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<img src="inst/extdata/muscat.png" width="200" align="right"/> `muscat` (**Mu**lti-sample **mu**lti-group **sc**RNA-seq **a**nalysis **t**ools ) provides various methods for *Differential State* (DS) analyses in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, as elaborated in our preprint: > Crowell HL, Soneson C\*, Germain P-L\*, Calini D, Collin L, Raposo C, Malhotra D & Robinson MD: On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data. *bioRxiv* **713412** (July, 2019). doi: [10.1101/713412]( *These authors contributed equally. *** **`muscat` is still work in progress. Any constructive feedback (feature requests, comments on documentation, issues or bug reports) is appreciated; therefor, please file a issue on GitHub rather then emailing, so that others may benifit from answers and discussions!** *** ### Installation `muscat` can be installed from GitHub using the following commands: ```{r} if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") # for the stable release branch: devtools::install_github("HelenaLC/muscat", ref = "master") # for the current development version: devtools::install_github("HelenaLC/muscat", ref = "devel") ``` ### Quick guide Let `sce` be a [`SingleCellExperiment`]( object with cell metadata (`colData`) columns 1. `"sample_id"` specifying unique sample identifiers (e.g., PeterPan1, Nautilus7, ...) 2. `"group_id"` specifying each sample's experimental condition (e.g., reference/stimulated, healthy/diseased, ...) 3. `"cluster_id"` specifying subpopulation (cluster) assignments (e.g., B cells, dendritic cells, ...) Aggregation-based methods come down to the following simple commands: ```{r} # compute pseudobulks (sum of counts) pb <- aggregateData(sce, assay = "counts", fun = "sum", by = c("cluster_id", "sample_id")) # run pseudobulk (aggregation-based) DS analysis ds_pb <- pbDS(pb, method = "edgeR") ``` Mixed models can be run directly on cell-level measurements, e.g.: ```{r} ds_mm <- mmDS(sce, method = "dream") ``` For details, please see the package vignette.