Package: miloR 2.9.1

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_2.9.1.tar.gz
miloR_2.9.1.zip(r-4.7)miloR_2.9.1.zip(r-4.6)miloR_2.9.1.zip(r-4.5)
miloR_2.9.1.tgz(r-4.6-x86_64)miloR_2.9.1.tgz(r-4.6-arm64)miloR_2.9.1.tgz(r-4.5-x86_64)miloR_2.9.1.tgz(r-4.5-arm64)
miloR_2.9.1.tar.gz(r-4.7-arm64)miloR_2.9.1.tar.gz(r-4.7-x86_64)miloR_2.9.1.tar.gz(r-4.6-arm64)miloR_2.9.1.tar.gz(r-4.6-x86_64)
miloR_2.7.1.tgz(r-4.5-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
miloR/json (API)

# Install 'miloR' in R:
install.packages('miloR', repos = c('https://bioc.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

11.29 score 436 stars 2 packages 664 scripts 54 exports 92 dependencies

Last updated from:ff744bbb5d. Checks:1 WARNING, 11 NOTE, 1 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING365
linux-devel-arm64NOTE534
linux-devel-x86_64NOTE662
source / vignettesOK1033
linux-release-arm64NOTE528
linux-release-x86_64NOTE691
macos-release-arm64NOTE482
macos-release-x86_64NOTE998
macos-oldrel-arm64NOTE494
macos-oldrel-x86_64NOTE709
windows-develNOTE1824
windows-releaseNOTE1763
windows-oldrelNOTE1696
wasm-releaseFAIL1168

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
Introduction | Load data | Pre-processing | Create a Milo object | From SingleCellExperiment object | From AnnData object (.h5ad) | From Seurat object | Construct KNN graph | 1. Defining representative neighbourhoods | Counting cells in neighbourhoods | Differential abundance testing | Visualize neighbourhoods displaying DA
Rendered frommilo_demo.Rmdusingknitr::rmarkdown

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

Mixed effect models for Milo DA testing
Introduction | Load data | Data processing and normalisation | Define cell neighbourhoods | Demonstrating the GLMM syntax | A note on when to use GLMM vs. GLM
Rendered frommilo_glmm.Rmdusingknitr::rmarkdown

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

Making comparisons for differential abundance using contrasts
Introduction | Load data | Define cell neighbourhoods | Differential abundance testing with contrasts
Rendered frommilo_contrasts.Rmdusingknitr::rmarkdown

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

Differential abundance testing with Milo - Mouse gastrulation example
Load data | Visualize the data | Differential abundance testing | Create a Milo object | Construct KNN graph | Defining representative neighbourhoods on the KNN graph | Counting cells in neighbourhoods | Defining experimental design | Computing neighbourhood connectivity | Testing | Inspecting DA testing results | Finding markers of DA populations | Custom grouping | Automatic grouping of neighbourhoods | Finding gene signatures for neighbourhoods | Visualize detected markers | DGE testing within a group
Rendered frommilo_gastrulation.Rmdusingknitr::rmarkdown

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

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