Package index • CLAMP

All functions

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