Codebook Setup

Before starting an exposomics data analysis we recommend having a codebook, with information on your exposure variables. Some suggestions:

• Variable Name: The name of the variable in the data set.

• Variable Description: A concise description of what the variable measures, including units (e.g., “urinary bisphenol A (ng/mL)”).

• Variable Type: The type of variable, such as continuous, categorical, or binary.

• Variable Period: The period of time over which the variable was measured, such as “lifetime”, “year”, “month”, or “day”.

• Variable Location: The location where the variable was measured, such as “home”, “work”, “school”, or “geospatial code”.

• Variable Ontology: The ontology term associated with the variable.

Ontology Choices

Variables captured in the codebook should be annotated with ontology terms to provide a standardized vocabulary for the variables. We recommend using the following ontologies for exposure and outcome variables:

• Environment Exposure Ontology to annotate your exposure variables.

• Human Phenotype Ontology to annotate your outcome variables and phenotypic data.

• Chemical Entities of Biological Interest to annotate your chemical exposure variables.

Why annotate with ontologies?

• Interpretability: Ontology labels clarify ambiguous or inconsistently named variables.

• Harmonization: You can compare and combine variables across datasets when they map to the same term.

• Grouping: Ontologies allow you to collapse fine-grained exposures into broader categories.

• Integration: Many public tools, knowledge graphs, and repositories are ontology-aware. This can make your results more interoperable and reusable.

Ontology Annotation App

To help annotate exposure variables, we provide a lightweight shiny app:

# Launch the shiny app to annotate exposure variables
shiny::runApp(build_ont_annot_app())

To use the app:

• Click Browse to select your exposure metadata file.

• Then you can click the variable you’d like to link to an annotation term and search in the Choose Ontology Term dropdown.

• After you select a term, you will see a short description of the term.

• After you are done, click Apply Annotate to save the annotation.

• Now you can group exposures into larger categories by selecting each line and then choose your ontology and root depth level (where a lower number means a more general term).

• Then you can click Apply Categorization to apply the selected categorization to the selected rows.

• If the ontology has nothing to do with your variable, you may manually enter a category in the Category column. This will change the Category Source to manual and will not be linked to the ontology.

• Once you have annotated all your variables click Download Annotated CSV to save the annotated metadata file.

Screenshot of the ontology annotation app. The sidebar has an upload button to load your exposure metadata file. The main panel displays the uploaded exposure metadata where users can select variables to annotate and categorize. The sidebar also contains buttons to apply annotations/categorizations and download the annotated file.

Missingness

Oftentimes when collecting data, there are missing values. Let’s use the plot_missing function to determine where our missing values are:

# Plot the missingness summary
plot_missing(
    exposomicset = expom,
    plot_type = "summary",
    threshold = 0
)

Count of features with missing data above a 0% missingness threshold by data layer. Exposure data have variables with missingness.

Here we see that there are 4 variables in the exposure data that are missing data. Let’s take a look at them:

# Plot missing variables withing exposure group
plot_missing(
    exposomicset = expom,
    plot_type = "lollipop",
    threshold = 0,
    layers = "Exposure"
)

Percent missingness per exposure variable. Parity, h_parity_None, shows the highest missingness.

Here we see that one variable, h_parity_None, has about 4% missing values. We can apply a missingness filter using the filter_missing function. However, given that this level of missingness is quite low, we will not be applying a missingness filter and instead impute the missing data.

Imputation

The run_impute_missing function is used to impute missing values. Here we can specify the imputation method for exposure and omics data separately.

The exposure_impute_method argument is used to set the imputation method for exposure data, and the omics_impute_method argument is used to set the imputation method for omics data. The omics_to_impute argument is used to specify which omics data to impute. Here we will impute the exposure data given using the missforest method, but other options for imputation methods include:

• median: Imputes missing values with the median of the variable.

• mean: Imputes missing values with the mean of the variable.

• knn: Uses k-nearest neighbors to impute missing values.

• mice: Uses the Multivariate Imputation by Chained Equations (MICE) method to impute missing values.

• missforest: Uses the MissForest method to impute missing values.

• lod_sqrt2: Imputes missing values using the square root of the lower limit of detection (LOD) for each variable. This is useful for variables that have a lower limit of detection, such as chemical exposures.

# Impute missing values
expom <- run_impute_missing(
    exposomicset = expom,
    exposure_impute_method = "missforest",
    exposure_cols = exp_vars
)

## Imputing exposure data using method: missforest

Filtering Omics Features

We can filter omics features based on variance or expression levels. The filter_omics function is used to filter omics features. The method argument is used to set the method for filtering. Here we can use either:

• Variance: Filters features based on variance. We recommend this for omics based on continuous measurements, such as log-transformed counts, M-values, protein intensities, or metabolite concentrations.

• Expression: Filters features based on expression levels. We recommend this for omics where many values may be near-zero or zero, such as RNA-seq data.

The assays argument is used to specify which omics data to filter. The assay_name argument is used to specify which assay to filter. The min_var, min_value, and min_prop arguments are used to set the minimum variance, minimum expression value, and minimum proportion of samples exceeding the minimum value, respectively.

# filter omics layers by variance and expression
# Methylation filtering
expom <- filter_omics(
    exposomicset = expom,
    method = "variance",
    assays = "Methylation",
    assay_name = 1,
    min_var = 0.05
)

## Filtering assay: Methylation

## Filtered 223 of 500 features from 'Methylation' using method 'variance'

# Gene expression filtering
expom <- filter_omics(
    exposomicset = expom,
    method = "expression",
    assays = "Gene Expression",
    assay_name = 1,
    min_value = 1,
    min_prop = 0.3
)

## Filtering assay: Gene Expression

## Filtered 29 of 500 features from 'Gene Expression' using method 'expression'

Normality Check

When determining variable associations, it is important to check the normality of the data. The run_normality_check function is used to check the normality of the data.

# Check variable normality
expom <- run_normality_check(
    exposomicset = expom,
    action = "add"
)

## Checking Normality Using Shapiro-Wilk Test

## 9 Exposure Variables are Normally Distributed

## 6 Exposure Variables are NOT Normally Distributed

The transform_exposure function is used to transform the data to make it more normal. Here the transform_method is set to boxcox_best as it will automatically select the best transformation method based on the data. The transform_method can be manually set to log2, sqrt, or x_1_3 as well. We specify the exposure_cols argument to set the columns to transform.

