miloR
Differential neighbourhood abundance testing on a graph
Bioconductor version: 3.23 · Package version: 2.8.1
Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("miloR")Details
| Maintainer | Mike Morgan <michael.morgan@abdn.ac.uk> |
| Author | Mike Morgan [aut, cre] (ORCID: <https://orcid.org/0000-0003-0757-0711>), Emma Dann [aut, ctb] |
| License | GPL-3 + file LICENSE |
| URL | https://marionilab.github.io/miloR |
| Bug Reports | https://github.com/MarioniLab/miloR/issues |
| Downloads rank | 1357 |
| Source branch | RELEASE_3_23 |
| biocViews | FunctionalGenomics, MultipleComparison, SingleCell, Software |
Documentation
- Differential abundance testing with Milo
- Differential abundance testing with Milo - Mouse gastrulation example
- Mixed effect models for Milo DA testing
- Making comparisons for differential abundance using contrasts
Download
Dependencies
Depends: R (>= 4.0.0), edgeR
Imports: BiocNeighbors, BiocGenerics, SingleCellExperiment, Matrix (>= 1.3-0), MatrixGenerics, S4Vectors, stats, stringr, methods, igraph, irlba, utils, cowplot, BiocParallel, BiocSingular, limma, ggplot2, tibble, matrixStats, ggraph, gtools, SummarizedExperiment, patchwork, tidyr, dplyr, ggrepel, ggbeeswarm, RColorBrewer, grDevices, Rcpp, pracma, numDeriv
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, mvtnorm, scater, scran, covr, knitr, rmarkdown, uwot, scuttle, BiocStyle, MouseGastrulationData, MouseThymusAgeing, magick, RCurl, MASS, curl, scRNAseq, graphics, sparseMatrixStats
Reverse dependencies
Imports Me (1): dandelionR