Package: plaid 1.1.0

Antonino Zito

plaid: PLAID ultrafast gene set enrichment scoring

PLAID (Pathway Level Average Intensity Detection) is an ultra-fast method to compute single-sample enrichment scores for gene expression or proteomics data. For each sample, plaid computes the gene set score as the average intensity of the genes/proteins in the gene set. The output is a gene set score matrix suitable for further analyses.

Authors:Ivo Kwee [aut], Antonino Zito [cre]

plaid_1.1.0.tar.gz
plaid_1.1.0.zip(r-4.7)plaid_1.1.0.zip(r-4.6)plaid_1.1.0.zip(r-4.5)
plaid_1.1.0.tgz(r-4.6-any)plaid_1.1.0.tgz(r-4.5-any)
plaid_1.1.0.tar.gz(r-4.7-any)plaid_1.1.0.tar.gz(r-4.6-any)
plaid_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
plaid/json (API)

# Install 'plaid' in R:
install.packages('plaid', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/bigomics/plaid/issues

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

On BioConductor:plaid-1.1.0(bioc 3.24)plaid-1.0.0(bioc 3.23)

genesetenrichmentgeneexpressionproteomicsbioinformaticsenrichment-analysisomics-datarna-seq-analysis

6.83 score 24 stars 3 scripts 15 exports 122 dependencies

Last updated from:6d2ad5c15e. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE207
linux-devel-x86_64OK393
source / vignettesOK265
linux-release-x86_64OK364
macos-release-arm64OK170
macos-oldrel-arm64OK189
windows-develOK235
windows-releaseOK246
windows-oldrelOK240
wasm-releaseOK209

Exports:colranksdualGSEAgmt2matgset.rankcormat2gmtnormalize_mediansplaidread.gmtreplaid.aucellreplaid.gsvareplaid.scsereplaid.singreplaid.ssgseareplaid.ucellwrite.gmt

Dependencies:abindannotateAnnotationDbiaskpassassortheadbeachmatBHBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsBiocIObiocmakeBiocParallelBiocSetBiocSingularBiostringsbitbit64blobcachemclicodetoolscowplotcpp11crayoncurldata.tableDBIdbplyrDelayedArrayDelayedMatrixStatsdir.expirydocoptdplyrfarverfastmapfastmatchfgseafilelockformatRfutile.loggerfutile.optionsgenericsGenomicRangesggplot2gluegraphGSEABaseGSVAgtableh5mreadHDF5Arrayhttrhttr2IRangesirlbaisobandjsonliteKEGGRESTlabelinglambda.rlatticelifecyclemagickmagrittrMatrixMatrixGenericsmatrixStatsmemoisememusemimeontologyIndexopensslpillarpkgconfigplyrpngpurrrqlcMatrixR6rappdirsRColorBrewerRcppRcppArmadilloRcppParallelRfastrhdf5rhdf5filtersRhdf5librjsonrlangRSQLitersvdS4ArraysS4VectorsS7ScaledMatrixscalesSeqinfoSingleCellExperimentslamsnowSparseArraysparseMatrixStatssparsesvdSpatialExperimentstringistringrSummarizedExperimentsystibbletidyrtidyselectutf8vctrsviridisLitewithrXMLxtableXVectorzigg

Comparing PLAID with other methods
Introduction | Benchmarking | Comparison | Computational Performance | Score Concordance | Replicating Other Methods | Conclusions | Session Info
Rendered fromcompare-vignette.Rmdusingknitr::rmarkdown

Last update: 2025-12-23
Started: 2025-10-02

Getting Started with PLAID
Introduction | Motivation | Example: Single-cell RNA-seq hallmark scoring | Preparing data | Preparing gene sets | Calculating the score | Very large matrices | Differential expression testing using dualGSEA | Replicating ssGSEA, singscore and scSE | Replicating singscore | Replicating ssGSEA | Replicating the scSE score | Compare scores | Session info
Rendered fromplaid-vignette.Rmdusingknitr::rmarkdown

Last update: 2025-12-23
Started: 2025-05-26

Readme and manuals

Help Manual

Help pageTopics
Chunked computation of cross productchunked_crossprod
Compute columnwise ranks of matrixcolranks
Calculate sparse correlation matrix handling missing valuescor_sparse_matrix
Reimplementation of dualGSEA (Bull et al., 2024) but defaults with replaid backend. For the preranked test we still use fgsea. Should be much faster than original using fgsea + GSVA::ssGSEA.dualGSEA
T-test statistical testing of differentially enrichmentfc_ttest
Z-test statistical testing of differentially enrichmentfc_ztest
Convert GMT to Binary Matrixgmt2mat
Compute geneset expression as the average log-ration of genes in the geneset. Requires log-expression matrix X and (sparse) geneset matrix matG.gset_averageCLR
Perform t-test on gene set scoresgset_ttest
Calculate gene set rank correlationgset.rankcor
Calculate row standard deviations for matrixmat.rowsds
Convert Binary Matrix to GMTmat2gmt
Matrix version for combining p-values using fisher or stouffer method. Much faster than doing metap::sumlog() and metap::sumz()matrix_metap
Perform one-sample t-test on matrix with gene setsmatrix_onesample_ttest
Normalize column medians of matrixnormalize_medians
Compute PLAID single-sample enrichment scoreplaid
Read GMT Fileread.gmt
Fast calculation of AUCellreplaid.aucell
Fast approximation of GSVAreplaid.gsva
Fast calculation of scSE scorereplaid.scse
Fast calculation of singscorereplaid.sing
Fast calculation of ssGSEAreplaid.ssgsea
Fast calculation of UCellreplaid.ucell
Compute columm ranks for sparse matrix. Internally used by colranks()sparse_colranks
Write GMT Filewrite.gmt