Package: CLAMP 0.99.3

Marc Subirana-Granes

CLAMP: Curated Latent-variable Analysis with Molecular Priors

CLAMP performs prior-informed latent variable decomposition of high-dimensional transcriptomic data. It integrates curated gene sets to learn biologically interpretable latent variables, supports file-backed matrices for large datasets, and provides tools for preprocessing, normalization, projection, and evaluation of latent structures. CLAMP is designed to scale to tens of thousands of samples, making it suitable for large public resources such as recount3 and ARCHS4. It enables researchers to uncover biologically meaningful patterns that connect genes, pathways, and complex traits in transcriptomics studies.

Authors:Marc Subirana-Granes [aut, cre], Maria Chikina [aut], National Human Genome Research Institute [fnd], Eunice Kennedy Shriver National Institute of Child Health and Human Development [fnd], National Science Foundation [fnd], National Eye Institute [fnd]

CLAMP_0.99.3.tar.gz
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CLAMP_0.99.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CLAMP/json (API)

# Install 'CLAMP' in R:

install.packages('CLAMP', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/chikinalab/clamp/issues

Pkgdown/docs site:https://chikinalab.github.io

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

celltypeTargets - Cell-type deconvolution matrix
dataWholeBlood - Whole-blood reference expression matrix
majorCellTypes - Major cell-type annotations
panDB - PanDB gene-set database
xCell - XCell cell-signature matrix

On BioConductor:CLAMP-0.99.3(bioc 3.24)

software geneexpression rnaseq transcriptomics dimensionreduction visualization normalization pathways preprocessing geneprediction genetarget openblas cpp

5.60 score 5 stars 16 scripts 40 exports 68 dependencies

Last updated from:1768d3b1bc. Checks:1 NOTE, 11 WARNING, 1 ERROR, 1 OK. Indexed: yes.

Target	Result	Time
bioc-checks	NOTE	285
linux-devel-arm64	WARNING	324
linux-devel-x86_64	WARNING	357
source / vignettes	ERROR	350
linux-release-arm64	WARNING	277
linux-release-x86_64	WARNING	310
macos-release-arm64	WARNING	172
macos-release-x86_64	WARNING	340
macos-oldrel-arm64	WARNING	137
macos-oldrel-x86_64	WARNING	464
windows-devel	WARNING	313
windows-release	WARNING	270
windows-oldrel	WARNING	256
wasm-release	OK	205

Exports:allAgainstAllAUCs CLAMPbase CLAMPdotplot CLAMPdotplotAll CLAMPfull CLAMPfullnVP CLAMPplotTopZ CLAMPplotU cleanFBM compareBs compute_svd cpmCLAMP cpmCLAMPFBM cross_ZY differentialLVActivity filterFBM findSplineMax getChat getGMT getMatchedPathwayMat getMatchedPathwayMatList getScaleFromSVs gmtListToSparseMat mat_mult num.pc oneToOneMask plotTopZ_Complex preprocessCLAMP preprocessCLAMPFBM projectCLAMP read_gmt ridge_B select_clamp_k select_svd_k solveU squashZscore tscale winsor_topk zscoreCLAMP zscoreCLAMPFBM

Dependencies:bigassertr bigparallelr bigstatsr BiocGenerics bit circlize cli clue cluster codetools colorspace ComplexHeatmap cowplot cpp11 crayon digest doParallel dplyr farver ff flock foreach generics GetoptLong ggplot2 ggrepel glmnet GlobalOptions glue gtable IRanges irlba isoband iterators labeling lattice lifecycle magrittr Matrix matrixStats parallelly patchwork pillar pkgconfig png ps R6 RColorBrewer Rcpp RcppArmadillo RcppEigen RhpcBLASctl rjson rlang rmio RSpectra rsvd S4Vectors S7 scales shape survival tibble tidyselect utf8 vctrs viridisLite withr

