This function performs a MSEA based on the adaptive multilevel splitting Monte Carlo approach.
msea(
metaboliteRanks,
subOntology = "food",
pvalCutoff = 0.01,
fobi = fobitools::fobi
)A named vector of FOBI metabolite identifiers with their metabolite-level stats.
A character string specifying one of the two FOBI sub-ontologies: "food", or "biomarker".
A numeric value indicating a p-value cutoff for raw p-values generated by MSEA.
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.
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)
#> # A tibble: 141 × 8
#> className classSize log2err ES NES pval padj leadingEdge
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <list>
#> 1 legume food product 29 0.204 0.601 1.34 0.0480 0.996 <chr [11]>
#> 2 sugar 2 0.134 0.841 1.54 0.104 0.996 <chr [2]>
#> 3 soybean (whole) 12 0.129 0.606 1.27 0.111 0.996 <chr [7]>
#> 4 soft drink 1 0.128 0.898 1.79 0.114 0.996 <chr [1]>
#> 5 meat food product 11 0.105 0.588 1.22 0.160 0.996 <chr [7]>
#> 6 egg food product 8 0.101 0.615 1.23 0.169 0.996 <chr [7]>
#> 7 beer 12 0.0953 0.565 1.19 0.187 0.996 <chr [4]>
#> 8 grapefruit (whole, ra… 5 0.0911 0.646 1.22 0.201 0.996 <chr [3]>
#> 9 nut (whole or part) 25 0.0892 0.523 1.17 0.208 0.996 <chr [10]>
#> 10 citrus fruit food pro… 1 0.0789 0.763 1.52 0.251 0.996 <chr [1]>
#> # ℹ 131 more rows
# Chemical class enrichment analysis
fobitools::msea(metaboliteRanks = metaboliteRanks,
subOntology = "biomarker",
pvalCutoff = 1)
#> # A tibble: 48 × 8
#> className classSize log2err ES NES pval padj leadingEdge
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <list>
#> 1 5-hydroxypsoralens 1 0.381 1 1.99 0.0155 0.743 <chr [1]>
#> 2 Para cresols 1 0.245 0.983 1.95 0.0340 0.815 <chr [1]>
#> 3 Catechols 1 0.174 0.949 1.89 0.0649 0.846 <chr [1]>
#> 4 Xanthines 1 0.152 0.932 1.85 0.0829 0.846 <chr [1]>
#> 5 Salicylic acids 1 0.114 0.881 1.75 0.138 0.846 <chr [1]>
#> 6 Indolyl carboxylic ac… 1 0.107 0.864 1.72 0.155 0.846 <chr [1]>
#> 7 1-hydroxy-2-unsubstit… 1 0.0944 0.831 1.65 0.190 0.846 <chr [1]>
#> 8 4'-O-methylisoflavones 2 0.0873 0.758 1.40 0.215 0.846 <chr [2]>
#> 9 1-hydroxy-4-unsubstit… 1 0.0848 0.797 1.58 0.225 0.846 <chr [1]>
#> 10 O-glycosyl compounds 2 0.0848 0.746 1.38 0.225 0.846 <chr [2]>
#> # ℹ 38 more rows