This a wrapper function of generateResultsTable

Compound IDs and names matched in pathway are included in the table. Thanks to sergi.picart!

library(tibble)
library(dplyr)
library(magrittr)
library(FELLA)
library(purrr)

## Get compound names from IDs
id_to_name <- function(list.hit) {
  ids_names <- getName(data, list.hit)
  ids_names <- map2(ids_names, names(ids_names), ~ paste(.y, paste(.x, collapse = ","), sep = ": ")) %>% unlist(.)
  return (list(Compound = paste(ids_names, collapse = " | ")))
}

## Add compound names and IDs as a new column in the table
generateResultsTableName <- function(data = data,
                                     object = object,
                                     method="hypergeom",
                                     threshold = 0.1) {

  tab.res <- generateResultsTable(
    method = method,
    threshold = threshold, 
    object = object, 
    data = data)

  if (is.null(tab.res)) return (NA)

  mat.hypergeom <- FELLA:::getMatrix(data, "hypergeom")
  list.cpd <- getInput(object)

  # all the metabolites (path) and those within the list (hits)
  list.path <- apply(mat.hypergeom, 2, function(x) names(which(x)))

  target.path <- purrr::map(tab.res$KEGG.id, ~ list.path[[.x]])
  names(target.path) <- tab.res$KEGG.id

  list.hits <- lapply(target.path, function(x) intersect(x, list.cpd))

  ids_names <- map(list.hits, ~ id_to_name(.x))

  df <- do.call(rbind, ids_names) %>% data.frame() %>% 
    rownames_to_column("KEGG.id") %>% left_join(tab.res, .)
  return (df)
}

Example

## First generate a toy enrichment

data(FELLA.sample)
data(input.sample)
## Enrich input
object <- enrich(
  compounds = input.sample, 
  data = FELLA.sample)

## GenerateResultsTable with compound name
df <- generateResultsTableName(data = FELLA.sample,
                                object = object,
                                method="hypergeom",
                                threshold = 1)
df

Dear liampvmartin1992, please excuse the late reply. I did not notice the comment.

If by "doing someting similar" you mean...

• Finding out what pathways each compound belongs to, originally (right before building the network): you should store the result of KEGGREST::keggLink("cpd", "pathway") when building the network. You can do it a posteriori, but there might be small changes with each KEGG release.

• Finding out what pathways can be directly reached from each compound, right after building the network (e.g. compound C does not belong to pathway P originally, but is related to enzyme E which belongs to P, so P can be reached from C via C-E-P): the answer is the one to the original question above.

• Finding out the contribution of each compound to each pathway: then you can grab the diffusion matrix with the kernel values and work on those directly. See code below.

# example on how to extract individual metabolite contributions in diffusion
# to know which metabolites were in which pathways see 03_hypergeom_getpathways
# https://support.bioconductor.org/p/119264/
library(igraph)
library(FELLA)
library(plyr)
library(dplyr)

data("FELLA.sample")
data("input.sample")

# rebuild diffusion matrix (since matrix is not 
# bundled in the default sample object)
g <- FELLA::getGraph(FELLA.sample)
db.dir <- paste0(tempdir(), "/fellasampledb")
FELLA::buildDataFromGraph(
  g, databaseDir = db.dir, internalDir = FALSE, niter = 10)

# reload db with diffusion matrix
FELLA.samplemat <- FELLA::loadKEGGdata(
  db.dir, internalDir = FALSE, loadMatrix = "diffusion")

# toy analysis
myAnalysis <- enrich(
  compounds = input.sample, 
  method = "diffusion", 
  data = FELLA.samplemat)

df.diffusion <- generateResultsTable(
  method = "diffusion", 
  object = myAnalysis, 
  data = FELLA.samplemat) 

# get diffusion matrix with kernel values
mat.diffusion <- FELLA:::getMatrix(FELLA.samplemat, "diffusion")
# input cpds
list.cpdin <- intersect(FELLA::getInput(myAnalysis), rownames(mat.diffusion))
# all cpds
list.cpdall <- FELLA::getCom(FELLA.samplemat, "compound")

# pathway hits
list.pathhits <- subset(df.diffusion, Entry.type == "pathway")$KEGG.id
# all pathways
list.pathall <- FELLA::getCom(FELLA.samplemat, "pathway")

# columns: input metabolites
# rows: pathways
# values: contribution of compound to pathway score 
mat.contr <- mat.diffusion[list.pathall, list.cpdin]
# The sum of each compound contributions to all pathway is 1
colSums(mat.contr)

# hsa00640 always gets more contribution than hsa00010 for the input compounds
rownames(mat.contr)[apply(mat.contr, 2, which.max)] %>% table

# in general, hsa00640 gets larger contributions than 
# hsa00010 from the compounds in the graph
# (24 compounds with larger contrib from hsa00010, 
# versus 301 compounds from hsa00640)
list.pathall[apply(mat.diffusion[list.pathall, list.cpdall], 2, which.max)] %>% table