Simple food over representation analysis (ORA)

Compiled date: 2026-05-30

Last edited: 2022-01-12

License: GPL-3

Installation

Run the following code to install the Bioconductor version of the package.

# install.packages("BiocManager")
BiocManager::install("fobitools")

Load fobitools

library(fobitools)

You can also load some additional packages that will be very useful in this vignette.

library(dplyr)
library(kableExtra)

metaboliteUniverse and metaboliteList

In microarrays, for example, we can study almost all the genes of an organism in our sample, so it makes sense to perform an over representation analysis (ORA) considering all the genes present in Gene Ontology (GO). Since most of the GO pathways would be represented by some gene in the microarray.

This is different in nutrimetabolomics. Targeted nutrimetabolomics studies sets of about 200-500 diet-related metabolites, so it would not make sense to use all known metabolites (for example in HMDB or CHEBI) in an ORA, as most of them would not have been quantified in the study.

In nutrimetabolomic studies it may be interesting to study enriched or over represented foods/food groups by the metabolites resulting from the study statistical analysis, rather than the enriched metabolic pathways, as would make more sense in genomics or other metabolomics studies.

The Food-Biomarker Ontology (FOBI) provides a biological knowledge for conducting these enrichment analyses in nutrimetabolomic studies, as FOBI provides the relationships between several foods and their associated dietary metabolites (Castellano-Escuder et al. 2020).

Accordingly, to perform an ORA with the fobitools package, it is necessary to provide a metabolite universe (all metabolites included in the statistical analysis) and a list of selected metabolites (selected metabolites according to a statistical criterion).

Here is an example:

# select 300 random metabolites from FOBI
idx_universe <- sample(nrow(fobitools::idmap), 300, replace = FALSE)
metaboliteUniverse <- fobitools::idmap %>%
  dplyr::slice(idx_universe) %>%
  pull(FOBI)

# select 10 random metabolites from metaboliteUniverse that are associated with 'Red meat' (FOBI:0193), 
# 'Lean meat' (FOBI:0185) , 'egg food product' (FOODON:00001274), 
# or 'grape (whole, raw)' (FOODON:03301702)
fobi_subset <- fobitools::fobi %>% # equivalent to `parse_fobi()`
  filter(FOBI %in% metaboliteUniverse) %>%
  filter(id_BiomarkerOf %in% c("FOBI:0193", "FOBI:0185", "FOODON:00001274", "FOODON:03301702")) %>%
  dplyr::slice(sample(nrow(.), 10, replace = FALSE))

