(list(ora_result_A,ora_result_B))

1

ORA result 1 with Reactome;

2

ORA result 2 with KEGG;

3

Fuse together.

Important

Only same source of enrichment results can be merge.

For example, the result of

egt_enrichment_analysis(genes = DEGs, database = database_Reactome(OrgDB = org.Hs.eg.db))

and

egt_enrichment_analysis(genes = DEGs, database = database_kegg(OrgDB = org.Hs.eg.db))

can be merged,

but the result of

egt_enrichment_analysis(genes = DEGs, database = database_Reactome(OrgDB = org.Hs.eg.db))

and

egt_enrichment_analysis(genes = genes_with_weights(DEGexample2$...1,DEGexample2$log2FoldChange), database = database_kegg(OrgDB = org.Hs.eg.db))

CAN’T be merged.

If you have ORA results that want to compare, please use egt_compare_groups().

Gene Annotation Converter

1IDs_of_genes <- convert_annotations_genes(DEGexample$...1[1:10], from_what="SYMBOL", to_what=c("ENTREZID","ENSEMBL","GENENAME"), OrgDB=org.Hs.eg.db)

1

Just told it from what and to what :)