Package: Seqtometry Title: Signature scoring for single cell analysis Version: 1.1.1 URL: https://github.com/HawigerLab/Seqtometry BugReports: https://github.com/HawigerLab/Seqtometry/issues Authors@R: c( person("Robert", "Kousnetsov", email = "robert.kousnetsov@health.slu.edu", role = c("aut", "cre")), person("Daniel", "Hawiger", email = "daniel.hawiger@health.slu.edu", role = c("cph", "fnd"))) Description: This package provides functions used in Seqtometry (Kousnetsov et al. 2024), a method for analyzing single cell (scRNA-seq or scATAC-seq) data via signature (gene set) enrichment scores. The Seqtometry scores may be useful for annotating or characterizing cells, either in a flow cytometry like workflow (where scores are standalone features used for progressive partitoning as described in the Seqtometry publication) or in a cluster-based workflow (as features of clusters). The exported impute function (a port of Python's MAGIC-impute, van Dijk et al. 2018), may also be useful for single cell analysis on its own. License: MIT + file LICENSE Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 8.0.0 Depends: R (>= 4.5.0) LinkingTo: Rcpp, RcppArmadillo Imports: BiocSingular, checkmate, data.table, DelayedMatrixStats, future.apply, Matrix, MatrixGenerics, purrr, Rcpp, RcppHNSW, RSpectra, sparseMatrixStats, zeallot Suggests: BiocStyle, box, dplyr, future, ggplot2, harmony, knitr, MASS, patchwork, rmarkdown, scater, scuttle, SingleCellExperiment, stringr, TENxPBMCData, testthat (>= 3.0.0), tibble biocViews: SingleCell, GeneSetEnrichment, GeneExpression VignetteBuilder: knitr Config/testthat/edition: 3 Config/pak/sysreqs: zlib1g-dev Repository: https://bioc.r-universe.dev Date/Publication: 2026-07-28 03:50:24 UTC RemoteUrl: https://github.com/bioc/Seqtometry RemoteRef: HEAD RemoteSha: 9432878f7ea926e93b431b651d0a20cc82ef3db3 NeedsCompilation: yes Packaged: 2026-10-26 08:25:31 UTC; root Author: Robert Kousnetsov [aut, cre], Daniel Hawiger [cph, fnd] Maintainer: Robert Kousnetsov