{"id":16320089,"url":"https://github.com/helenalc/muscat","last_synced_at":"2025-05-15T13:02:34.695Z","repository":{"id":37103196,"uuid":"155884999","full_name":"HelenaLC/muscat","owner":"HelenaLC","description":"Multi-sample multi-group scRNA-seq analysis 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src=\"inst/extdata/muscat.png\" width=\"200\" align=\"right\"/\u003e \n\n**`muscat` (**Mu**lti-sample **mu**lti-group **sc**RNA-seq **a**nalysis **t**ools )**\n\n...provides methods for *Differential State* (DS) analyses in scRNA-seq data  \nwith multiple samples, groups, and (cell)-subpopulations, as elaborated in:\n\n\u003e Crowell HL, Soneson C\\*, Germain P-L\\*,  \nCalini D, Collin L, Raposo C, Malhotra D \u0026 Robinson MD:  \n\"*muscat* detects subpopulation-specific state transitions from  \nmulti-sample multi-condition single-cell transcriptomics data\"  \n*Nature Communications* **11**, 6077 (2020)  \n[DOI: 10.1038/s41467-020-19894-4](https://doi.org/10.1038/s41467-020-19894-4)\n\n*These authors contributed equally.\n\n### installation\n\n`muscat` is available through Bioconductor, and\ncan be installed using the following commands:\n\n```r\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n    install.packages(\"BiocManager\")\nBiocManager::install(\"muscat\")\n```\n\n### quick guide\n\nLet `sce` be a [`SingleCellExperiment`](https://www.bioconductor.org/packages/SingleCellExperiment.html) object with cell metadata (`colData`) columns\n\n1. `\"sample_id\"` specifying unique sample identifiers (e.g., PeterPan1, Nautilus7, ...)\n2. `\"group_id\"` specifying each sample's experimental condition (e.g., reference/stimulated, healthy/diseased, ...)\n3. `\"cluster_id\"` specifying subpopulation (cluster) assignments (e.g., B cells, dendritic cells, ...)\n\nAggregation-based methods come down to the following simple commands: \n\n```r\n# compute pseudobulks (sum of counts)\npb \u003c- aggregateData(sce, \n    assay = \"counts\", fun = \"sum\",\n    by = c(\"cluster_id\", \"sample_id\"))\n    \n# run pseudobulk (aggregation-based) DS analysis\nds_pb \u003c- pbDS(pb, method = \"edgeR\")\n```\n\nMixed models can be run directly on cell-level measurements, e.g.:\n\n```r\nds_mm \u003c- mmDS(sce, method = \"dream\")\n```\n\nFor details, please see the package vignettes.\n\n### differential detection\n\n`muscat` also supports testing for differential detection as proposed in \n\n\u003e Gilis J, Perin L, Malfait M, Van den Berge K,  \nAssefa AT, Verbist B, Risso D, and Clement L:  \nDifferential detection workflows for  \nmulti-sample single-cell RNA-seq data.  \n*bioRxiv* (2023). [DOI: 10.1101/2023.12.17.572043](https://doi.org/10.1101/2023.12.17.572043)\n\nKey alterations to the commands above are highlighted below (!!!), \nhowever, we recommend users consult the corresponding publication \nand package vignette for more details.\n\n```r\n# sum binarized counts\npb \u003c- aggregateData(sce, \n    assay = \"counts\", \n    fun = \"num.detected\", # !!!\n    by = c(\"cluster_id\", \"sample_id\"))\n# test for differential detection\ndd \u003c- pbDD(pb) # or..\ndd \u003c- pbDS(pb, method = \"DD\")\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhelenalc%2Fmuscat","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhelenalc%2Fmuscat","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhelenalc%2Fmuscat/lists"}