# Transform variables
expom <- transform_exposure(
    exposomicset = expom,
    transform_method = "boxcox_best",
    exposure_cols = exp_vars
)

## Applying the boxcox_best transformation.

To check the normality of the exposure data, we can use the plot_normality_summary function. This function plots the normality of the data before and after transformation. The transformed argument is set to TRUE to plot the normality status of the transformed data.

# Examine normality summary
plot_normality_summary(
    exposomicset = expom,
    transformed = TRUE
)

Normality status of numeric exposure variables after Box-Cox transformation.

Principal Component Analysis

To identify the variability of the data, we can perform a principal component analysis (PCA). The run_pca performs a joint PCA across all numeric exposures and omic assays after standardization, identifying shared axes of variation across layers. The resulting PCs in colData() reflect integrated sample-level variance across all data types, and outliers are defined in that joint multi-omics PC space.

Here we specify that we would like to log-transform the exposure and omics data before performing PCA using the log_trans_exp and the log_trans_omics arguments, respectively. We automatically identify sample outliers based on the Mahalanobis distance, a measure of the distance between a point and a distribution.

# Perform principal component analysis
expom <- run_pca(
    exposomicset = expom,
    log_trans_exp = TRUE,
    log_trans_omics = TRUE,
    action = "add"
)

## Identifying common samples.

## Subsetting exposure data.

## Subsetting omics data.

## Performing PCA on Feature Space.

## Performing PCA on Sample Space.

## Outliers detected: s1231

# Plot the PCA plot of sample and feature space
plot_pca(exposomicset = expom)

## Warning in geom_bar(stat = "identity", fill = barfill, color = barcolor, : Ignoring empty aesthetic: `width`.
## Ignoring empty aesthetic: `width`.

PCA of sample and feature space with sample outlier detection.

Here we see one sample outlier, and that most variation is captured in the first two principal components for both features and samples. We can filter out the outlier using the filter_sample_outliers function.

# Filter out sample outliers
expom <- filter_sample_outliers(
    exposomicset = expom,
    outliers = c("s1231")
)

## Removing outliers: s1231

To understand the relationship between the principal components and exposures we can correlate them using the run_correlation function. Here we specify that the feature_type is pcs for principal components, specify a set of exposure variables, exp_vars, and the number of principal components, n_pcs. We set correlation_cutoff to 0 and pval_cutoff to 1 to initially include all correlations.

# Run the correlation analysis
expom <- run_correlation(
    exposomicset = expom,
    feature_type = "pcs",
    exposure_cols = exp_vars,
    n_pcs = 20,
    action = "add",
    correlation_cutoff = 0,
    pval_cutoff = 1
)

We can visualize these correlations with the plot_correlation_tile function. We specify we are plotting the feature_type of pcs to grab the principal component correlation results. We then set the significance threshold to 0.05 with the pval_cutoff argument.

# Plot the correlation tile plot
plot_correlation_tile(
    exposomicset = expom,
    feature_type = "pcs",
    pval_cutoff = 0.05
)

Correlation heatmap of exposures versus principal components. Child lead levels (hs_pb_c_Log2) and maternal BPA levels (hs_bpa_madj_Log2) are associated with the most principal components.

Exposure Summary

We can summarize the exposure data using the run_summarize_exposures function. This function calculates summary statistics for each exposure variable, including the number of values, number of missing values, minimum, maximum, range, sum, median, mean, standard error, and confidence intervals. The exposure_cols argument determines which variables to include in the summary.

# Summarize exposure data
run_summarize_exposures(
    exposomicset = expom,
    action = "get",
    exposure_cols = exp_vars
) |>
    head()

## # A tibble: 6 × 27
##   variable   n_values  n_na   min   max range    sum median  mean    se ci_lower
##   <chr>         <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>    <dbl>
## 1 h_pm10_ra…       47     0 16.6  25.8   9.25 989.    21.2  21.0   0.32    20.4 
## 2 h_pm25_ra…       47     0 10.6  18.2   7.55 697.    14.6  14.8   0.25    14.3 
## 3 hs_bpa_ma…       47     0  0     2.31  2.31  71.3    1.58  1.52  0.06     1.41
## 4 hs_mibp_c…       47     0  0.1   0.22  0.11   7.65   0.17  0.16  0        0.16
## 5 hs_pb_c_L…       47     0  1.44  5     3.56 156.     3.31  3.31  0.11     3.1 
## 6 hs_pfhxs_…       47     0  0     2.14  2.13  65.7    1.48  1.4   0.07     1.25
## # ℹ 16 more variables: ci_upper <dbl>, variance <dbl>, sd <dbl>,
## #   coef_var <dbl>, period <chr>, location <chr>, description <chr>,
## #   var_type <chr>, transformation <chr>, selected_ontology_label <chr>,
## #   selected_ontology_id <chr>, root_id <chr>, root_label <chr>,
## #   category <chr>, category_source <chr>, transformation_applied <chr>

Exposure Visualization

To visualize our exposure data, we can use the plot_exposures function. This function allows us to plot the exposure data in a variety of ways. Here we will plot the exposure data using a boxplot. The exposure_cat argument is used to set the exposure category to plot. Additionally, we could specify exposure_cols to only plot certain exposures. The plot_type argument is used to set the type of plot to create. Here we use a boxplot, but we could also use a ridge plot.

# Plot aerosol exposure distributions by sex
plot_exposures(
    exposomicset = expom,
    group_by = "e3_sex_None",
    exposure_cat = "aerosol",
    plot_type = "boxplot",
    ylab = "Values",
    title = "Aerosol Exposure by Sex"
)

Distribution of aerosol exposures by sex.

Here we do not see any significant differences in aerosol exposure between males and females.

Sample Clustering

The run_cluster_samples function is used to cluster samples based on the exposure data, clustering approaches are available by setting the clustering_approach argument. Here we use the dynamic approach, which uses a dynamic tree cut method to identify clusters. Other options are:

• gap: Gap statistic method (default); estimates optimal k by comparing within-cluster dispersion to that of reference data.

• diana: Divisive hierarchical clustering (DIANA); chooses k based on the largest drop in dendrogram height.

• elbow: Elbow method; detects the point of maximum curvature in within-cluster sum of squares (WSS) to determine k.

• dynamic: Dynamic tree cut; adaptively detects clusters from a dendrogram structure without needing to predefine k.

• density: Density-based clustering (via densityClust); identifies clusters based on local density peaks in distance space.

