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Master annotation pipeline: download, load, and run all enrichments

Source: R/hc_annotate.R
hc_annotate.Rd

hc_annotate() is a master function that performs functional enrichment analysis. It downloads necessary annotation databases, formats them, and runs enrichment analyses for Gene Ontology (GO), KEGG pathways, and custom databases (Human Protein Atlas, Reactome, TRRUST, PanglaoDB) based on the clustering results stored in the AnnDatR object. It also generates visualizations such as treemaps for GO terms and bubblemaps for all enrichment results.

Usage

hc_annotate(
  AnnDatR,
  dbs = c("GO", "KEGG", "Others"),
  universe = NULL,
  simplify_go = TRUE,
  db_loc = "databases",
  hpa_version = 24,
  verbose = TRUE
)

Arguments

AnnDatR

AnnDatR object containing clustering results

dbs

Character vector of databases to use for enrichment ("GO", "KEGG", "Others") (default: all)

universe

Character vector of background genes (default: NULL, all genes in clustering data)

simplify_go

Logical, group semantically similar GO terms and plot Tree map

db_loc

Directory to store annotation databases (default: "databases")

hpa_version

Version of the Human Protein Atlas to download (default: 24)

verbose

Logical, print progress messages (default: TRUE)

Value

A named list with elements:

  • enrichment: Data frame with combined enrichment results from all selected databases

  • treemaps: List of ggplot objects with GO treemaps per cluster (if GO selected)

  • bubblemap_go: ggplot object with GO enrichment bubblemap (if GO selected)

  • bubblemap_kegg: ggplot object with KEGG enrichment bubblemap (if KEGG selected)

  • bubblemap_others: ggplot object with custom database enrichment bubblemap (if Others selected)

Details

In case some of the databases did not download correctly, please rerun the function. If that does not help, please manually download the databases, place them in the specified db_loc directory and unzip the zipped files. Links:

  • Human Protein Atlas version 24: https://v%d.proteinatlas.org/download/proteinatlas.tsv.zip

  • Reactome: https://reactome.org/download/current/Ensembl2Reactome_All_Levels.txt

  • Trrust: https://www.grnpedia.org/trrust/data/trrust_rawdata.human.tsv

  • Panglao: https://panglaodb.se/markers/PanglaoDB_markers_27_Mar_2020.tsv.gz

Or just run the annotation only using KEGG and GO by setting dbs = c("KEGG", "GO").

This function will require time depending on the size of the dataset and the number of clusters, especially if GO is included. Set verbose = TRUE to monitor the progress.

For the bubblemaps, the odds ratio is capped at 50 for better visualization. Also, only the top 150 terms (by mean odds ratio across clusters) are shown.

Examples

# Run clustering pipeline
adata_res <- hc_pca(example_adata, components = 40)
adata_res <- hc_distance(adata_res, components = 20)
adata_res <- hc_snn(adata_res, neighbors = 15)
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Building SNN based on a provided distance matrix
#> Computing SNN
adata_res <- hc_cluster_consensus(adata_res, resolution = 7)
#> Iteration: 0 *** value: 948.279
#> Iteration: 1 *** value: 70.5063
#> Iteration: 2 *** value: 22.246
#> Iteration: 3 *** value: 22.2447
#> Iteration: 4 *** value: 22.2447
#> Minimum: 22.2447
#> Joining with `by = join_by(cons_cluster)`

# Run annotation pipeline
enrichment_results <- hc_annotate(adata_res, dbs = "KEGG")
#> 
#> 
#> 'select()' returned 1:many mapping between keys and columns
#> Warning: 0.82% of input gene IDs are fail to map...
#> No KEGG enrichment result for cluster: 13
#> No KEGG enrichment result for cluster: 22
#> No KEGG enrichment result for cluster: 12
#> No KEGG enrichment result for cluster: 19
#> No KEGG enrichment result for cluster: 26
#> No KEGG enrichment result for cluster: 9
#> No KEGG enrichment result for cluster: 31
#> No KEGG enrichment result for cluster: 23
#> No KEGG enrichment result for cluster: 15
#> No KEGG enrichment result for cluster: 24
#> No KEGG enrichment result for cluster: 8
#> No KEGG enrichment result for cluster: 28
#> No KEGG enrichment result for cluster: 10
#> No KEGG enrichment result for cluster: 1
#> No KEGG enrichment result for cluster: 6
#> No KEGG enrichment result for cluster: 25
#> No KEGG enrichment result for cluster: 4
#> No KEGG enrichment result for cluster: 11
#> No KEGG enrichment result for cluster: 18
#> No KEGG enrichment result for cluster: 30
#> No KEGG enrichment result for cluster: 16
#> 'select()' returned 1:many mapping between keys and columns
#> KEGG enrichment done.
#> 'select()' returned 1:many mapping between keys and columns
head(enrichment_results$enrichment)
#> # A tibble: 6 × 10
#>   `Cluster ID` Database      `Term ID` Term          GeneRatio BgRatio `P-value`
#>   <chr>        <chr>         <chr>     <chr>         <chr>     <chr>       <dbl>
#> 1 14           KEGG pathways hsa03040  Spliceosome   8/27      13/349   4.40e- 7
#> 2 17           KEGG pathways hsa04382  Cornified en… 14/16     40/349   6.84e-13
#> 3 2            KEGG pathways hsa04660  T cell recep… 6/11      13/349   3.02e- 7
#> 4 2            KEGG pathways hsa04640  Hematopoieti… 4/11      11/349   1.60e- 4
#> 5 2            KEGG pathways hsa05166  Human T-cell… 4/11      12/349   2.36e- 4
#> 6 2            KEGG pathways hsa05170  Human immuno… 3/11      15/349   8.65e- 3
#> # ℹ 3 more variables: `Adjusted P-value` <dbl>, `Gene IDs` <chr>,
#> #   `Gene names` <chr>