<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](https://doi.org/10.1101/713412)
*These authors contributed equally.
***
Let `sce` be a [`SingleCellExperiment`](https://www.bioconductor.org/packages/SingleCellExperiment.html) 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, see the package vignette.