# Sample clustering
expom <- run_cluster_samples(
    exposomicset = expom,
    exposure_cols = exp_vars,
    clustering_approach = "dynamic",
    action = "add"
)

## Starting clustering analysis...

##  ..cutHeight not given, setting it to 40.7  ===>  99% of the (truncated) height range in dendro.
##  ..done.

## Optimal number of clusters for samples: 2

We plot the sample clusters using the plot_sample_clusters function. This function plots z-scored values of the exposure data for each sample, colored by the cluster assignment. The exposure_cols argument is used to set the columns to plot.

# Plot the sample clusters
plot_sample_clusters(
    exposomicset = expom,
    exposure_cols = exp_vars
)

## tidyHeatmap says: If you use tidyHeatmap for scientific research, please cite: Mangiola, S. and Papenfuss, A.T., 2020. 'tidyHeatmap: an R package for modular heatmap production based on tidy principles.' Journal of Open Source Software. doi:10.21105/joss.02472.
## This message is displayed once per session.

## Warning: `when()` was deprecated in purrr 1.0.0.
## ℹ Please use `if` instead.
## ℹ The deprecated feature was likely used in the tidyHeatmap package.
##   Please report the issue at
##   <https://github.com/stemangiola/tidyHeatmap/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Sample clustering heatmap using exposure profiles (z-scored). Clusters appear mostly driven by aerosol exposure during pregnancy.

Here we see two clusters, largely driven by particulate matter/aerosol exposure during pregnancy (h_pm25_ratio_preg_None and h_pm10_ratio_preg_None).

Exposure Correlations

The run_correlation function identifies correlations between exposure variables. We set feature_type to exposures to focus on exposure variables and use a correlation cutoff of 0.3 to filter for meaningful associations. This cutoff can be adjusted based on your data and analysis needs.

# Run correlation analysis
expom <- run_correlation(
    exposomicset = expom,
    feature_type = "exposures",
    action = "add",
    exposure_cols = exp_vars,
    correlation_cutoff = 0.3
)

To visualize the exposure correlations, we can use the plot_circos_correlation function. Here we will plot the circos plot. This function creates a circular plot of the exposure correlations. The correlation_cutoff argument is used to set the minimum correlation score for the association. Here we use a cutoff of 0.3.

# Plot exposure correlation circos plot
plot_circos_correlation(
    exposomicset = expom,
    feature_type = "exposures",
    corr_threshold = 0.3,
    exposure_cols = exp_vars
)

Circos view of exposure-exposure correlations (threshold 0.3).

Exposure-wide association (ExWAS)

The run_association function performs an ExWAS analysis to identify associations between exposures and outcomes. We specify the data source, outcome variable, feature set, and covariates for the analysis. Since we have a binary outcome, we set the model family to binomial.

# Perform ExWAS
expom <- run_association(
    exposomicset = expom,
    source = "exposures",
    outcome = "hs_asthma",
    feature_set = exp_vars,
    action = "add",
    family = "binomial"
)

## Running GLMs.

To visualize the results of the ExWAS analysis, we can use the plot_association function, which will plot results for the the specified features. The terms argument is used to set the features to plot. The filter_thresh argument is used to set the threshold for filtering the results. The filter_col argument is used to set the column to filter on. Here we use p.value to filter on the p-value of the association. We can also include the R^2 or adjusted R^2 (if covariates are included) using the r2_col argument.

# Plot the association forest plot
plot_association(
    exposomicset = expom,
    source = "exposures",
    terms = exp_vars,
    filter_thresh = 0.05,
    filter_col = "p.value",
    r2_col = "r2"
)

ExWAS associations of exposures with asthma status. No exposures are significantly associated (P < 0.05) with asthma status.

Here we see that no exposure variables are significantly associated with our asthma status. Although we do see that confidence interval for child Mono-iso-butyl phthalate (MiBP) levels (hs_mibp_cadj_Log2) does not cross 0, indicating a negative, albeit not significant (P < 0.05) association.

We can also associate our omics features with an outcome of interest using the run_association function. Here we specify an additional argument, top_n, which is used to set the top number of high variance omics features to include per omics layer.

# Perform ExWAS
expom <- run_association(
    exposomicset = expom,
    outcome = "hs_asthma",
    source = "omics",
    top_n = 500,
    action = "add",
    family = "binomial"
)

## Log2-Transforming each assay in MultiAssayExperiment.

## Scaling each assay in MultiAssayExperiment.

## Running GLMs.

Now we can visualize these results with a manhattan plot.

# Plot the manhattan plot
plot_manhattan(
    exposomicset = expom,
    min_per_cat = 0,
    feature_col = "feature_clean",
    vars_to_label = c(
        "TC19001180.hg.1",
        "TC01000565.hg.1",
        "cg01937701",
        "hs_mibp_cadj_Log2"
    ),
    panel_sizes = c(1, 3, 1, 3, 1, 1, 1),
    facet_angle = 0
)

Manhattan plot of omics-wide associations with asthma status.

Exposome Scores

We can also 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 = "irt",
        score_column_name = "exposome_irt_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...
## Extracting exposure data...

## Calculating median exposure scores...

## Calculating PCA exposure scores...

## Calculating IRT exposure scores...

## Registered S3 method overwritten by 'vegan':
##   method     from      
##   rev.hclust dendextend

## Warning: EM cycles terminated after 500 iterations.

## 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_irt_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_irt_score",
        "exposome_quantile_score",
        "exposome_var_score"
    ),
    filter_col = "p.value",
    filter_thresh = 0.05,
    r2_col = "r2"
)

Associations of aerosol exposome scores with asthma status. The variance-based score has the strongest association with asthma status.

Differential Abundance

We provide functionality to test for differentially abundant features associated with an outcome across multiple omics layers. This is done using the run_differential_abundance function, which fits a model defined by the user (using the formula argument) and supports several methods. Here we apply the limma_trend method, a widely used approach for analyzing omics data. Users can also specify how features are scaled (e.g. none, quantile, TMM) before fitting.

# Run differential abundance analysis
expom <- run_differential_abundance(
    exposomicset = expom,
    formula = ~hs_asthma,
    method = "limma_trend",
    scaling_method = "none",
    action = "add"
)

## Running differential abundance testing.

## Processing assay: Gene Expression

## Processing assay: Methylation

## Differential abundance testing completed.

We can summarize the results of the differential abundance analysis with a volcano plot, which highlights features with a high log fold change and that are statistically significant. The plot_volcano function generates this visualization, with options to set thresholds for p-values and log fold changes, and to label a subset of top-ranked features. In this example, we use the feature_clean column to display interpretable feature names.

Note: we set the pval_col to P.Value for the purposes of this example, but we recommend keeping the default of adj.P.Val to use the adjusted p-values.

