msea | R Documentation |
This function performs a MSEA based on the adaptive multilevel splitting Monte Carlo approach.
msea(
metaboliteRanks,
subOntology = "food",
pvalCutoff = 0.01,
fobi = fobitools::fobi
)
metaboliteRanks |
A named vector of FOBI metabolite identifiers with their metabolite-level stats. |
subOntology |
A character string specifying one of the two FOBI sub-ontologies: "food", or "biomarker". |
pvalCutoff |
A numeric value indicating a p-value cutoff for raw p-values generated by MSEA. |
fobi |
FOBI table obtained with 'parse_fobi()'. If this value is set to NULL, the last version of FOBI will be downloaded from GitHub. |
A tibble with MSEA results.
Pol Castellano-Escuder
G. Korotkevich, V. Sukhov, A. Sergushichev. Fast gene set enrichment analysis. bioRxiv (2019), doi:10.1101/060012
Pol Castellano-Escuder, Raúl González-Domínguez, David S Wishart, Cristina Andrés-Lacueva, Alex Sánchez-Pla, FOBI: an ontology to represent food intake data and associate it with metabolomic data, Database, Volume 2020, 2020, baaa033, https://doi.org/10.1093/databa/baaa033.
metabolites <- c(fobitools::idmap$FOBI[1:49], fobitools::idmap$FOBI[70:80])
random_pvals <- c(runif(n = length(metabolites)*0.3, min = 0.001, max = 0.05), runif(n = length(metabolites)*0.7, min = 0.05, max = 0.99))
names(random_pvals) <- metabolites
metaboliteRanks <- random_pvals[order(random_pvals)]
# Food enrichment analysis
fobitools::msea(metaboliteRanks = metaboliteRanks,
pvalCutoff = 1)
# Chemical class enrichment analysis
fobitools::msea(metaboliteRanks = metaboliteRanks,
subOntology = "biomarker",
pvalCutoff = 1)
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