Simple food over representation analysis (ORA)

Compiled date: 2026-05-24

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 13 6 0.0000001 0.0000211 FOBI:030691, FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
green tea leaf (dry) 9 5 0.0000008 0.0000290 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
red tea 9 5 0.0000008 0.0000290 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
red velvet 9 5 0.0000008 0.0000290 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
cherry (whole, raw) 13 5 0.0000076 0.0001477 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
sweet potato vegetable food product 13 5 0.0000076 0.0001477 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
tomato (whole, raw) 14 5 0.0000116 0.0001477 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
almond (whole, raw) 7 4 0.0000128 0.0001477 FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
black pepper food product 7 4 0.0000128 0.0001477 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
black tea leaf (dry) 7 4 0.0000128 0.0001477 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:050034
broccoli floret (whole, raw) 7 4 0.0000128 0.0001477 FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
kale leaf (raw) 7 4 0.0000128 0.0001477 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:050034
lettuce vegetable food product 7 4 0.0000128 0.0001477 FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
orange (whole, raw) 16 5 0.0000248 0.0001803 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
apricot (whole, raw) 8 4 0.0000252 0.0001803 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
blackberry (whole, raw) 8 4 0.0000252 0.0001803 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
cauliflower (whole, raw) 8 4 0.0000252 0.0001803 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
pea (whole) 8 4 0.0000252 0.0001803 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
peach (whole, raw) 8 4 0.0000252 0.0001803 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
prune food product 8 4 0.0000252 0.0001803 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
tea food product 8 4 0.0000252 0.0001803 FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
wine (food product) 17 5 0.0000347 0.0002364 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
black currant (whole, raw) 9 4 0.0000448 0.0002801 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
pomegranate (whole, raw) 9 4 0.0000448 0.0002801 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
oregano (ground) 11 4 0.0001142 0.0006345 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:050034
plum (whole, raw) 11 4 0.0001142 0.0006345 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
strawberry (whole, raw) 11 4 0.0001142 0.0006345 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
bean (whole) 12 4 0.0001690 0.0008068 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
coffee (liquid drink) 12 4 0.0001690 0.0008068 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
pear (whole, raw) 12 4 0.0001690 0.0008068 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
chocolate 5 3 0.0001829 0.0008068 FOBI:030406, FOBI:030553, FOBI:030461
eggplant (whole, raw) 5 3 0.0001829 0.0008068 FOBI:030406, FOBI:030461, FOBI:050034
onion (whole, raw) 5 3 0.0001829 0.0008068 FOBI:030553, FOBI:030562, FOBI:050034
turnip (whole, raw) 5 3 0.0001829 0.0008068 FOBI:030406, FOBI:030553, FOBI:050034
apple juice 13 4 0.0002407 0.0009758 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461
lemon (whole, raw) 13 4 0.0002407 0.0009758 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:050034
orange juice 13 4 0.0002407 0.0009758 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461
grapefruit (whole, raw) 14 4 0.0003323 0.0010194 FOBI:030406, FOBI:030553, FOBI:050290, FOBI:050034
olive (whole, ripe) 14 4 0.0003323 0.0010194 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
avocado (whole, raw) 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
beetroot 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
bell pepper 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
cabbage (whole, raw) 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
chickpea (whole) 6 3 0.0003602 0.0010194 FOBI:030406, FOBI:030461, FOBI:050034
cucumber (whole, raw) 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
fig (whole) 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
hazelnut 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
hot pepper vegetable food product 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
kiwi 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
pineapple (whole, raw) 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
potato (whole, raw) 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
raisin (whole) 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
yellow bell pepper (whole, raw) 6 3 0.0003602 0.0010194 FOBI:030553, FOBI:030461, FOBI:050034
banana (whole, ripe) 7 3 0.0006207 0.0016627 FOBI:030553, FOBI:030461, FOBI:050034
raspberry (whole, raw) 7 3 0.0006207 0.0016627 FOBI:030406, FOBI:030553, FOBI:030461
white sugar 7 3 0.0006207 0.0016627 FOBI:030406, FOBI:030461, FOBI:050034
grape (whole, raw) 17 4 0.0007578 0.0019941 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
carrot root (whole, raw) 8 3 0.0009780 0.0024050 FOBI:030553, FOBI:030461, FOBI:050034
celery stalk (raw) 8 3 0.0009780 0.0024050 FOBI:030553, FOBI:030562, FOBI:050034
cocoa 8 3 0.0009780 0.0024050 FOBI:030406, FOBI:030461, FOBI:050034
peanut (whole, raw) 8 3 0.0009780 0.0024050 FOBI:030553, FOBI:030461, FOBI:050034
milk product (fermented) 9 3 0.0014446 0.0034950 FOBI:030553, FOBI:030461, FOBI:050034
bread food product 10 3 0.0020322 0.0047629 FOBI:030406, FOBI:030461, FOBI:050034
whole bread 10 3 0.0020322 0.0047629 FOBI:030406, FOBI:030461, FOBI:050034
lentil (whole) 3 2 0.0023703 0.0053871 FOBI:030406, FOBI:030461
soybean oil 3 2 0.0023703 0.0053871 FOBI:030406, FOBI:050034
beer 23 4 0.0025907 0.0058001 FOBI:030406, FOBI:030553, FOBI:030461, FOBI:050034
grain plant 11 3 0.0027514 0.0058958 FOBI:030406, FOBI:030461, FOBI:050034
grain product 11 3 0.0027514 0.0058958 FOBI:030406, FOBI:030461, FOBI:050034
wheat 11 3 0.0027514 0.0058958 FOBI:030406, FOBI:030461, FOBI:050034
soybean (whole) 25 4 0.0035972 0.0075998 FOBI:030704, FOBI:030406, FOBI:030698, FOBI:050034
Dark yellow vegetables 4 2 0.0046664 0.0084332 FOBI:030553, FOBI:050034
herb 4 2 0.0046664 0.0084332 FOBI:030406, FOBI:030553
mango (whole, raw) 4 2 0.0046664 0.0084332 FOBI:030461, FOBI:050034
melon (raw) 4 2 0.0046664 0.0084332 FOBI:030461, FOBI:050034
nectarine (whole, raw) 4 2 0.0046664 0.0084332 FOBI:030461, FOBI:050034
pumpkin (whole, raw) 4 2 0.0046664 0.0084332 FOBI:030553, FOBI:050034
radish (whole, raw) 4 2 0.0046664 0.0084332 FOBI:030553, FOBI:050034
rhubarb stalk (whole, raw) 4 2 0.0046664 0.0084332 FOBI:030461, FOBI:050034
sauerkraut 4 2 0.0046664 0.0084332 FOBI:030553, FOBI:050034
spinach (whole, raw) 4 2 0.0046664 0.0084332 FOBI:030553, FOBI:050034
watermelon (whole, raw) 4 2 0.0046664 0.0084332 FOBI:030553, FOBI:050034
zucchini plant 4 2 0.0046664 0.0084332 FOBI:030461, FOBI:050034
apple (whole) 48 5 0.0065697 0.0117316 FOBI:030406, FOBI:030553, FOBI:030562, FOBI:030461, FOBI:050034
blueberry (whole, raw) 5 2 0.0076553 0.0130489 FOBI:030406, FOBI:030461
white bread 5 2 0.0076553 0.0130489 FOBI:030406, FOBI:050034
White fish 5 2 0.0076553 0.0130489 FOBI:030406, FOBI:050034
white wine 5 2 0.0076553 0.0130489 FOBI:030406, 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_1.20.0
#> [13] BiocStyle_2.40.0
#> 
#> 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_1.46.0    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_1.38.0           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.