# Plot the volcano plot
plot_volcano(
    exposomicset = expom,
    top_n_label = 3,
    feature_col = "feature_clean",
    logFC_thresh = log2(1),
    pval_thresh = 0.05,
    pval_col = "P.Value",
    logFC_col = "logFC",
    nrow = 1
)

Volcano plot of differentially abundant features across omics layers.

Sensitivity Analysis

Depending on pre-processing steps, the results of the differential abundance analysis may vary. The sensitivity_analysis function is used to perform a sensitivity analysis to determine the robustness of the results. Here we determine if a feature is still differentially abundant if different minimum values, proportions, scaling methods are used, the inclusion of covariates, and after bootstrapping. We then define a stability score based on the number of times a feature is found to be differentially abundant under different conditions as well as the consistency of the effect size:

Where:

\[ Stability\ Score = \frac{\sum_i{(p_i < \alpha)}}{N} * \frac{1}{1 + \frac{\sigma_{\beta}}{\mu_{|\beta|}}}\]

Where:

• \(p_i\) is the p-value for the \(i^{th}\) test

• \(\alpha\) is the significance threshold

• \(\beta\) is the log fold change

• \(N\) is the number of tests

• \(\sigma_{\beta}\) is the standard deviation of the effect size estimates

• \(\mu_{|\beta|}\) is the mean of the absolute value of the effect size estimates.

The first term captures the proportion of tests that are significant, while the second term captures the consistency of the effect size estimates. The stability score ranges from 0 to 1.

A stability score of 1 indicates that the feature is always found to be differentially abundant, while a stability score of 0 indicates that the feature is never found to be differentially abundant. Besides these, we provide other score metrics as well:

• presence_rate: Proportion of runs in which the feature’s p-value is below the specified threshold (selection frequency).

• effect_consistency: Inverse of the coefficient of variation of log fold-changes; measures effect size stability across runs.

• stability_score: Hybrid score combining presence_rate and effect_consistency, capturing reproducibility and signal strength.

• mean_log_p: Average of negative log-transformed p-values; represents overall statistical signal strength.

• logp_weighted_score: Product of mean_log_p and effect_consistency; highlights consistently strong features.

• sd_logFC: Standard deviation of log fold-change estimates; quantifies variability of effect sizes.

• iqr_logFC: Interquartile range of log fold-changes; provides a robust measure of effect size spread.

• cv_logFC: Coefficient of variation of log fold-changes; reflects relative variability of effect size.

• sign_flip_freq: Proportion of runs where the sign of the effect size differs from the overall average direction.

• sd_log_p: Standard deviation of log-transformed p-values; indicates variability in statistical signal.

# Perform Sensitivity Analysis
expom <- run_sensitivity_analysis(
    exposomicset = expom,
    base_formula = ~hs_asthma,
    methods = c("limma_trend"),
    scaling_methods = c("none"),
    pval_col = "P.Value",
    logfc_col = "logFC",
    logFC_threshold = log2(1),
    pval_threshold = 0.05,
    stability_metric = "stability_score",
    bootstrap_n = 10,
    action = "add"
)

## Running bootstrap iteration 1 of 10

## Running bootstrap iteration 2 of 10

## Running bootstrap iteration 3 of 10

## Running bootstrap iteration 4 of 10

## Running bootstrap iteration 5 of 10

## Running bootstrap iteration 6 of 10

## Running bootstrap iteration 7 of 10

## Running bootstrap iteration 8 of 10

## Running bootstrap iteration 9 of 10

## Running bootstrap iteration 10 of 10

## Computing feature stability across sensitivity conditions.

## Feature stability analysis completed.

## Number Of Features Above Threshold Of 0.32:

## ----------------------------------------

## Gene Expression: 10/471

## Methylation: 7/277

Now we can plot the distribution of stability scores and visualize how many features have scores greater than a threshold of 0.34.

# Plot distriubtion of stability scores
plot_sensitivity_summary(
    exposomicset = expom,
    stability_score_thresh = 0.34,
    stability_metric = "stability_score"
)

## Number of Features with stability_score > 0.34:

## Gene Expression: 9 / 471

## Methylation: 6 / 277

## Picking joint bandwidth of 0.0231

Sensitivity summary highlighting robust features by stability score.

Factor Analysis

These methods are designed to identify factors that we can then associate with an outcome variable. Here we will use the run_association function to identify factors that are associated with asthma status.

# Identify factors that are associated with the outcome
expom <- run_association(
    exposomicset = expom,
    source = "factors",
    outcome = "hs_asthma",
    feature_set = exp_vars,
    action = "add",
    family = "binomial"
)

## Running GLMs.

Now let’s see if any of our factors are associated with asthma status.

# Plot the forest plot
plot_association(
    exposomicset = expom,
    source = "factors",
    filter_col = "p_adjust",
    filter_thresh = 0.05,
    r2_col = "r2"
)

Associations of latent factors with asthma after covariate adjustment.

We see that several factors are associated with our outcome. Factors have loading scores which indicate the strength of the association between the factor and the features. Here we can extract the top features, those in the 70th percentile, per omic, associated with our factors of interest. We set the pval_col and pval_thresh to filter our association results to grab the factors that pass our thresholds. However, we could specify specific factors with the factors argument too.

# Extract top features that contribute to a factor
expom <- extract_top_factor_features(
    exposomicset = expom,
    method = "percentile",
    pval_col = "p_adjust",
    pval_thresh = 0.05,
    percentile = 0.7,
    action = "add"
)

## Extracting top contributing features for specified factors.

## Using DIABLO loadings.

## Applying percentile-based filtering (>70%).

## Selected 589 features contributing to specified factors.

We can visualize the top features associated with each factor using the plot_top_factor_features function. The top_n argument is used to set the number of top features to plot. The factors argument is used to set the factors to plot.

# Plot the top factor features
plot_top_factor_features(
    exposomicset = expom,
    top_n = 5,
    feature_col = "feature_clean"
)

Top-loading features per factor and omics layer.

Exposure-Omics Association

Now we have the option to correlate either the top factor features, differentially abundant features, or user-specified omics features (by using a variable map, a data frame with two columns, exp_name for the name of the omics assay, and variable for the name of the molecular feature) with exposures.

Here we will correlate features driving multiple latent factors with exposures. To grab these features, we will grab them from the metadata in our MultiAssayExperiment object. The correlation_cutoff is used to set the minimum correlation score, while the pval_cutoff is used to set the maximum p-value for the association.

