Milo的R包实现，用于测试KNN图中的差异丰度_R_下.zip

# miloR _Milo_ is a method for differential abundance analysis on KNN graph from single-cell datasets. For more details, read [our manuscript](https://doi.org/10.1038/s41587-021-01033-z). 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)._ <p align="center"> <img src="docs/milo_schematic.png" width="500"> </p> [](https://travis-ci.com/MarioniLab/miloR) [](https://codecov.io/gh/MarioniLab/miloR) [](https://github.com/MarioniLab/miloR/actions/workflows/RCMD_check.yml) ### Installation ``` ## 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") ``` ### Tutorials 1. [Basic Milo example on simulated dataset](https://bioconductor.org/packages/release/bioc/vignettes/miloR/inst/doc/milo_demo.html) 2. [Milo example on mouse gastrulation dataset](https://rawcdn.githack.com/MarioniLab/miloR/7c7f906b94a73e62e36e095ddb3e3567b414144e/vignettes/milo_gastrulation.html#5_Finding_markers_of_DA_populations): this includes a demo for downstream analysis functions. 3. [Integrating miloR in scanpy/anndata workflow](https://github.com/MarioniLab/milo_analysis_2020/blob/main/notebooks/milo_in_python.ipynb) (see also [`milopy`](https://github.com/emdann/milopy) for a full workflow in python) 4. [Specifying contrasts of interest for differential abundance testing with Milo](https://bioconductor.org/packages/release/bioc/vignettes/miloR/inst/doc/milo_contrasts.html) ### Example work flow An example of the `Milo` work flow to get started: ```{r} data(sim_trajectory) milo.meta <- sim_trajectory$meta milo.obj <- Milo(sim_trajectory$SCE) milo.obj ``` Build a graph and neighbourhoods. ```{r} 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. ```{r} 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) ``` ### Support For any question, feature request or bug report please create a new issue in this repository. If you have an error or code-based query, please provide the executed code and the preceding code from the point of constructing the `Milo` object, along with the output of your `sessionInfo()` - this will help us immeasurably to diagnose the issue. ### Contributions 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.