DEcodeTree is a machine learning model designed to predict differential expression statistics from gene regulatory features.

📜 Link to the Paper

Read our full research paper for more details:
The YTHDF Proteins Shape the Brain Gene Signatures of Alzheimer's Disease

Installing

To install the DEcodeTree package, use the following commands:

install.packages("devtools")
devtools::install_github("stasaki/DEcodeTree")

Running the model

Quick Test Run

The following is a test run using only cell type information as predictors:

library(DEcodeTree)

# Load differential expression statistics for RNA-seq from DLPFC regions against cognitive decline in ROSMAP cohorts
deg_stat_data <- readRDS(system.file("data", "DLPFC_CogDec_DEG_stats.rds", package = "DEcodeTree"))

# Run the DEcodeTree model using cell type information as predictors
runDEcodeTreeModel(
  outcome = "tstat",
  deg_stat = deg_stat_data,
  feature_type = c("cell_type"),
  out_dir = "./results/",
  n_splits = 5,
  metric = c("COR", "RMSE"),
  num_samples = 10,
  num_cpus = 4
)

Full Feature Analysis

To run the complete analysis with all regulatory features, use:

⏱️ Runtime Warning: The following analysis uses 16 CPU cores and may take 1-4 hours to complete depending on your system's performance. The model performs extensive hyperparameter tuning (64 iterations) and cross-validation across multiple feature types.

runDEcodeTreeModel(
  outcome = "tstat",                           # Predict t-statistics
  deg_stat = deg_stat_data,                   # Our DE results
  feature_type = c("base", "RBP", "miRNA", "TF", "cell_type"), # All regulatory features
  out_dir = "./results/",                     # Output directory
  n_splits = 5,                               # Cross-validation splits
  metric = c("COR", "RMSE"),                  # Performance metrics
  num_samples = 64,                           # Hyperparameter tuning iterations
  num_cpus = 16                               # Parallel processing cores
) 

Additional Examples

We also provide an example of running DEcodeTree for TDP43 knockdown RNA-seq data in TDP43_example.md.

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Parameters

Parameter	Description	Default
outcome	A character string specifying the outcome variable (e.g., "logFC", "tstat").	"tstat"
deg_stat	A data frame containing differential expression statistics.	Required
feature_type	Features to include ("base", "RBP", "miRNA", "TF", "cell_type").	c("base")
out_dir	Output directory for results.	"./results/"
n_splits	Number of data splits for cross-validation.	5
metric	Performance metrics ("COR", "RMSE").	c("COR", "RMSE")
measures	List of measures for performance evaluation compatible with mlr.	Default measures
cv_folds	Number of folds for cross-validation.	5
num_samples	Number of hyperparameter tuning iterations.	64
num_cpus	Number of CPUs for parallel processing.	4
param_values	Default hyperparameter values for XGBoost models.	Predefined list
param_space	Parameter set defining the hyperparameter optimization space.	Predefined set
save_model	Whether to save trained models.	TRUE

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Outputs

1. Evaluation Results: Summary of model performance metrics (e.g., RMSE, correlation).
2. SHAP Values: Feature importance values for interpretability.
3. Plots: Visualizations of model performance and SHAP scores.
4. Trained Models: Serialized XGBoost models for future use.

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License

The code for this project is licensed under the BSD 3-Clause License. Gene regulatory features were prepared using the following databases. Please ensure compliance with the respective licenses for each data source:

• Transcription factor binding targets: GTRD
• RNA-binding protein binding targets: POSTAR2
• miRNA binding targets: TargetScan