Exposome Scores
4 August 2026
0.1 Load Data and Libraries
# Load Libraries
library(tidyverse)
library(tidyexposomics)We will start off with our example dataset pulled from the ISGlobal Exposome Data Challenge 2021 (Maitre et al., 2022).
# Load example data
data("tidyexposomics_example")
# Create exposomic set object
expom <- create_exposomicset(
codebook = tidyexposomics_example$annotated_cb,
exposure = tidyexposomics_example$meta,
omics = list(
"Gene Expression" = tidyexposomics_example$exp_filt,
"Methylation" = tidyexposomics_example$methyl_filt
),
row_data = list(
"Gene Expression" = tidyexposomics_example$exp_fdata,
"Methylation" = tidyexposomics_example$methyl_fdata
)
)## Ensuring all omics datasets are matrices with column names.## Creating SummarizedExperiment objects.## Creating MultiAssayExperiment object.## MultiAssayExperiment created successfully.We will focus on a few exposure variable categories.
# Grab exposure variables
exp_vars <- tidyexposomics_example$annotated_cb |>
filter(category %in% c(
"aerosol",
"main group molecular entity",
"polyatomic entity"
)) |>
pull(variable) |>
as.character()1 Quality Control
As in the main vignette, we will impute exposure data using missforest.
# Impute missing values
expom <- run_impute_missing(
exposomicset = expom,
exposure_impute_method = "missforest",
exposure_cols = exp_vars
)## Imputing exposure data using method: missforestAnd we will transform our exposure data to ensure it is more normally distributed using the boxcox_best method.
# Transform variables
expom <- transform_exposure(
exposomicset = expom,
transform_method = "boxcox_best",
exposure_cols = exp_vars
)## Applying the boxcox_best transformation.1.1 Exposome Scores
We can calculate exposome scores, which are a summary measure of exposure. The run_exposome_score function is used to calculate the exposome score. The exposure_cols argument is used to set the columns to use for the exposome score. The score_type argument is used to set the type of score to calculate. Here we could use:
median: Calculates the median of the exposure variables.mean: Calculates the mean of the exposure variables.sum: Calculates the sum of the exposure variables.pca: Calculates the first principal component of the exposure variables.irt: Uses Item Response Theory to calculate the exposome score.quantile: Calculates the quantile of the exposure variables.var: Calculates the variance of the exposure variables.
The score_column_name argument is used to set the name of the column to store the exposome score in. Here we will define a score for aerosols using a variety of different methods and demonstrate their use in association with asthma status.
# determine which aerosol variables to use
aerosols <- c("h_pm25_ratio_preg_None", "h_pm10_ratio_preg_None")
# Create exposome scores
expom <- expom |>
run_exposome_score(
exposure_cols = aerosols,
score_type = "median",
score_column_name = "exposome_median_score"
) |>
run_exposome_score(
exposure_cols = aerosols,
score_type = "pca",
score_column_name = "exposome_pca_score"
) |>
run_exposome_score(
exposure_cols = aerosols,
score_type = "quantile",
score_column_name = "exposome_quantile_score"
) |>
run_exposome_score(
exposure_cols = aerosols,
score_type = "var",
score_column_name = "exposome_var_score"
)## Extracting exposure data...
## Extracting exposure data...
## Extracting exposure data...
## Extracting exposure data...## Calculating median exposure scores...## Calculating PCA exposure scores...## Calculating quantile exposure scores...## Calculating variance exposure scores...We can then associate these exposome scores with asthma status using the run_association function, just like we did before. However, this time we specify our feature_set to be the exposome scores we just calculated.
# Associate exposome scores with outcome
expom <- run_association(
exposomicset = expom,
outcome = "hs_asthma",
source = "exposures",
feature_set = c(
"exposome_median_score",
"exposome_pca_score",
"exposome_quantile_score",
"exposome_var_score"
),
action = "add",
family = "binomial"
)## Running GLMs.# Plot the association forest plot
plot_association(
exposomicset = expom,
source = "exposures",
terms = c(
"exposome_median_score",
"exposome_pca_score",
"exposome_quantile_score",
"exposome_var_score"
),
filter_col = "p.value",
filter_thresh = 0.05,
r2_col = "r2"
)
Figure 1: Associations of aerosol exposome scores with asthma status
The variance-based score has the strongest association with asthma status.
1.2 Session Info
See Session Info
sessionInfo()
## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] tidyexposomics_1.1.2 MultiAssayExperiment_1.39.0
## [3] SummarizedExperiment_1.43.0 Biobase_2.73.2
## [5] GenomicRanges_1.65.1 Seqinfo_1.3.0
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