CLAMP

Bioconductor release status

Branch	R CMD check	Last updated
devel
release	Not yet released	—

The goal of CLAMP (Curated Latent-variable Analysis with Molecular Priors) is to provide an easy-to-use package to extract interpretable latent variables from large transcriptomic datasets using biological priors.

Installation

CLAMP is available in the development branch of Bioconductor and has not yet entered a Bioconductor release. It requires R >= 4.6.0 and an R version compatible with Bioconductor devel (see the Bioconductor installation guide).

To install from Bioconductor devel, first switch your R library to the development version of Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install(version = "devel")
BiocManager::install("CLAMP")

Alternatively, with Bioconductor devel configured, you can install the latest source from GitHub:

BiocManager::install("chikinalab/CLAMP")

Now you can load the package using:

library(CLAMP)

Basic usage

Full documentation is available at chikinalab.org/CLAMP. For detailed instructions on how to use CLAMP, please see the vignette.

library(CLAMP)

set.seed(1)

# Load example dataset (genes × samples)
data("dataWholeBlood")

# The example data are already normalized; filter and z-score directly.

prep <- preprocessCLAMP(
    dataWholeBlood,
    mean_cutoff = 0.5,
    var_cutoff = 0.1,
    log2_transform = FALSE
)

Y_z <- zscoreCLAMP(
    prep$Y_filtered,
    prep$rowStats
)

# Compute truncated SVD and select number of latent variables
svd_k <- select_svd_k(Y_z)

svd_res <- compute_svd(
    Y_z,
    k = svd_k
)

clamp_k <- select_clamp_k(
    svd_res,
    n_samples = ncol(Y_z),
    svd_k = svd_k
)

# Run CLAMPbase to initialize latent variables
baseRes <- CLAMPbase(
    Y = Y_z,
    svdres = svd_res,
    clamp_k = clamp_k
)

# Load pre-fetched gene set libraries bundled with the package
gmtList <- list(
    CellMarkers = readRDS(
        system.file("extdata", "CellMarker_2024.rds", package = "CLAMP")
    ),
    KEGG = readRDS(
        system.file("extdata", "KEGG_2021_Human.rds", package = "CLAMP")
    )
)

# Combine gene set libraries into a single sparse matrix
pathMatCell <- gmtListToSparseMat(gmtList)

# Load additional xCell reference matrix
data("xCell")

# Match pathways to the filtered gene space used by CLAMP
matchedPathsWB <- getMatchedPathwayMatList(
    pathMatCell,
    xCell,
    new.genes = rownames(Y_z),
    min.genes = 2
)

# Run CLAMPfull using CLAMPbase initialization and matched priors
fullRes <- CLAMPfull(
    Y = Y_z,
    priorMat = matchedPathsWB,
    clamp.base.result = baseRes,
    svdres = svd_res,
    clamp_k = clamp_k,
    use_cpp = TRUE
)

# Inspect outputs
dim(fullRes$Z)
dim(fullRes$B)
dim(fullRes$U)

# Inspect significant pathway annotations
summary_df <- as.data.frame(fullRes$summary)

sig_summary <- summary_df[
    summary_df$FDR < 0.05 & summary_df$AUC > 0.7,
    c("LV", "pathway", "FDR", "AUC")
]

sig_summary <- sig_summary[order(sig_summary$FDR), ]

head(sig_summary, 20)

Code of Conduct

Please note that the CLAMP project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.