Package: scPAS Type: Package Title: Single-Cell Phenotype-Associated Subpopulation Identifier Version: 1.0.4 Authors@R: c( person("Aimin", "Xie", email = "aiminyy1993@gmail.com", role = "aut", comment = "Original author"), person("Zaoqu", "Liu", email = "liuzaoqu@163.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0452-742X", "Maintainer, bug fixes and optimization")) ) Maintainer: Zaoqu Liu Description: Identifies phenotype-associated cell subpopulations from single-cell RNA-seq data by integrating bulk RNA-seq data with phenotype information. This package uses network-regularized sparse regression to quantify the strength of association between each cell and a phenotype (e.g., disease stage, tumor metastasis, treatment response, survival outcomes). Compatible with both Seurat v4 (4.0.0+) and Seurat v5 (5.0.0+). The method supports Gaussian (continuous), binomial (binary), and Cox (survival) regression families. Full cross-platform compatibility (Windows, macOS, Linux). URL: https://github.com/Zaoqu-Liu/scPAS BugReports: https://github.com/Zaoqu-Liu/scPAS/issues Depends: R (>= 4.0.0) Imports: Rcpp (>= 1.0.0), Matrix, methods, stats, utils, Seurat (>= 4.0.0), SeuratObject (>= 4.0.0), preprocessCore, survival Suggests: ALRA, future, future.apply, testthat (>= 3.0.0), knitr, rmarkdown, ggplot2, patchwork, RColorBrewer, survminer, dplyr, reshape2 VignetteBuilder: knitr LinkingTo: Rcpp, RcppEigen License: GPL-3 Encoding: UTF-8 NeedsCompilation: yes RoxygenNote: 7.3.3 SystemRequirements: GNU make Biarch: true Config/pak/sysreqs: cmake libglpk-dev make libicu-dev libpng-dev libuv1-dev libxml2-dev libssl-dev python3 zlib1g-dev Repository: https://zaoqu-liu.r-universe.dev Date/Publication: 2026-01-23 10:16:15 UTC RemoteUrl: https://github.com/Zaoqu-Liu/scPAS RemoteRef: HEAD RemoteSha: 8f4f190d3e9b11b4b9f4a128fc8a0b66c1b22e75 Packaged: 2026-07-22 07:54:14 UTC; root Author: Aimin Xie [aut] (Original author), Zaoqu Liu [aut, cre] (ORCID: , Maintainer, bug fixes and optimization)