Milo is a method for differential abundance analysis on KNN graph from single-cell datasets. For more details, read our manuscript. If you use Milo in your study, please cite Dann, E., Henderson, N.C., Teichmann, S.A. et al. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat Biotechnol (2021).
## Milo is available from Bioconductor (preferred stable installation) if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("miloR") ## Install development version devtools::install_github("MarioniLab/miloR", ref="devel")
milopyfor a full workflow in python)
An example of the
Milo work flow to get started:
data(sim_trajectory) milo.meta <- sim_trajectory$meta milo.obj <- Milo(sim_trajectory$SCE) milo.obj
Build a graph and neighbourhoods.
milo.obj <- buildGraph(milo.obj, k=20, d=30) milo.obj <- makeNhoods(milo.obj, k=20, d=30, refined=TRUE, prop=0.2)
Calculate distances, count cells according to an experimental design and perform DA testing.
milo.obj <- calcNhoodDistance(milo.obj, d=30) milo.obj <- countCells(milo.obj, samples="Sample", meta.data=milo.meta) milo.design <- as.data.frame(xtabs(~ Condition + Sample, data=milo.meta)) milo.design <- milo.design[milo.design$Freq > 0, ] rownames(milo.design) <- milo.design$Sample milo.design <- milo.design[colnames(nhoodCounts(milo.obj)),] milo.res <- testNhoods(milo.obj, design=~Condition, design.df=milo.design) head(milo.res)
For any question, feature request or bug report please create a new issue in this repository.
We welcome contributions and suggestions from the community (though we may not take them onboard if they don't align with our development roadmap - please don't be offended). Please submit the initial idea as an issue, which we will discuss and ask for refinements/clarifications. If we approve the idea, then please open a pull request onto the devel branch, from which we will begin a review process. To smooth the process, please note that code changes must be backwards compatible, and must include all relevant unit tests.
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