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Community Detection and Pathway Enrichment Analysis

Source: R/community_path.R
community_path.Rd

Detects gene communities within an adjacency network using one or two community detection methods, and performs pathway enrichment for each detected community.

Usage

community_path(
  adj_matrix,
  methods = "louvain",
  pathway_db = "KEGG",
  organism = c("human", "mouse"),
  genes_path = 5,
  plot = TRUE,
  verbose = TRUE,
  method_params = list(),
  comparison_params = list(),
  BPPARAM = BiocParallel::bpparam()
)

Arguments

adj_matrix

A square adjacency matrix or a SummarizedExperiment object containing a single adjacency matrix. Row and column names must correspond to gene symbols.

methods

A character vector of one or two community detection methods supported by robin. If two are given, performance is compared and the best is selected. Default: "louvain".

pathway_db

Character string specifying the pathway database to use: "KEGG" or "Reactome". Default: "KEGG".

organism

Character string specifying the organism: "human" or "mouse". Default: "human".

genes_path

Integer. Minimum number of genes per community to run enrichment analysis. Default: 5.

plot

Logical. If TRUE, generates a plot of detected communities. Default: TRUE.

verbose

Logical. If TRUE, shows progress messages. Default: TRUE.

method_params

List of parameters for community detection methods. Common parameters include:

  • resolution: Resolution parameter for Louvain/Leiden (default: 1)

  • steps: Number of steps for Walktrap (default: 4)

  • spins: Number of spins for Spinglass (default: 25)

  • nb.trials: Number of trials for Infomap (default: 10)

comparison_params

List of parameters for robin comparison:

  • measure: Stability measure ("vi", "nmi", "split.join", "adjusted.rand"). Default: "vi"

  • type: Robin construction type ("dependent", "independent"). Default: "independent"

  • rewire.w.type: Rewiring strategy for weighted graphs. Default: "Rewire"

BPPARAM

BiocParallel backend for parallel processing. Default: BiocParallel::bpparam().

Value

A list with elements:

  • communities: List with best_method and a named vector of community membership per gene.

  • pathways: List of enrichment results per community (only for communities meeting size threshold).

  • graph: The igraph object with community annotations.

Details

If two methods are provided, the function uses robinCompare and selects the method with higher AUC. Pathway enrichment is done via clusterProfiler (KEGG) or via ReactomePA (Reactome). Communities smaller than genes_path are excluded.

Examples

data(toy_counts)


# Infer networks (toy_counts is already a MultiAssayExperiment)
networks <- infer_networks(
    count_matrices_list = toy_counts,
    method = "GENIE3",
    nCores = 1
)
head(networks[[1]])
#>   regulatoryGene targetGene    weight
#> 1          HLA-B        FTL 0.2057753
#> 2          HLA-A      HLA-B 0.1676587
#> 3           CD74      CXCR4 0.1484850
#> 4          HLA-B      HLA-A 0.1451776
#> 5            FTL       FTH1 0.1370328
#> 6           FTH1        FTL 0.1369161

# Generate and symmetrize adjacency matrices (returns SummarizedExperiment)
wadj_se <- generate_adjacency(networks)
swadj_se <- symmetrize(wadj_se, weight_function = "mean")

# Apply cutoff (returns SummarizedExperiment)
binary_se <- cutoff_adjacency(
    count_matrices = toy_counts,
    weighted_adjm_list = swadj_se,
    n = 1,
    method = "GENIE3",
    quantile_threshold = 0.95,
    nCores = 1,
    debug = TRUE
)
#> [Method: GENIE3] Matrix 1 → Cutoff = 0.06362
#> [Method: GENIE3] Matrix 2 → Cutoff = 0.06895
#> [Method: GENIE3] Matrix 3 → Cutoff = 0.06286

# Create consensus (returns SummarizedExperiment)
consensus <- create_consensus(binary_se, method = "union")

# community_path now accepts SummarizedExperiment objects directly
comm_cons <- community_path(consensus)
#> 
#> 
#> Detecting communities...

#> Running pathway enrichment...
#> 'select()' returned 1:1 mapping between keys and columns
#> Reading KEGG annotation online: "https://rest.kegg.jp/link/hsa/pathway"...
#> Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/hsa"...
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns