distinct

DOI: 10.18129/B9.bioc.distinct    

distinct: a method for differential analyses via hierarchical permutation tests

Bioconductor version: Release (3.11)

distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group.

Author: Simone Tiberi [aut, cre], Mark D. Robinson [aut].

Maintainer: Simone Tiberi <simone.tiberi at uzh.ch>

Citation (from within R, enter citation("distinct")):

Installation

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("distinct")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("distinct")

 

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Details

biocViews DifferentialExpression, FlowCytometry, GeneExpression, GeneTarget, Genetics, MultipleComparison, RNASeq, Sequencing, SingleCell, Software, StatisticalMethod, Transcription, Visualization
Version 1.0.2
In Bioconductor since BioC 3.11 (R-4.0) (< 6 months)
License GPL (>= 3)
Depends R (>= 4.0)
Imports Rcpp, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma
LinkingTo Rcpp, RcppArmadillo
Suggests knitr, testthat, scater, UpSetR
SystemRequirements C++11
Enhances
URL https://github.com/SimoneTiberi/distinct
BugReports https://github.com/SimoneTiberi/distinct/issues
Depends On Me
Imports Me
Suggests Me
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Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package distinct_1.0.2.tar.gz
Windows Binary distinct_1.0.2.zip (32- & 64-bit)
macOS 10.13 (High Sierra) distinct_1.0.2.tgz
Source Repository git clone https://git.bioconductor.org/packages/distinct
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/distinct
Package Short Url https://bioconductor.org/packages/distinct/
Package Downloads Report Download Stats
Old Source Packages for BioC 3.11 Source Archive

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