# Grab top common factor features and ensure
# feature is renamed to variable for the variable_map
top_factor_features <- expom |>
    extract_results(result = "multiomics_integration") |>
    pluck("top_factor_features") |>
    dplyr::select(variable = feature, exp_name)

# Correlate top factor features with exposures
# Perform correlation analysis between
# factor features and exposures
expom <- run_correlation(
    exposomicset = expom,
    feature_type = "omics",
    variable_map = top_factor_features,
    exposure_cols = exp_vars,
    action = "add",
    correlation_cutoff = 0.3,
    pval_cutoff = 0.05,
    cor_pval_column = "p.value"
)

## Log2-Transforming each assay in MultiAssayExperiment.

# Perform correlation analysis between factor features
expom <- run_correlation(
    exposomicset = expom,
    feature_type = "omics",
    variable_map = top_factor_features,
    exposure_cols = exp_vars,
    feature_cors = TRUE,
    action = "add",
    correlation_cutoff = 0.3,
    pval_cutoff = 0.05,
    cor_pval_column = "p.value"
)

## Log2-Transforming each assay in MultiAssayExperiment.

We can plot the results of the exposure-omics association analysis using the plot_correlation_summary function. Here we set the mode to summary, which will plot the number of associations per exposure and feature type. The feature_type argument is used to set the type of features to plot. Here we use omics, which are the top factor features.

# Plot correlation summary
plot_correlation_summary(
    exposomicset = expom,
    mode = "summary",
    feature_type = "omics"
)

Summary of exposure-omics and feature-feature associations.

Here we note that gene expression features have a greater number of associations with exposures. Among exposures, we see that aerosols have the greatest number of associations with features, which may reflect the larger number of aerosol variables.

It may also be useful to identify which exposures are correlated with similar molecular features. We can do this with the plot_circos_correlation function. This function will plot a circos plot of the exposures and their shared features. The feature_type argument is used to set the correlation results to use for the analysis. Here we use the omics feature set, which plots the feature-exposure correlation information. The cutoff argument is used to set the minimum number of shared features to plot. Here we set the shared_cutoff to 1, which means that only exposures that share at least one feature will be plotted.

# Plot the circos plot of shared correlated features
plot_circos_correlation(
    exposomicset = expom,
    feature_type = "omics",
    shared_cutoff = 1,
    midpoint = 1.5
)

Circos of exposures sharing correlated molecular features.

Here we see that particulate matter exposures are correlated with many of the same features.

Network Analysis

The run_create_network function is used to create a network of exposures and omics features. The correlation results are used to create the network. The feature_type argument is used to set the type of features to use for the network. We could choose any of the following:

• degs: Differentially abundant features correlated with exposures.

• factors: Factor features correlated with exposures.

• omics: User specified omics features correlated with exposures.

Here we create networks based on two correlation tables generated above:

• omics: Correlations between omics and exposure variables.

• omics_feature_cor: Correlation just between the omics features.

This allows us to quantify a bipartite graph between exposures and omics features and a graph just between the omics features.

# Create omics within feature correlation network
expom <- run_create_network(
    exposomicset = expom,
    feature_type = "omics_feature_cor",
    action = "add"
)

## Creating network from correlation results.

## Network added to metadata as: network_omics_feature_cor

# Create omics-exposure correlation network
expom <- run_create_network(
    exposomicset = expom,
    feature_type = "omics",
    action = "add"
)

## Creating network from correlation results.

## Network added to metadata as: network_omics

To plot the network we can use the plot_network function. The network argument is used to set the type of network to plot. The top_n_nodes argument is used to set the number of nodes to plot. The node_color_var argument is used to set the variable to color the nodes by. The label argument is used to set whether or not to label the nodes. We can label the top n nodes, where the default is 5 nodes based on centrality. Additionally, we can choose to label certain nodes using the nodes_to_label argument. We can also include the network statistics using the include_stats argument.

# Plot the exposure-omics network
# Setting the seed so that the graph layout is consistent 
# given that layouts can have random starting points
set.seed(42)
plot_network(
    exposomicset = expom,
    network = "omics",
    top_n_nodes = 50,
    include_stats = TRUE,
    cor_thresh = 0.2,
    node_color_var = "group",
    label = TRUE,
    label_top_n = 5
)

## Extracting graph.

Bipartite network of exposures and correlated omics features where node labels reflect most central nodes.

Here we note that the network is highly connected, with particulate matter and methyl paraben exposures having the most connections to omics features. To briefly explain the metrics mentioned:

• Nodes: Number of nodes included in the graph.

• Edges: Number of connections between nodes in the graph.

• Components: Number of disconnected subgraphs.

• Diameter: The longest shortest path between any two nodes.

• Mean Distance: The average shortest path between any two nodes.

• Modularity: A measure of the division in the graph, where higher modularity indicates that nodes within the same community are more densely connected compared to nodes in different communities.

Exposure-Omics Impact Network Analysis

The bipartite graph above describes how exposures are associated with omics features. However, this does not describe if exposures are associated with features that are more central in the feature-only graph (i.e. are exposures associated with features that are more central or peripheral to the graph?). To answer this question, we can use the run_exposure_impact function to calculate centrality metrics for the omics features associated with a given exposure. Centrality is computed on the feature-only graph. Centrality metrics are used to identify features that are more central to the network and may be more important in the context of the exposure-omics relationships. The centrality metrics included are:

• Mean Betweenness Centrality: Measures how often a node lies on the shortest path between other nodes where high values indicate potential intermediates.

• Mean Closeness Centrality: Measures how close a node is to all others in the network where high values indicate faster access to all nodes.

• Mean Degree: The average number of direct connections or edges a node has.

• Mean Eigenvector Centrality: Measures how well-connected a node is and how well-connected its neighbors are, where high values suggest influence in a well-connected cluster.

# Run exposure-omics impact analysis
expom <- run_exposure_impact(
    exposomicset = expom,
    feature_type = "omics"
)

To plot the results of the exposure-omics impact analysis, we can use the plot_exposure_impact function. We set feature_type to omics, which means that we are plotting the network metrics of the omics features correlated with exposures. The min_per_group argument is used to set the minimum number of features per group to plot. This is useful for filtering out exposures that do not have enough associated features.

# Plot the exposure impact summary
plot_exposure_impact(
    exposomicset = expom,
    feature_type = "omics",
    min_per_group = 10
)

Network centrality metrics of omics features associated with each exposure.

Here we see that child PFNA (hs_pfna_c_Log2) exposure is associated with omics features that are central to our network, despite not having the greatest number of features associated with it. This suggests that childhood PFNA exposure may have a significant impact on the omics features, even if it is not associated with as many features as some of the other exposures.