metaboliteList <- fobi_subset %>%
  pull(FOBI)
fobitools::ora(metaboliteList = metaboliteList, 
               metaboliteUniverse = metaboliteUniverse, 
               subOntology = "food", 
               pvalCutoff = 0.01)
className classSize overlap pval padj overlapMetabolites
Red meat 15 7 0.0000000 0.0000002 FOBI:030691, FOBI:030553, FOBI:030343, FOBI:030689, FOBI:030707, FOBI:050262, FOBI:050034
black pepper food product 6 4 0.0000031 0.0002298 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
almond (whole, raw) 8 4 0.0000142 0.0002998 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
green tea leaf (dry) 8 4 0.0000142 0.0002998 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
kale leaf (raw) 8 4 0.0000142 0.0002998 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
red tea 8 4 0.0000142 0.0002998 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
red velvet 8 4 0.0000142 0.0002998 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
black currant (whole, raw) 9 4 0.0000252 0.0004152 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
carrot root (whole, raw) 9 4 0.0000252 0.0004152 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
soybean oil 4 3 0.0000496 0.0006717 FOBI:030343, FOBI:050262, FOBI:050034
olive (whole, ripe) 11 4 0.0000647 0.0006717 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
strawberry (whole, raw) 12 4 0.0000960 0.0006717 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
bell pepper 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
cabbage (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
cucumber (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
hazelnut 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
hot pepper vegetable food product 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
kiwi 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
pineapple (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
potato (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
pumpkin (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
radish (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
raisin (whole) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
sauerkraut 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
spinach (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
watermelon (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
yellow bell pepper (whole, raw) 5 3 0.0001225 0.0006717 FOBI:030553, FOBI:050262, FOBI:050034
grape (whole, raw) 13 4 0.0001371 0.0006762 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
sweet potato vegetable food product 13 4 0.0001371 0.0006762 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
tomato (whole, raw) 13 4 0.0001371 0.0006762 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
grapefruit (whole, raw) 14 4 0.0001898 0.0008527 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
apricot (whole, raw) 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
banana (whole, ripe) 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
blackberry (whole, raw) 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
bread food product 6 3 0.0002420 0.0008527 FOBI:030343, FOBI:050262, FOBI:050034
cauliflower (whole, raw) 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
cereal 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
cereal food product 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
lettuce vegetable food product 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
peach (whole, raw) 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
peanut (whole, raw) 6 3 0.0002420 0.0008527 FOBI:030553, FOBI:050262, FOBI:050034
whole bread 6 3 0.0002420 0.0008527 FOBI:030343, FOBI:050262, FOBI:050034
oil 15 4 0.0002559 0.0008809 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
coffee (liquid drink) 16 4 0.0003375 0.0011099 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
cumin seed (whole, dried) 16 4 0.0003375 0.0011099 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
black tea leaf (dry) 7 3 0.0004181 0.0011899 FOBI:030553, FOBI:030343, FOBI:050034
grain plant 7 3 0.0004181 0.0011899 FOBI:030343, FOBI:050262, FOBI:050034
grain product 7 3 0.0004181 0.0011899 FOBI:030343, FOBI:050262, FOBI:050034
pear (whole, raw) 7 3 0.0004181 0.0011899 FOBI:030553, FOBI:050262, FOBI:050034
plum (whole, raw) 7 3 0.0004181 0.0011899 FOBI:030553, FOBI:050262, FOBI:050034
rye food product 7 3 0.0004181 0.0011899 FOBI:030343, FOBI:050262, FOBI:050034
wheat 7 3 0.0004181 0.0011899 FOBI:030343, FOBI:050262, FOBI:050034
wine (food product) 18 4 0.0005548 0.0015493 FOBI:030553, FOBI:030343, FOBI:050262, FOBI:050034
broccoli floret (whole, raw) 9 3 0.0009780 0.0026805 FOBI:030553, FOBI:050262, FOBI:050034
bean (whole) 10 3 0.0013793 0.0036454 FOBI:030553, FOBI:050262, FOBI:050034
milk product (fermented) 10 3 0.0013793 0.0036454 FOBI:030553, FOBI:050262, FOBI:050034
soybean (whole) 23 4 0.0015173 0.0039398 FOBI:030695, FOBI:030343, FOBI:050262, FOBI:050034
asparagus (whole, raw) 3 2 0.0018478 0.0041359 FOBI:050262, FOBI:050034
butter 3 2 0.0018478 0.0041359 FOBI:050262, FOBI:050034
chicory (whole, raw) 3 2 0.0018478 0.0041359 FOBI:050262, FOBI:050034
eggplant (whole, raw) 3 2 0.0018478 0.0041359 FOBI:050262, FOBI:050034
Lean meat 3 2 0.0018478 0.0041359 FOBI:030689, FOBI:030707
melon (raw) 3 2 0.0018478 0.0041359 FOBI:050262, FOBI:050034
nectarine (whole, raw) 3 2 0.0018478 0.0041359 FOBI:050262, FOBI:050034
peanut butter 3 2 0.0018478 0.0041359 FOBI:050262, FOBI:050034
zucchini plant 3 2 0.0018478 0.0041359 FOBI:050262, FOBI:050034
cherry (whole, raw) 11 3 0.0018723 0.0041359 FOBI:030553, FOBI:030343, FOBI:050034
tea food product 12 3 0.0024644 0.0053638 FOBI:030553, FOBI:050262, FOBI:050034
flour 13 3 0.0031627 0.0065926 FOBI:030343, FOBI:050262, FOBI:050034
orange (whole, raw) 13 3 0.0031627 0.0065926 FOBI:030553, FOBI:050262, FOBI:050034
oregano (ground) 13 3 0.0031627 0.0065926 FOBI:030553, FOBI:030343, FOBI:050034
avocado (whole, raw) 4 2 0.0036459 0.0069179 FOBI:030553, FOBI:050034
beetroot 4 2 0.0036459 0.0069179 FOBI:030553, FOBI:050034
Dark yellow vegetables 4 2 0.0036459 0.0069179 FOBI:030553, FOBI:050034
fig (whole) 4 2 0.0036459 0.0069179 FOBI:030553, FOBI:050034
herb 4 2 0.0036459 0.0069179 FOBI:030553, FOBI:030343
prune food product 4 2 0.0036459 0.0069179 FOBI:030553, FOBI:050034
turnip (whole, raw) 4 2 0.0036459 0.0069179 FOBI:030553, FOBI:050034
barley grain (whole, raw) 5 2 0.0059948 0.0103166 FOBI:050262, FOBI:050034
pea (whole) 5 2 0.0059948 0.0103166 FOBI:030553, FOBI:050034
pomegranate (whole, raw) 5 2 0.0059948 0.0103166 FOBI:030553, FOBI:050034
raspberry (whole, raw) 5 2 0.0059948 0.0103166 FOBI:030553, FOBI:030343
white bread 5 2 0.0059948 0.0103166 FOBI:030343, FOBI:050034
White fish 5 2 0.0059948 0.0103166 FOBI:030343, FOBI:050034
white sugar 5 2 0.0059948 0.0103166 FOBI:030343, FOBI:050034
white wine 5 2 0.0059948 0.0103166 FOBI:030343, FOBI:050034
lemon (whole, raw) 17 3 0.0071392 0.0121449 FOBI:030553, FOBI:030343, FOBI:050034
celery stalk (raw) 6 2 0.0088712 0.0147521 FOBI:030553, FOBI:050034
garlic (whole, raw) 6 2 0.0088712 0.0147521 FOBI:050262, FOBI:050034

