Installation | Load packages | Introduction | Workflow overview | Quick Start | Available datasets | Computing the SVD and inferring k | Example 1: In-memory data.frame (Whole Blood) | Load example data | Preprocess and z-score | Compute SVD and infer k | CLAMPbase initialization | Prepare pathway priors | CLAMPfull | Display significant latent variables | Example 2: File-Backed Matrix (Alzheimer’s Brain) | Cleanup old FBM files | Computing CPM on a File-Backed Matrix (FBM) | HDF5 schema | Load HDF5 expression | Construct file‑backed FBM | CPM, preprocess and z‑score FBM | CLAMPbase | Example 3: Tab-Delimited Count File (Pancreatic Islets) | Load count data and map gene symbols | Aggregate counts by gene symbol | CPM, preprocess and z-score | Differential latent-variable expression between conditions | Projection: Applying one CLAMP model to another dataset | Choosing the Number of Latent Variables (CLAMP_K) | Elbow method (default) | Permutation method | Gavish–Donoho optimal hard threshold (PCAtools) | Visualization | Pathway–LV association heatmap (CLAMPplotU) | Top-gene loading plot (CLAMPplotTopZ) | Single-LV pathway dot plot (CLAMPdotplot) | All-LV pathway dot plot (CLAMPdotplotAll) | Parallelization in CLAMP | Session Information

Introduction | Installation | Load packages | Available Resources | Accessing data resources | Reading example data | Session info

Overview | 1. Load Data and Normalize | 2. Load Prior Knowledge Matrices | 3. Compute SVD and Infer k | 4. Fit CLAMPbase and CLAMPfull | 5. Compare CLAMPbase vs CLAMPfull | 6. Inspect Named Matrix Outputs | 7. Verify FBM Implementation | Session Information