Enrichment Visualizations

To visualize our enrichment results we provide several options:

• dot`plot: A dot plot showing the top enriched terms. The size of the dots represents the number of features associated with the term, while the color represents the significance of the term.

• cnet: A network plot showing the relationship between features and enriched terms.

• network: A network plot showing the relationship between enriched terms.

• heatmap: A heatmap showing the relationship between features and enriched terms.

• summary: A summary figure of the enrichment results.

# Plot the summary diagram
plot_enrichment(
    exposomicset = expom,
    feature_type = "omics_cor",
    plot_type = "summary"
)

## NULL

# Plot a dotplot of terms
plot_enrichment(
    exposomicset = expom,
    feature_type = "omics_cor",
    plot_type = "dotplot",
    top_n = 15,
    add_top_genes = TRUE,
    top_n_genes = 5
)

## NULL

# Plot the term network plot
# Setting a seed so that the plot layout is consistent
set.seed(42)
plot_enrichment(
    exposomicset = expom,
    feature_type = "omics_cor",
    plot_type = "network",
    label_top_n = 1
)

## NULL

# Plot a heatmap of genes and corresponding GO terms
plot_enrichment(
    exposomicset = expom,
    feature_type = "omics_cor",
    go_groups = "Group 1",
    plot_type = "heatmap",
    heatmap_fill = TRUE,
    feature_col = "feature_clean"
)

## NULL

# Plot the gene-term network
# Setting a seed so that the plot layout is consistent
set.seed(42)
plot_enrichment(
    exposomicset = expom,
    feature_type = "omics_cor",
    go_groups = "Group 1",
    plot_type = "cnet",
    feature_col = "feature_clean"
)

## NULL

Pivot Sample

Let’s check out the pivot_sample function. This function pivots the sample data to a tibble with samples as rows and exposures as columns.

# Pivot sample data to a tibble
expom |>
    pivot_sample() |>
    head()

## # A tibble: 6 × 73
##   .sample e3_sex_None hs_child_age_None h_age_None h_mbmi_None e3_gac_None
##   <chr>   <fct>                   <dbl>      <dbl>       <dbl>       <dbl>
## 1 s812    female                   6.84       35          33.2        41.4
## 2 s296    female                   6.75       32.8        25.1        39.1
## 3 s242    female                   6.97       33          24.6        40.4
## 4 s376    male                     6.35       27.1        28.3        37  
## 5 s1183   female                   6.74       32.7        33.3        38.1
## 6 s98     female                   6.93       23          31.2        39  
## # ℹ 67 more variables: h_native_None <fct>, h_parity_None <fct>,
## #   hs_asthma <dbl>, hs_zbmi_who <dbl>, h_pm10_ratio_preg_None <dbl>,
## #   h_pm25_ratio_preg_None <dbl>, hs_pm10_yr_hs_h_None <dbl>,
## #   hs_pm25_yr_hs_h_None <dbl>, hs_pb_c_Log2 <dbl>, hs_pfhxs_m_Log2 <dbl>,
## #   hs_pfna_c_Log2 <dbl>, hs_bpa_madj_Log2 <dbl>, hs_mibp_cadj_Log2 <dbl>,
## #   PC1 <dbl>, PC2 <dbl>, PC3 <dbl>, PC4 <dbl>, PC5 <dbl>, PC6 <dbl>,
## #   PC7 <dbl>, PC8 <dbl>, PC9 <dbl>, PC10 <dbl>, PC11 <dbl>, PC12 <dbl>, …

We could use this functionality to count the number of asthmatics per sex:

# Count the number of asthma cases by sex status
expom |>
    pivot_sample() |>
    group_by(hs_asthma, e3_sex_None) |>
    reframe(n = n())

## # A tibble: 4 × 3
##   hs_asthma e3_sex_None     n
##       <dbl> <fct>       <int>
## 1         0 female         23
## 2         0 male           15
## 3         1 female          5
## 4         1 male            4

Pivot Feature

The pivot_feature function pivots the feature metadata to a tibble with features as rows and feature metadata as columns. This can be useful for exploring the feature metadata in a more flexible way.

# Pivot feature data to a tibble
expom |>
    pivot_feature() |>
    head()

## # A tibble: 6 × 42
##   .exp_name     .feature transcript_cluster_id probeset_id seqname strand  start
##   <chr>         <chr>    <chr>                 <chr>       <chr>   <chr>   <int>
## 1 Gene Express… TC02004… TC02004973.hg.1       TC02004973… chr2    +      8.99e7
## 2 Gene Express… TC02004… TC02004949.hg.1       TC02004949… chr2    -      8.96e7
## 3 Gene Express… TC14002… TC14002235.hg.1       TC14002235… chr14   -      1.06e8
## 4 Gene Express… TC15000… TC15000863.hg.1       TC15000863… chr15   +      9.09e7
## 5 Gene Express… TC19001… TC19001585.hg.1       TC19001585… chr19   -      4.39e7
## 6 Gene Express… TC17000… TC17000226.hg.1       TC17000226… chr17   +      1.90e7
## # ℹ 35 more variables: stop <int>, total_probes <int>, gene_assignment <chr>,
## #   mrna_assignment <chr>, notes <chr>, phase <chr>, TC_size <dbl>,
## #   TSS_Affy <int>, EntrezeGeneID_Affy <chr>, GeneSymbol_Affy <chr>,
## #   GeneSymbolDB <chr>, GeneSymbolDB2 <chr>, mrna_ID <chr>, mrna_DB <chr>,
## #   mrna_N <int>, notesYN <chr>, geneYN <chr>, genes_N <dbl>, CallRate <dbl>,
## #   fil1 <chr>, feature_clean <chr>, Forward_Sequence <chr>, SourceSeq <chr>,
## #   Random_Loci <chr>, Methyl27_Loci <chr>, UCSC_RefGene_Name <chr>, …

We could use this functionality to count the number of features per omics layer, or to filter features based on their metadata. For example, we can count the number of features per omics layer:

# Count the number of features per omic layer
expom |>
    pivot_feature() |>
    group_by(.exp_name) |>
    summarise(n = n())

## # A tibble: 2 × 2
##   .exp_name           n
##   <chr>           <int>
## 1 Gene Expression   471
## 2 Methylation       277

Pivot Experiment

Now if we want to grab assay data from a particular experiment, we can do that with the pivot_exp. Let’s try grabbing assay values for the TC01004453.hg.1 probe (probe for IL23R) in the Gene Expression experiment:

