This package is for version 3.15 of Bioconductor; for the stable, up-to-date release version, see sccomp.
Bioconductor version: 3.15
A robust and outlier-aware method for testing differential tissue composition from single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.
Author: Stefano Mangiola [aut, cre]
Maintainer: Stefano Mangiola <mangiolastefano at gmail.com>
Citation (from within R,
enter citation("sccomp")
):
To install this package, start R (version "4.2") and enter:
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("sccomp")
For older versions of R, please refer to the appropriate Bioconductor release.
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("sccomp")
HTML | R Script | Overview of the sccomp package |
Reference Manual |
biocViews | Clustering, GeneExpression, ImmunoOncology, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics |
Version | 1.0.0 |
In Bioconductor since | BioC 3.15 (R-4.2) (0.5 years) |
License | GPL-3 |
Depends | R (>= 4.1.0) |
Imports | methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1), rstan (>= 2.18.1), SeuratObject, SingleCellExperiment, parallel, dplyr, tidyr, purrr, magrittr, rlang, tibble, boot, lifecycle, stats, tidyselect, utils, ggplot2, ggrepel, patchwork, forcats, readr, scales, stringr |
LinkingTo | BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) |
Suggests | BiocStyle, testthat (>= 3.0.0), markdown, ggplot2, knitr, tidyseurat, tidySingleCellExperiment |
SystemRequirements | GNU make |
Enhances | furrr, extraDistr |
URL | https://github.com/stemangiola/sccomp |
BugReports | https://github.com/stemangiola/sccomp/issues |
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Build Report |
Follow Installation instructions to use this package in your R session.
Source Package | sccomp_1.0.0.tar.gz |
Windows Binary | sccomp_1.0.0.zip |
macOS Binary (x86_64) | sccomp_1.0.0.tgz |
Source Repository | git clone https://git.bioconductor.org/packages/sccomp |
Source Repository (Developer Access) | git clone [email protected]:packages/sccomp |
Package Short Url | https://bioconductor.org/packages/sccomp/ |
Package Downloads Report | Download Stats |
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