This example shows a regression workflow for predicting gestational age from multimodal pregnancy data using caretMultimodal. The workflow covers downloading and preprocessing seven paired omics assays, training modality-specific elastic net models and a stacked ensemble under leave-one-subject-out cross-validation, and evaluating performance with Spearman correlation, contribution plots, and ablation analysis.
Load and preprocess the data
# Download the data
url <- "https://nalab.stanford.edu/wp-content/uploads/termpregnancymultiomics.zip"
buf <- curl::curl_fetch_memory(url)$content
load(archive::archive_read(rawConnection(buf), file = "termpregnancymultiomics/Data.Rda"))
# Name the input data
names(InputData) <- c("Cellfree RNA", "Plasma Luminex", "Serum Luminex", "Microbiome", "Immune System", "Metabolomics", "Plasma Somalogic")
# Correct nonstandard feature names
InputData <- lapply(InputData, function(df) {
df <- as.data.frame(df)
names(df) <- make.names(iconv(names(df), from = "latin1", to = "UTF-8", sub = ""), unique = TRUE)
df
})
names(InputData$Metabolomics) <- paste("Metabolomic Feature", seq_along(names(InputData$Metabolomics)))
# Remove postpartum samples
postpartum_indices <- which(featureweeks < 0)
InputData <- lapply(InputData, function(x) x[-postpartum_indices, ])
featurepatients <- featurepatients[-postpartum_indices]
featureweeks <- featureweeks[-postpartum_indices]Train models with caretMultimodal
set.seed(42L)
# Define hyperparameter tuning grid
alphas <- seq(0, 1, 0.1)
lambdas <- seq(0, 12, by = 0.5)
tuneGrid <- expand.grid(alpha = alphas, lambda = lambdas)
# Set up leave-one-subject out cross validation
loso_folds <- lapply(unique(featurepatients), function(p) {
which(featurepatients != p) # indices of all samples NOT in this patient
})
trControl <- caret::trainControl(
method = "cv",
index = loso_folds,
savePredictions = "final",
summaryFunction = caret::defaultSummary
)
# Train the base models
pregnancy_models <- caretMultimodal::caret_list(
data_list = InputData,
target = featureweeks,
method = "glmnet",
tuneGrid = tuneGrid,
trControl = trControl,
trim = FALSE
)## Loading required package: ggplot2## Loading required package: lattice## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
# Train the ensemble model
pregnancy_stack <- caretMultimodal::caret_stack(
pregnancy_models,
method = "glmnet",
tuneGrid = tuneGrid,
trControl = trControl
)## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.Evaluate and Interpret
summary(pregnancy_stack)## model method alpha lambda RMSE Rsquared MAE RMSESD
## <char> <char> <num> <num> <num> <num> <num> <num>
## 1: Cellfree RNA glmnet 0.5 4.5 7.514323 0.6728265 6.094657 2.316190
## 2: Plasma Luminex glmnet 0.7 1.0 6.264192 0.7268943 5.029513 1.921326
## 3: Serum Luminex glmnet 1.0 2.0 7.988148 0.6981922 6.677682 1.668235
## 4: Microbiome glmnet 0.2 11.5 8.130642 0.4181656 6.648128 1.991066
## 5: Immune System glmnet 0.1 1.5 4.559213 0.9013736 3.970832 1.973698
## 6: Metabolomics glmnet 0.1 0.5 2.965703 0.9640824 2.513691 1.675753
## 7: Plasma Somalogic glmnet 1.0 1.0 3.444065 0.8865887 2.659584 2.118518
## 8: ensemble glmnet 0.0 0.5 2.758618 0.9772290 2.305836 1.388231
## RsquaredSD MAESD
## <num> <num>
## 1: 0.28378405 1.820931
## 2: 0.30932681 1.738192
## 3: 0.27331008 1.482994
## 4: 0.31921014 1.713364
## 5: 0.23586666 1.745224
## 6: 0.04075627 1.390273
## 7: 0.24339738 1.232796
## 8: 0.02707345 1.176519
caretMultimodal::plot_model_contributions(pregnancy_stack)
caretMultimodal::plot_feature_contributions(pregnancy_stack)
metric_fun <- function(preds, target) {
-log10(cor.test(preds, target, method = "spearman")$p.value)
}
caretMultimodal::plot_metric(pregnancy_stack, metric_fun = metric_fun, metric_name = "-log(p-val)")
caretMultimodal::plot_ablation(pregnancy_stack, metric_fun = metric_fun, metric_name = "-log(p-val)")## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.## Warning in cor.test.default(preds, target, method = "spearman"): cannot compute
## exact p-value with ties## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.## Warning in cor.test.default(preds, target, method = "spearman"): cannot compute
## exact p-value with ties## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.## Warning in cor.test.default(preds, target, method = "spearman"): cannot compute
## exact p-value with ties## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.## Warning in cor.test.default(preds, target, method = "spearman"): cannot compute
## exact p-value with ties## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.## Warning in cor.test.default(preds, target, method = "spearman"): cannot compute
## exact p-value with ties## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.## Warning in cor.test.default(preds, target, method = "spearman"): cannot compute
## exact p-value with ties
## Warning in cor.test.default(preds, target, method = "spearman"): cannot compute
## exact p-value with ties