# Pivot experiment data to a tibble
expom |>
    pivot_exp(
        exp_name = "Gene Expression",
        features = "TC01004453.hg.1"
    ) |>
    head()

## # A tibble: 6 × 102
##   exp_name     .feature .sample counts transcript_cluster_id probeset_id seqname
##   <chr>        <chr>    <chr>    <dbl> <chr>                 <chr>       <chr>  
## 1 Gene Expres… TC01004… s812     1.09  TC01004453.hg.1       TC01004453… chr1   
## 2 Gene Expres… TC01004… s296     1.71  TC01004453.hg.1       TC01004453… chr1   
## 3 Gene Expres… TC01004… s242     1.19  TC01004453.hg.1       TC01004453… chr1   
## 4 Gene Expres… TC01004… s376     1.21  TC01004453.hg.1       TC01004453… chr1   
## 5 Gene Expres… TC01004… s1183    0.803 TC01004453.hg.1       TC01004453… chr1   
## 6 Gene Expres… TC01004… s98      0.835 TC01004453.hg.1       TC01004453… chr1   
## # ℹ 95 more variables: strand <chr>, start <int>, stop <int>,
## #   total_probes <int>, gene_assignment <chr>, mrna_assignment <chr>,
## #   notes <chr>, phase <chr>, TC_size <dbl>, TSS_Affy <int>,
## #   EntrezeGeneID_Affy <chr>, GeneSymbol_Affy <chr>, GeneSymbolDB <chr>,
## #   GeneSymbolDB2 <chr>, mrna_ID <chr>, mrna_DB <chr>, mrna_N <int>,
## #   notesYN <chr>, geneYN <chr>, genes_N <dbl>, CallRate <dbl>, fil1 <chr>,
## #   feature_clean <chr>, e3_sex_None <fct>, hs_child_age_None <dbl>, …

We can use this functionality to create custom plots or analyses based on the exposure and feature data. For example, we can plot the expression levels of IL23R by asthma status:

# Plot expression of IL23R
expom |>
    pivot_exp(
        exp_name = "Gene Expression",
        features = "TC01004453.hg.1"
    ) |>
    ggplot(aes(
        x = as.character(hs_asthma),
        y = log2(counts + 1),
        color = as.character(hs_asthma),
        fill = as.character(hs_asthma)
    )) +
    geom_boxplot(alpha = 0.5) +
    geom_jitter(alpha = 0.1) +
    ggpubr::geom_pwc(label = "{p.adj.format}{p.adj.signif}") +
    theme_minimal() +
    ggpubr::rotate_x_text(angle = 45) +
    ggsci::scale_color_cosmic() +
    ggsci::scale_fill_cosmic() +
    labs(
        x = "",
        y = expression(Log[2] * "Abd."),
        fill = "Asthma Status",
        color = "Asthma Status"
    )

IL23R gene expression levels (probe TC01004453.hg.1) by asthma status.

Saving Results

We can export the results in our MultiAssayExperiment to an Excel spreadsheet using the extract_results_excel function. Here we add all of our results to the Excel file, but we can choose certain results by changing the result_types argument.

# Save results
extract_results_excel(
    exposomicset = expom,
    file = tempfile(),
    result_types = "all"
)

## Writing Correlation Results.

## Writing Association Results.

## Writing Differential Abundance Results.

## Writing Sensitivity Analysis Results.

## Writing Multiomics Integration Results.

## Writing Network Impact Results.

## Writing Enrichment Results.

## Writing Pipeline Summary.

## Writing Exposure Summary Results.

## Results written to: /tmp/RtmpX9Epgz/file386279c59816

Session Info

sessionInfo()

## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 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=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: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] tidyexposomics_0.99.12      MultiAssayExperiment_1.37.2
##  [3] SummarizedExperiment_1.41.1 Biobase_2.71.0             
##  [5] GenomicRanges_1.63.1        Seqinfo_1.1.0              
##  [7] IRanges_2.45.0              S4Vectors_0.49.0           
##  [9] BiocGenerics_0.57.0         generics_0.1.4             
## [11] MatrixGenerics_1.23.0       matrixStats_1.5.0          
## [13] lubridate_1.9.5             forcats_1.0.1              
## [15] stringr_1.6.0               dplyr_1.2.0                
## [17] purrr_1.2.1                 readr_2.1.6                
## [19] tidyr_1.3.2                 tibble_3.3.1               
## [21] ggplot2_4.0.2               tidyverse_2.0.0            
## [23] BiocStyle_2.39.0           
## 
## loaded via a namespace (and not attached):
##   [1] naniar_1.1.0          httr_1.4.7            RColorBrewer_1.1-3   
##   [4] doParallel_1.0.17     ggsci_4.2.0           dynamicTreeCut_1.63-1
##   [7] tools_4.5.2           doRNG_1.8.6.3         backports_1.5.0      
##  [10] vegan_2.7-2           utf8_1.2.6            R6_2.6.1             
##  [13] DT_0.34.0             mgcv_1.9-4            permute_0.9-10       
##  [16] GetoptLong_1.1.0      withr_3.0.2           gridExtra_2.3        
##  [19] progressr_0.18.0      cli_3.6.5             factoextra_1.0.7     
##  [22] RGCCA_3.0.3           labeling_0.4.3        sass_0.4.10          
##  [25] S7_0.2.1              randomForest_4.7-1.2  proxy_0.4-29         
##  [28] pbapply_1.7-4         ggridges_0.5.7        foreign_0.8-91       
##  [31] R.utils_2.13.0        sessioninfo_1.2.3     parallelly_1.46.1    
##  [34] itertools_0.1-3       limma_3.67.0          rstudioapi_0.18.0    
##  [37] RSQLite_2.4.6         FNN_1.1.4.1           shape_1.4.6.1        
##  [40] zip_2.3.3             car_3.1-5             dendextend_1.19.1    
##  [43] Matrix_1.7-4          clipr_0.8.0           abind_1.4-8          
##  [46] R.methodsS3_1.8.2     lifecycle_1.0.5       edgeR_4.9.2          
##  [49] yaml_2.3.12           snakecase_0.11.1      carData_3.0-6        
##  [52] recipes_1.3.1         SparseArray_1.11.10   BiocFileCache_3.1.0  
##  [55] Rtsne_0.17            grid_4.5.2            blob_1.3.0           
##  [58] promises_1.5.0        crayon_1.5.3          lattice_0.22-9       
##  [61] sys_3.4.3             maketools_1.3.2       pillar_1.11.1        
##  [64] knitr_1.51            ComplexHeatmap_2.27.1 rjson_0.2.23         
##  [67] corpcor_1.6.10        future.apply_1.20.1   mixOmics_6.35.0      
##  [70] codetools_0.2-20      glue_1.8.0            beepr_2.0            
##  [73] data.table_1.18.2.1   vctrs_0.7.1           png_0.1-8            
##  [76] Rdpack_2.6.6          testthat_3.3.2        gtable_0.3.6         
##  [79] assertthat_0.2.1      cachem_1.1.0          openxlsx_4.2.8.1     
##  [82] gower_1.0.2           xfun_0.56             rbibutils_2.4.1      
##  [85] S4Arrays_1.11.1       mime_0.13             prodlim_2025.04.28   
##  [88] tidygraph_1.3.1       survival_3.8-6        audio_0.1-12         
##  [91] timeDate_4052.112     iterators_1.0.14      hardhat_1.4.2        
##  [94] lava_1.8.2            statmod_1.5.1         ipred_0.9-15         
##  [97] nlme_3.1-168          fenr_1.9.1            bit64_4.6.0-1        
## [100] filelock_1.0.3        bslib_0.10.0          Deriv_4.2.0          
## [103] otel_0.2.0            rpart_4.1.24          colorspace_2.1-2     
## [106] DBI_1.2.3             Hmisc_5.2-5           nnet_7.3-20          
## [109] tidyselect_1.2.1      bit_4.6.0             compiler_4.5.2       
## [112] curl_7.0.0            tidyHeatmap_1.13.1    rvest_1.0.5          
## [115] httr2_1.2.2           htmlTable_2.4.3       xml2_1.5.2           
## [118] DelayedArray_0.37.0   checkmate_2.3.4       scales_1.4.0         
## [121] rappdirs_0.3.4        digest_0.6.39         rmarkdown_2.30       
## [124] XVector_0.51.0        htmltools_0.5.9       pkgconfig_2.0.3      
## [127] base64enc_0.1-6       SimDesign_2.23        dbplyr_2.5.1         
## [130] fastmap_1.2.0         rlang_1.1.7           GlobalOptions_0.1.3  
## [133] htmlwidgets_1.6.4     shiny_1.12.1          ggh4x_0.3.1          
## [136] farver_2.1.2          jquerylib_0.1.4       jsonlite_2.0.0       
## [139] dcurver_0.9.3         BiocParallel_1.45.0   R.oo_1.27.1          
## [142] ModelMetrics_1.2.2.2  magrittr_2.0.4        Formula_1.2-5        
## [145] patchwork_1.3.2       Rcpp_1.1.1            viridis_0.6.5        
## [148] visdat_0.6.0          stringi_1.8.7         pROC_1.19.0.1        
## [151] ggraph_2.2.2          brio_1.1.5            MASS_7.3-65          
## [154] plyr_1.8.9            parallel_4.5.2        listenv_0.10.0       
## [157] ggrepel_0.9.6         graphlayouts_1.2.2    splines_4.5.2        
## [160] hms_1.1.4             circlize_0.4.17       locfit_1.5-9.12      
## [163] igraph_2.2.2          ggpubr_0.6.2          ranger_0.18.0        
## [166] ggsignif_0.6.4        rngtools_1.5.2        buildtools_1.0.0     
## [169] reshape2_1.4.5        GPArotation_2025.3-1  tidybulk_2.1.0       
## [172] evaluate_1.0.5        BiocManager_1.30.27   tweenr_2.0.3         
## [175] tzdb_0.5.0            foreach_1.5.2         missForest_1.6.1     
## [178] httpuv_1.6.16         polyclip_1.10-7       future_1.69.0        
## [181] clue_0.3-66           mirt_1.45.1           ggforce_0.5.0        
## [184] BiocBaseUtils_1.13.0  janitor_2.2.1         broom_1.0.12         
## [187] xtable_1.8-4          e1071_1.7-17          RSpectra_0.16-2      
## [190] rstatix_0.7.3         later_1.4.5           viridisLite_0.4.3    
## [193] class_7.3-23          rARPACK_0.11-0        memoise_2.0.1        
## [196] ellipse_0.5.0         densityClust_0.3.3    cluster_2.1.8.2      
## [199] timechange_0.4.0      globals_0.19.0        caret_7.0-1

References

Chung, M. K., House, J. S., Akhtari, F. S., Makris, K. C., Langston, M. A., Islam, K. T., Holmes, P., Chadeau-Hyam, M., Smirnov, A. I., Du, X., Thessen, A. E., Cui, Y., Zhang, K., Manrai, A. K., Motsinger-Reif, A., Patel, C. J., & Members of the Exposomics Consortium. (2024). Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs). Exposome, 4(1), osae001. https://doi.org/10.1093/exposome/osae001

fenr. (n.d.). Bioconductor. Retrieved August 18, 2025, from https://www.bioconductor.org/packages/release/bioc/html/fenr.html

Maitre, L., Guimbaud, J.-B., Warembourg, C., Güil-Oumrait, N., Petrone, P. M., Chadeau-Hyam, M., Vrijheid, M., Basagaña, X., Gonzalez, J. R., & Exposome Data Challenge Participant Consortium. (2022). State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event. Environment International, 168(107422), 107422. https://doi.org/10.1016/j.envint.2022.107422

Miller, G. W., & Banbury Exposomics Consortium. (2025). Integrating exposomics into biomedicine. Science, 388(6745), 356–358. https://doi.org/10.1126/science.adr0544

mixOmics. (n.d.). Bioconductor. Retrieved August 18, 2025, from https://www.bioconductor.org/packages/devel/bioc/html/mixOmics.html

MOFA2. (n.d.). Bioconductor. Retrieved August 18, 2025, from https://www.bioconductor.org/packages/release/bioc/html/MOFA2.html

MultiAssay Special Interest Group. (2025, April 15). MultiAssayExperiment: The Integrative Bioconductor Container. https://www.bioconductor.org/packages/release/bioc/vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html

nipalsMCIA. (n.d.). Bioconductor. Retrieved August 18, 2025, from https://www.bioconductor.org/packages/release/bioc/html/nipalsMCIA.html

Regularized and Sparse Generalized Canonical Correlation Analysis for Multiblock Data. (n.d.). Retrieved August 18, 2025, from https://rgcca-factory.github.io/RGCCA/

Wild, C. P. (2005). Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiology, Biomarkers & Prevention, 14(8), 1847–1850. https://doi.org/10.1158/1055-9965.EPI-05-0456