Network visualization of metaboliteList terms

Then, with the fobi_graph function we can visualize the metaboliteList terms with their corresponding FOBI relationships.

terms <- fobi_subset %>%
  pull(id_code)

# create the associated graph
fobitools::fobi_graph(terms = terms, 
                      get = "anc",
                      labels = TRUE,
                      legend = TRUE)

Session Information

sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] kableExtra_1.4.0 lubridate_1.9.5  forcats_1.0.1    stringr_1.6.0   
#>  [5] dplyr_1.2.1      purrr_1.2.2      readr_2.2.0      tidyr_1.3.2     
#>  [9] tibble_3.3.1     ggplot2_4.0.3    tidyverse_2.0.0  fobitools_1.21.0
#> [13] BiocStyle_2.41.0
#> 
#> loaded via a namespace (and not attached):
#>   [1] DBI_1.3.0              qdapRegex_0.7.10       gridExtra_2.3         
#>   [4] rlang_1.2.0            magrittr_2.0.5         e1071_1.7-17          
#>   [7] compiler_4.6.0         RSQLite_3.53.1         systemfonts_1.3.2     
#>  [10] vctrs_0.7.3            pkgconfig_2.0.3        crayon_1.5.3          
#>  [13] fastmap_1.2.0          labeling_0.4.3         ggraph_2.2.2          
#>  [16] rmarkdown_2.31         prodlim_2026.03.11     tzdb_0.5.0            
#>  [19] bit_4.6.0              xfun_0.57              cachem_1.1.0          
#>  [22] jsonlite_2.0.0         blob_1.3.0             tictoc_1.2.1          
#>  [25] BiocParallel_1.47.0    tweenr_2.0.3           syuzhet_1.0.7         
#>  [28] parallel_4.6.0         R6_2.6.1               bslib_0.11.0          
#>  [31] stringi_1.8.7          RColorBrewer_1.1-3     textclean_0.9.7       
#>  [34] parallelly_1.47.0      rpart_4.1.27           jquerylib_0.1.4       
#>  [37] Rcpp_1.1.1-1.1         knitr_1.51             future.apply_1.20.2   
#>  [40] clisymbols_1.2.0       timechange_0.4.0       Matrix_1.7-5          
#>  [43] splines_4.6.0          nnet_7.3-20            igraph_2.3.1          
#>  [46] tidyselect_1.2.1       rstudioapi_0.18.0      yaml_2.3.12           
#>  [49] viridis_0.6.5          codetools_0.2-20       listenv_0.10.1        
#>  [52] lattice_0.22-9         withr_3.0.2            S7_0.2.2              
#>  [55] evaluate_1.0.5         ontologyIndex_2.12     future_1.70.0         
#>  [58] survival_3.8-6         proxy_0.4-29           polyclip_1.10-7       
#>  [61] xml2_1.5.2             pillar_1.11.1          BiocManager_1.30.27   
#>  [64] lexicon_1.2.1          generics_0.1.4         vroom_1.7.1           
#>  [67] hms_1.1.4              scales_1.4.0           ff_4.5.2              
#>  [70] globals_0.19.1         xtable_1.8-8           class_7.3-23          
#>  [73] glue_1.8.1             RecordLinkage_0.4-12.6 maketools_1.3.2       
#>  [76] tools_4.6.0            sys_3.4.3              data.table_1.18.4     
#>  [79] fgsea_1.39.0           buildtools_1.0.0       graphlayouts_1.2.3    
#>  [82] fastmatch_1.1-8        tidygraph_1.3.1        cowplot_1.2.0         
#>  [85] grid_4.6.0             ipred_0.9-15           ggforce_0.5.0         
#>  [88] cli_3.6.6              evd_2.3-7.1            textshaping_1.0.5     
#>  [91] viridisLite_0.4.3      svglite_2.2.2          lava_1.9.1            
#>  [94] gtable_0.3.6           sass_0.4.10            digest_0.6.39         
#>  [97] ggrepel_0.9.8          farver_2.1.2           memoise_2.0.1         
#> [100] htmltools_0.5.9        lifecycle_1.0.5        bit64_4.8.2           
#> [103] MASS_7.3-65

References

Castellano-Escuder, Pol, Raúl González-Domı́nguez, David S Wishart, Cristina Andrés-Lacueva, and Alex Sánchez-Pla. 2020. “FOBI: An Ontology to Represent Food Intake Data and Associate It with Metabolomic Data.” Database 2020.