In pcastellanoescuder/fobibsa: Tools for Manipulating the FOBI Ontology

Compiled date: r Sys.Date()

Last edited: 2022-01-12

License: r packageDescription("fobitools")[["License"]]

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Installation

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

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("fobitools")

Load packages

library(fobitools)

We will also need some additional CRAN packages that will be very useful in this vignette.

library(tidyverse)
library(kableExtra)

Load food items from a food frequency questionnaire (FFQ) sample data

In nutritional studies, dietary data are usually collected by using different questionnaires such as FFQs (food frequency questionnaires) or 24h-DRs (24 hours dietary recall). Commonly, the text collected in these questionnaires require a manual preprocessing step before being analyzed.

This is an example of how an FFQ could look like in a common nutritional study.

load("data/sample_ffq.rda")

sample_ffq %>%
  dplyr::slice(1L:10L) %>%
  kbl(row.names = FALSE, booktabs = TRUE) %>%
  kable_styling(latex_options = c("striped"))

Automatic dietary text anotation

The fobitools::annotate_foods() function allows the automatic annotation of free nutritional text using the FOBI ontology [@castellano2020fobi]. This function provides users with a table of food IDs, food names, FOBI IDs and FOBI names of the FOBI terms that match the input text. The input should be structured as a two column data frame, indicating the food IDs (first column) and food names (second column). Note that food names can be provided both as words and complex strings.

This function includes a text mining algorithm composed of 5 sequential layers. In this process, singulars and plurals are analyzed, irrelevant words are removed, each string of the text input is tokenized and each word is analyzed independently, and the semantic similarity between input text and FOBI items is computed. Finally, this function also shows the percentage of the annotated input text.

annotated_text <- fobitools::annotate_foods(sample_ffq)

annotated_text$annotated %>%
  dplyr::slice(1L:10L) %>%
  kbl(row.names = FALSE, booktabs = TRUE) %>%
  kable_styling(latex_options = c("striped"))

The similarity argument

Additionally, the similarity argument indicates the semantic similarity cutoff used at the last layer of the text mining pipeline. It is a numeric value between 1 (exact match) and 0 (very poor match). Users can modify this value to obtain more or less accurated annotations. Authors do not recommend values below 0.85 (default).

annotated_text_95 <- fobitools::annotate_foods(sample_ffq, similarity = 0.95)

annotated_text_95$annotated %>%
  dplyr::slice(1L:10L) %>%
  kbl(row.names = FALSE, booktabs = TRUE) %>%
  kable_styling(latex_options = c("striped"))

See that by increasing the similarity value from 0.85 (default value) to 0.95 (a more accurate annotation), the percentage of annotated terms decreases from 89.57% to 86.5%. Let's check those food items annotated with similarity = 0.85 but not with similarity = 0.95.

annotated_text$annotated %>%
  filter(!FOOD_ID %in% annotated_text_95$annotated$FOOD_ID) %>%
  kbl(row.names = FALSE, booktabs = TRUE) %>%
  kable_styling(latex_options = c("striped"))

Network visualization of the annotated terms

Then, with the fobitools::fobi_graph() function we can visualize the annotated food terms with their corresponding FOBI relationships.

terms <- annotated_text$annotated %>%
  pull(FOBI_ID)

fobitools::fobi_graph(terms = terms,
                      get = NULL,
                      layout = "lgl",
                      labels = TRUE,
                      legend = TRUE,
                      labelsize = 6,
                      legendSize = 20)

How do I know which compounds are associated with my study food items?

Most likely we may be interested in knowing the food-related compounds in our study. Well, if so, once the foods are annotated we can obtain the metabolites associated with the annotated foods as follows:

inverse_rel <- fobitools::fobi %>%
  filter(id_BiomarkerOf %in% annotated_text$annotated$FOBI_ID) %>%
  dplyr::select(id_code, name, id_BiomarkerOf, FOBI) %>%
  dplyr::rename(METABOLITE_ID = 1, METABOLITE_NAME = 2, FOBI_ID = 3, METABOLITE_FOBI_ID = 4)

annotated_foods_and_metabolites <- left_join(annotated_text$annotated, inverse_rel, by = "FOBI_ID")

annotated_foods_and_metabolites %>%
  filter(!is.na(METABOLITE_ID)) %>%
  dplyr::slice(1L:10L) %>%
  kbl(row.names = FALSE, booktabs = TRUE) %>%
  kable_styling(latex_options = c("striped"))

Limitations

The FOBI ontology is currently in its first release version, so it does not yet include information on many metabolites, foods and food relationships. All future efforts will be directed at expanding this ontology, leading to a significant increase in the number of metabolites, foods (from FoodOn ontology [@dooley2018foodon]) and metabolite-food relationships. The fobitools package provides the methodology for easy use of the FOBI ontology regardless of the amount of information it contains. Therefore, future FOBI improvements will also have a direct impact on the fobitools package, increasing its utility and allowing to perform, among others, more accurate, complete and robust dietary text annotations.