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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",
  genes_path = 5,
  plot = TRUE,
  verbose = TRUE,
  method_params = list(),
  comparison_params = list(),
  BPPARAM = BiocParallel::bpparam()
)

Arguments

adj_matrix

A square 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".

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(count_matrices)
data(adj_truth)
networks <- infer_networks(
    count_matrices_list = count_matrices,
    method = "GENIE3",
    nCores = 1
)
head(networks[[1]])
#>   regulatoryGene targetGene    weight
#> 1          ARPC2      ARPC3 0.2003304
#> 2          HLA-A       CD74 0.1626279
#> 3          ARPC3      ARPC2 0.1569443
#> 4           CD3E        JUN 0.1558975
#> 5          UBA52     GNB2L1 0.1452854
#> 6          HLA-E        FOS 0.1436726

wadj_list <- generate_adjacency(networks)
swadj_list <- symmetrize(wadj_list, weight_function = "mean")

binary_listj <- cutoff_adjacency(
    count_matrices = count_matrices,
    weighted_adjm_list = swadj_list,
    n = 2,
    method = "GENIE3",
    quantile_threshold = 0.99,
    nCores = 1,
    debug = TRUE
)
#> [Method: GENIE3] Matrix 1 → Cutoff = 0.09447
#> [Method: GENIE3] Matrix 2 → Cutoff = 0.10483
#> [Method: GENIE3] Matrix 3 → Cutoff = 0.11466
head(binary_listj[[1]])
#>       ACTG1 ARPC2 ARPC3 BTF3 CD3D CD3E CD74 CFL1 COX4I1 COX7C CXCR4 EEF1A1
#> ACTG1     0     0     0    0    0    0    0    1      0     0     0      0
#> ARPC2     0     0     1    0    0    0    0    0      0     0     0      0
#> ARPC3     0     1     0    0    0    0    0    0      0     0     0      0
#> BTF3      0     0     0    0    0    0    0    0      0     0     0      0
#> CD3D      0     0     0    0    0    1    0    0      0     0     0      0
#> CD3E      0     0     0    0    1    0    0    0      0     0     0      0
#>       EEF1D EEF2 EIF1 EIF3K EIF4A2 FOS FTH1 FTL GNB2L1 HLA-A HLA-B HLA-C HLA-E
#> ACTG1     0    0    0     0      0   0    0   0      0     0     0     0     0
#> ARPC2     0    0    0     0      0   0    0   0      0     0     0     0     1
#> ARPC3     0    0    0     0      0   0    0   0      0     0     0     0     0
#> BTF3      0    0    0     0      0   0    0   0      0     0     0     0     0
#> CD3D      0    0    0     0      0   0    0   0      0     0     0     0     0
#> CD3E      0    0    0     0      0   0    0   0      0     0     0     0     0
#>       JUN JUNB MYL12B MYL6 NACA PABPC1 PFN1 TMSB4X UBA52 UBC
#> ACTG1   0    0      0    0    0      0    0      0     0   0
#> ARPC2   0    0      0    0    0      0    0      0     0   0
#> ARPC3   0    0      0    0    0      0    0      0     0   0
#> BTF3    0    0      0    0    0      0    0      0     0   0
#> CD3D    0    0      0    0    0      0    0      0     0   0
#> CD3E    1    0      0    0    0      0    0      0     0   0

consensus <- create_consensus(binary_listj, method = "vote")
comm_cons <- community_path(consensus)
#> Detecting communities...
#> Warning: minimal value for n is 3, returning requested palette with 3 different levels

#> Running pathway enrichment...