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Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data

Bioconductor version: Release (3.19)

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>

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biocViews Clustering, GeneExpression, ImmunoOncology, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics
Version 1.8.0
In Bioconductor since BioC 3.15 (R-4.2) (2 years)
License GPL-3
Depends R (>= 4.2.0)
Imports methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1), rstan (>= 2.26.0), SeuratObject, SingleCellExperiment, parallel, dplyr, tidyr, purrr, magrittr, rlang, tibble, boot, lifecycle, stats, tidyselect, utils, ggplot2, ggrepel, patchwork, forcats, readr, scales, stringr, glue
System Requirements GNU make
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Suggests BiocStyle, testthat (>= 3.0.0), markdown, knitr, loo, tidyseurat, tidySingleCellExperiment, prettydoc
Linking To BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>=, RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>= 2.26.0)
Enhances furrr, extraDistr
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