BAMarray
Java software for microarray data. Implements Bayesian Analysis of Variance for Microarrays (BAM)
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Joint Mean-Variance Stabilization and Regularization for High Throughput Data
New R package!
An R package that stabilizes variances and also regularizes inferences using information in both the mean and the variance. Applications include high throughput “omic” data as well as other high throughput datasets.
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LDLasso
An R package that performs a linkage-disequilibrium based fusion of SNPestimates in GWAS studies with the goal of producing flatter, longer signal sites to allow weaker, correlated signal while still inducing sparsity via lasso shrinkage.
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Local Sparse Bump Hunting
An R package that searches for local structures in the form of modes in high dimensional space. Sparsity is imposed via sparse principal components analysis and localness imposed by tree-based partitioning of the predictor space. Modes are then identified via a version of the patient rule induction method (PRIM). New methods for optimal PRIM hyperparameter tuning are also implemented.
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pathwayPCA
With the advance in high-throughput technology for molecular assays, multi-omics datasets have become increasingly available. However, most currently available pathway analysis software do not provide estimates on sample-specific pathway activities, and provide little or no functionalities for analyzing multiple types of omics data simultaneously. To address these challenges, we present pathwayPCA, a unique integrative pathway analysis software that utilizes modern statistical methodology on principal component analysis (PCA) and gene selection.
The main features of pathwayPCA include:
- Performing pathway analysis for datasets with binary, continuous, or survival outcomes with computational efficiency.
- Extracting relevant genes from pathways using the SuperPCA and AESPCA approaches.
- Computing PCs based on the selected genes. These estimated latent variables represent pathway activity for individual subjects, which can be used to perform integrative pathway analysis, such as multi-omics analysis, or predicting survival time.
- Can be used to analyze studies with complex experimental designs that include multiple covariates and/or interaction effects. For example, testing whether pathway associations with clinical phenotype are different between male and female subjects.
- Performing analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects.
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randomForestSRC
Random forest for survival, regression, and classification. A unified treatment of Breiman’s random forests.
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spikeslab
Spike and slab R package for high dimensional linear regression models (release 1.1.2). Uses a generalized elastic net (gnet) for variableselection. Parallel processing enabled.
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