Package: miloR 2.8.1

Mike Morgan

miloR: Differential neighbourhood abundance testing on a graph

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.

Authors:Mike Morgan [aut, cre], Emma Dann [aut, ctb]

miloR_2.8.1.tar.gz
miloR_2.8.1.zip(r-4.7)miloR_2.8.1.zip(r-4.6)miloR_2.8.1.zip(r-4.5)
miloR_2.8.1.tgz(r-4.6-x86_64)miloR_2.8.1.tgz(r-4.6-arm64)miloR_2.8.1.tgz(r-4.5-x86_64)miloR_2.8.1.tgz(r-4.5-arm64)
miloR_2.8.1.tar.gz(r-4.7-arm64)miloR_2.8.1.tar.gz(r-4.7-x86_64)miloR_2.8.1.tar.gz(r-4.6-arm64)miloR_2.8.1.tar.gz(r-4.6-x86_64)
miloR_2.6.0.tgz(r-4.5-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
miloR/json (API)
NEWS

# Install 'miloR' in R:
install.packages('miloR', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/marionilab/milor/issues

Pkgdown/docs site:https://marionilab.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On BioConductor:miloR-2.9.1(bioc 3.24)miloR-2.8.1(bioc 3.23)

singlecellmultiplecomparisonfunctionalgenomicssoftwareopenblascppopenmp

11.37 score 433 stars 2 packages 578 scripts 1.3k downloads 54 exports 92 dependencies

Last updated from:bc331e8aa3 (on RELEASE_3_23). Checks:1 WARNING, 11 NOTE, 1 OK, 1 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksWARNING395
linux-devel-arm64NOTE576
linux-devel-x86_64NOTE717
source / vignettesOK1015
linux-release-arm64NOTE570
linux-release-x86_64NOTE716
macos-release-arm64NOTE462
macos-release-x86_64NOTE763
macos-oldrel-arm64NOTE435
macos-oldrel-x86_64NOTE677
windows-develNOTE2081
windows-releaseNOTE2160
windows-oldrelNOTE2109
wasm-releaseFAIL254

Exports:.calc_distance.parse_formula.rEParseannotateNhoodsbuildFromAdjacencybuildGraphbuildNhoodGraphcalcNhoodDistancecalcNhoodExpressioncheckSeparationcomputePvaluecountCellsfindNhoodGroupMarkersfindNhoodMarkersfitGLMMglmmControl.defaultsgraphgraph<-graphSpatialFDRgroupNhoodsinitialiseGinitializeFullZmakeNhoodsmatrix.traceMilonhoodAdjacencynhoodAdjacency<-nhoodCountsnhoodCounts<-nhoodDistancesnhoodDistances<-nhoodExpressionnhoodExpression<-nhoodGraphnhoodGraph<-nhoodIndexnhoodIndex<-nhoodReducedDimnhoodReducedDim<-nhoodsnhoods<-plotDAbeeswarmplotNhoodCountsplotNhoodExpressionDAplotNhoodExpressionGroupsplotNhoodGraphplotNhoodGraphDAplotNhoodGroupsplotNhoodMAplotNhoodSizeHistSatterthwaite_dfshowtestDiffExptestNhoods

Dependencies:abindassortheadbase64encbeachmatbeeswarmBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularcachemclicodetoolscowplotcpp11DelayedArraydplyredgeRfarverfastmapformatRfutile.loggerfutile.optionsgenericsGenomicRangesggbeeswarmggforceggplot2ggraphggrepelgluegraphlayoutsgridExtragtablegtoolsigraphIRangesirlbaisobandjsonlitelabelinglambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisenumDerivpatchworkpillarpkgconfigpolyclippracmapurrrR6RColorBrewerRcppRcppArmadillorlangrsvdS4ArraysS4VectorsS7ScaledMatrixscalesSeqinfoSingleCellExperimentsnowSparseArraystatmodstringistringrSummarizedExperimentsystemfontstibbletidygraphtidyrtidyselecttweenrutf8vctrsviporviridisviridisLitewithrXVector

