RWRFGSEA: Random walk with restart and FGSEA

In vittoriofortino84/COPS: Clustering algorithms for Omics-driven Patient Stratification

Random walk with restart and FGSEA

Description

Runs random walk in a given network starting from seed genes selected from most over/under expressed in the data intersected with known disease genes. The gene affinities are then processed with FGSEA to yield pathway features.

Usage

RWRFGSEA(
  expr,
  gene_network,
  gene_set_list,
  disease_genes = NULL,
  rwr_gene_lists = NULL,
  rwr_seed_size = NULL,
  min_size = 5,
  max_size = 200,
  parallel = 1,
  verbose = FALSE,
  rwr_dnet = FALSE,
  rwr_restart_probability = 0.75,
  rwr_adjacency_normalization = "laplacian",
  rwr_affinity_normalization = "none",
  fgsea_input_cutoff = 0,
  rwr_ecdf = FALSE,
  second_seed_list_reverse_order = FALSE,
  rwr_return_seeds = FALSE,
  fgsea_nperm = 10000,
  logp_scaling = TRUE,
  ...
)

rwr_wrapper(
  expr,
  gene_network,
  rwr_seed_size = nrow(expr)%/%6,
  parallel = 1,
  rwr_restart_probability = 0.75,
  rwr_adjacency_normalization = "laplacian",
  rwr_affinity_normalization = "none",
  rwr_dnet = FALSE,
  verbose = verbose,
  ...
)

fgsea_wrapper(
  data_matrix,
  gene_set_list,
  fgsea_input_cutoff = 0,
  parallel = 1,
  fgsea_nperm = 10000,
  logp_scaling = TRUE,
  ...
)

Arguments

expr	a gene expression matrix with samples on columns
gene_network	an igraph.object with nodes matching to expr rows
gene_set_list	list of character vectors containing gene sets for FGSEA
disease_genes	a character vector containing gene IDs associated with the target disease
rwr_gene_lists	list containing exactly two character vectors corresponding to gene ids. Used to separate genes for double RWR (e.g. up and down regulated seeds) RWR affinities of second seed list will get subtracted from the RWR affinities of the first seed list.
rwr_seed_size	integer, controls the number of gene seed candidates to intersect with the disease genes, defaults to one sixth of rows.
min_size	integer, minimum size of gene sets
max_size	integer, maximum size of gene sets
parallel	integer, number of threads
verbose	TRUE/FALSE
rwr_dnet	if TRUE, uses dRWR. Otherwise uses an internal function with similar options and solve.
rwr_restart_probability	restart probability used for RWR.
rwr_adjacency_normalization	method used to normalise the adjacency matrix of the input graph. See seeded_rwr or dRWR for more details.
rwr_affinity_normalization	method used to normalise the rwr affinity matrix. See dRWR for more details.
fgsea_input_cutoff	a cutoff value used to select most visited genes for FGSEA.
rwr_ecdf	if TRUE, uses ecdf_transform to transform expression values into empirical cumulative probability density.
second_seed_list_reverse_order	if TRUE, seeds for second RWR are selected from based on lowest expression or ecdf.
rwr_return_seeds	if TRUE, returns seeds in output list (not implemented)
fgsea_nperm	a numeric value determining the number of permutations used in fgseaSimple
logp_scaling	if TRUE, multiplies FGSEA normalized enrichment scores (NESs) with the log p.value to give more weight to the most significantly enriched pathways.
...	extra arguments are ignored
data_matrix	numeric values used for ranking, assumes samples on columns

Value

matrix of enrichment scores

Functions

• rwr_wrapper(): Transforms gene-expression values into gene-network node-affinity values

• fgsea_wrapper(): Wrapper for fast pre-ranked gene set enrichment analysis (fgseaSimple)

Examples

library(COPS)

ad_data <- ad_ge_micro_zscore
ad_wgcna_net <- coexpression_network_unweighted(ad_data)
pw_db <- msigdbr::msigdbr(
    species = "Homo sapiens", 
    category = "C2", 
    subcategory = "CP:KEGG")
pw_list <- lapply(split(pw_db, pw_db$gs_name), function(x) x$ensembl_gene) 

ad_rwrfgsea_res <- RWRFGSEA(
    ad_data, 
    ad_wgcna_net, 
    pw_list[1:3], 
    parallel = 1, 
    fgsea_nperm = 1e2)

# Separate genes (e.g. based on DEA)
set.seed(0)
# toy example random fold-change
ad_fc <- sample(c(1,-1), nrow(ad_data), replace = TRUE)

ad_gene_lists <- list(
    rownames(ad_data)[ad_fc > 0], 
    rownames(ad_data)[ad_fc < 0])
ad_rwrfgsea_res <- RWRFGSEA(
    ad_data, 
    ad_wgcna_net, 
    pw_list[1:3], 
    rwr_genelists = ad_gene_lists, 
    rwr_ecdf = TRUE, 
    second_seed_list_reverse_order = TRUE, 
    parallel = 1, 
    fgsea_nperm = 1e2)

## Not run: 
# OTP genes for dermatitis, moderate download size
assoc_score_fields <- paste(
    paste0(
        "&fields=", 
        c("target.gene_info.symbol", "association_score.datatypes")
    ), 
    collapse = ""
)
disease_otp <- COPS::retrieveDiseaseGenesOT(
    "MONDO_0002406", 
    assoc_score_fields)[[1]]
otp_genes <- disease_otp$target.gene_info.symbol[
    disease_otp$association_score.datatypes.genetic_association > 0]

ad_rwrfgsea_res <- RWRFGSEA(
    ad_data, 
    ad_wgcna_net, 
    pw_list[1:3], 
    disease_genes = otp_genes, 
    rwr_genelists = ad_gene_lists, 
    rwr_ecdf = TRUE, 
    second_seed_list_reverse_order = TRUE, 
    parallel = 2)

## End(Not run)

vittoriofortino84/COPS documentation built on Jan. 28, 2025, 3:16 p.m.