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

Help page	Topics
Compute all-vs-all AUC matrix	allAgainstAllAUCs
Compute AUC using Wilcoxon rank-sum test	AUC
Adjust p-values using Benjamini-Hochberg method	BH
Binarize matrix by top-k values per column	binarizeTop
Cell-type deconvolution matrix	celltypeTargets
CLAMP base matrix factorization	CLAMPbase
Dot plot of top pathways for a single latent variable	CLAMPdotplot
Dot plot of pathway-LV associations across all latent variables	CLAMPdotplotAll
Runs the streamlined full CLAMP model.	CLAMPfull
Full CLAMP model with prior information and cross-validation	CLAMPfullnVP
Plot top genes per LV by Z loading	CLAMPplotTopZ
Plot the U matrix (pathway-LV associations) as a heatmap	CLAMPplotU
Clean a Filebacked Big Matrix (FBM) by log-transforming and handling NAs	cleanFBM
Find common row names between two matrices or data frames	commonRows
Compare two sets of factor loadings or embeddings	compareBs
Compute a truncated SVD for a CLAMP input matrix	compute_svd
Compute row-wise sum and sum of squares for a Filebacked Big Matrix	computeRowStatsFBM
Compute counts-per-million (CPM) for CLAMP pipelines	cpmCLAMP
Compute CPM on a file-backed matrix for CLAMP (in-place)	cpmCLAMPFBM
Cross-product Z^T Y with FBM or dense matrices	cross_ZY
Cross-validation AUC for CLAMP latent variables and pathways	crossVal
Whole-blood reference expression matrix	dataWholeBlood
Differential latent-variable activity between sample groups	differentialLVActivity
Filter rows of a Filebacked Big Matrix based on mean and variance	filterFBM
Find the location of the maximum of a smoothing spline	findSplineMax
Count number of latent variables exceeding AUC thresholds	getAUCstats
Compute Chat matrix from prior annotation	getChat
Download and read a GMT file from a URL	getGMT
Subset and filter pathway matrix to match target genes	getMatchedPathwayMat
Subset and filter multiple pathway matrices to match target genes	getMatchedPathwayMat2
Subset and filter multiple pathway matrices to match target genes	getMatchedPathwayMatList
Subset and filter pathway matrix to match target genes	getMatchedPathwayMatOld
Get maximum AUC per latent variable	getMaxAUC
Estimate noise scale from singular values with linear tail extrapolation	getScaleFromSVs
Convert a list of GMT gene sets to a sparse matrix	gmtListToSparseMat
Major cell-type annotations	majorCellTypes
Matrix multiplication with support for FBM objects	mat_mult
Greedy maximum correspondence from correlation matrix	max_correspondence_greedy
Print a concatenated message	mymessage
Estimate number of principal components via elbow or permutation method	num.pc
One-to-one masking of maximum associations	oneToOneMask
panDB gene-set database	panDB
Ridge-regularized pseudoinverse via SVD	pinv.ridge
ComplexHeatmap visualization of top genes by latent variable	plotTopZ_Complex
Preprocess an expression matrix for CLAMP	preprocessCLAMP
Preprocess a bigstatsr FBM for CLAMP	preprocessCLAMPFBM
Project new data into CLAMP latent space	projectCLAMP
Read a GMT file into a list	read_gmt
Ridge regression update for B	ridge_B
Rotate SVD components to make dominant directions positive	rotateSVD
Row-wise correlation between two matrices	row_cor
Run elbow method to estimate number of PCs	run_elbow
Run permutation method to estimate number of PCs	run_permutation
Select default number of CLAMP latent variables from an SVD	select_clamp_k
Select default number of components for a CLAMP solver SVD	select_svd_k
Fit the loading matrix Z using sparse regression of prior information U	solveU
Squash extreme z-scores	squashZscore
Row-wise scaling (mean 0, sd 1)	tscale
Winsorize matrix columns by capping the top-k values	winsor_topk
xCell cell-signature matrix	xCell
Z-score a filtered expression matrix for CLAMP	zscoreCLAMP
Z-score a filtered FBM in-place	zscoreCLAMPFBM

Package: CLAMP 0.99.3

CLAMP: Curated Latent-variable Analysis with Molecular Priors

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