Differential abundance testing with Milo

Emma Dann, Mike Morgan

Rendered frommilo_demo.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2025-06-04
Started: 2020-10-13

Differential abundance testing with Milo - Mouse gastrulation example

Emma Dann, Mike Morgan

Rendered frommilo_gastrulation.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2024-04-29
Started: 2020-11-10

Mixed effect models for Milo DA testing

Mike Morgan

Rendered frommilo_glmm.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2025-06-04
Started: 2023-11-09

Making comparisons for differential abundance using contrasts

Mike Morgan

Rendered frommilo_contrasts.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2024-09-17
Started: 2022-02-01

Readme and manuals

Help Manual

Help pageTopics
The miloR packagemiloR-package
Add annotations from colData to DA testing resultsannotateNhoods
Build a graph from an input adjacency matrixbuildFromAdjacency
Build a k-nearest neighbour graphbuildGraph
Build an abstracted graph of neighbourhoods for visualizationbuildNhoodGraph
Calculate within neighbourhood distancescalcNhoodDistance
Average expression within neighbourhoodscalcNhoodExpression
Check for separation of count distributions by variablescheckSeparation
Compute the p-value for the fixed effect parameterscomputePvalue
Count cells in neighbourhoodscountCells
Identify post-hoc neighbourhood marker genesfindNhoodGroupMarkers
Identify post-hoc neighbourhood marker genesfindNhoodMarkers
GLMM parameter estimation using pseudo-likelihood with a custom covariance matrixfitGeneticPLGlmm
Perform differential abundance testing using a NB-generalised linear mixed modelfitGLMM
GLMM parameter estimation using pseudo-likelihoodfitPLGlmm
glmm control default valuesglmmControl.defaults
Control the spatial FDRgraphSpatialFDR
Group neighbourhoodsgroupNhoods
Construct the initial G matrixinitialiseG
Construct the full Z matrixinitializeFullZ
Define neighbourhoods on a graph (fast)makeNhoods
Compute the trace of a matrixmatrix.trace
The Milo constructorMilo Milo-class
Get and set methods for Milo objectsgraph graph,Milo-method graph<- graph<-,Milo-method Milo-methods nhoodAdjacency nhoodAdjacency,Milo-method nhoodAdjacency<- nhoodAdjacency<-,Milo-method nhoodCounts nhoodCounts,Milo-method nhoodCounts<- nhoodCounts<-,Milo-method nhoodDistances nhoodDistances,Milo-method nhoodDistances<- nhoodDistances<-,Milo-method nhoodExpression nhoodExpression,Milo-method nhoodExpression<- nhoodExpression<-,Milo-method nhoodGraph nhoodGraph,Milo-method nhoodGraph<- nhoodGraph<-,Milo-method nhoodIndex nhoodIndex,Milo-method nhoodIndex<- nhoodIndex<-,Milo-method nhoodReducedDim nhoodReducedDim,Milo-method nhoodReducedDim<- nhoodReducedDim<-,Milo-method nhoods nhoods,Milo-method nhoods<- nhoods<-,Milo-method show show,Milo-method
miloRmiloR
Visualize DA results as a beeswarm plotplotDAbeeswarm
Plot the number of cells in a neighbourhood per sample and conditionplotNhoodCounts
Visualize gene expression in neighbourhoodsplotNhoodExpressionDA plotNhoodExpressionGroups
Plot graph of neighbourhoodplotNhoodGraph
Plot Milo results on graph of neighbourhoodplotNhoodGraphDA
Plot graph of neighbourhoods coloring by nhoodGroupsplotNhoodGroups
Visualize DA results as an MAplotplotNhoodMA
Plot histogram of neighbourhood sizesplotNhoodSizeHist
Compute degrees of freedom using Satterthwaite methodSatterthwaite_df
sim_discretesim_discrete
sim_familysim_family
sim_nbglmmsim_nbglmm
Simulated linear trajectory datasim_trajectory
Perform post-hoc differential gene expression analysistestDiffExp
Perform differential neighbourhood abundance testingtestNhoods