msea {fobitools} | R Documentation

## Performs a preranked metabolite set enrichment analysis (MSEA)

### Description

This function performs a MSEA based on the adaptive multilevel splitting Monte Carlo approach.

### Usage

    msea(
      metaboliteRanks,
      subOntology = "food",
      pvalCutoff = 0.01,
      fobi = fobitools::fobi
    )

### Arguments

`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.

### Value

A tibble with MSEA results.

### Author(s)

Pol Castellano-Escuder

### References

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.

### Examples

    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)

[Package fobitools version 1.0.0 Index]