enrich_genesets.RdRuns geneset tests and store in BOWER class.
# S3 method for list enrich_genesets( list, bower, core = FALSE, gene_symbol = "X1", logfoldchanges = "logfoldchanges", pvals = "pvals", remove_mito_ribo = TRUE, minSize = 0, maxSize = 1000, ... ) # S3 method for Seurat enrich_genesets( sce, bower, groupby = NULL, core = FALSE, standardize = TRUE, mode = c("AUCell", "Seurat", "scanpy"), sce_assay = "logcounts", seurat_assay = "RNA", ncpus = NULL, aucMaxRank_pct = 5, ... ) # S3 method for SingleCellExperiment enrich_genesets( sce, bower, groupby = NULL, core = FALSE, standardize = TRUE, mode = c("AUCell", "Seurat", "scanpy"), sce_assay = "logcounts", seurat_assay = "RNA", ncpus = NULL, aucMaxRank_pct = 5, ... )
| list | list containing differentially expressed gene testing results in a data frame. |
|---|---|
| bower | Processed BOWER object. |
| core | boolean. Whether or not to use the coregenes of genesets slot. Default is FALSE (use genesets). |
| gene_symbol | column name for gene_symbol for gsea. |
| logfoldchanges | column name for logfoldchanges for gsea. |
| pvals | column name for pvals for gsea. |
| remove_mito_ribo | boolean. whether or not to remove mitochondial and ribosomal genes from consideration for gsea. Default is TRUE. |
| minSize | minimum geneset size for gsea. Default is 0. |
| maxSize | maximum geneset size for gsea. Default is 1000. |
| ... | passed to fgsea::fgsea, AUCell::AUCell_buildRankings or Seurat::AddModuleScore |
| sce | a single cell object in the format of a Seurat object or SingleCellExperiment object. |
| groupby | Column name in the meta.data/colData of the single cell objects specifying the group to average the enrichment score. If not specified, not cluster average will be calculated. |
| standardize | whether or not to standardize mean enrichment values to 0 to 1. Only used if groupby is not NULL. |
| mode | choice of enrichment test to perform. |
| sce_assay | name of assay in SingleCellExperiment object. |
| seurat_assay | name of assay in Seurat object. |
| ncpus | number of cores used for parallelizing geneset testing. |
| aucMaxRank_pct | percentage to use for aucMaxRank in AUCell::AUCell_calcAUC. |
| nperm | number of permuation iterations for gsea. Default is 10000. |
Returns a dataframe of average gene set scores.
library(ktplots) #> Loading required package: ggplot2 data(kidneyimmune) gmt_file <- system.file("extdata", "h.all.v7.4.symbols.gmt", package = "bowerbird") bwr <- bower(gmt_file) bwr <- snn_graph(bwr) #> Loading required namespace: FNN bwr <- find_clusters(bwr) bwr <- summarize_clusters(bwr, ncpus = 1) #> Downloading udpipe model from https://raw.githubusercontent.com/jwijffels/udpipe.models.ud.2.5/master/inst/udpipe-ud-2.5-191206/english-ewt-ud-2.5-191206.udpipe to ~/Library/Application Support/bowerbird/english-ewt-ud-2.5-191206.udpipe #> - This model has been trained on version 2.5 of data from https://universaldependencies.org #> - The model is distributed under the CC-BY-SA-NC license: https://creativecommons.org/licenses/by-nc-sa/4.0 #> - Visit https://github.com/jwijffels/udpipe.models.ud.2.5 for model license details. #> - For a list of all models and their licenses (most models you can download with this package have either a CC-BY-SA or a CC-BY-SA-NC license) read the documentation at ?udpipe_download_model. For building your own models: visit the documentation by typing vignette('udpipe-train', package = 'udpipe') #> Downloading finished, model stored at '~/Library/Application Support/bowerbird/english-ewt-ud-2.5-191206.udpipe' bwr <- enrich_genesets(kidneyimmune, bwr, groupby = 'celltype', ncpus = 1) #> Loading required package: Seurat #> Registered S3 method overwritten by 'spatstat.geom': #> method from #> print.boxx cli #> Attaching SeuratObject #> Loading required namespace: AUCell bwr #> BOWER class #> number of genesets: 50 #> genesets kNN Graph: #> IGRAPH b91852b UNW- 50 124 -- #> + attr: name (v/c), cluster (v/n), geneset_size (v/n), terms (v/c), #> | labels (v/c), weight (e/n) #> + edges from b91852b (vertex names): #> [1] HALLMARK_TNFA_SIGNALING_VIA_NFKB--HALLMARK_HYPOXIA #> [2] HALLMARK_TNFA_SIGNALING_VIA_NFKB--HALLMARK_TGF_BETA_SIGNALING #> [3] HALLMARK_TNFA_SIGNALING_VIA_NFKB--HALLMARK_IL6_JAK_STAT3_SIGNALING #> [4] HALLMARK_TNFA_SIGNALING_VIA_NFKB--HALLMARK_APOPTOSIS #> [5] HALLMARK_TNFA_SIGNALING_VIA_NFKB--HALLMARK_MYOGENESIS #> [6] HALLMARK_TNFA_SIGNALING_VIA_NFKB--HALLMARK_COMPLEMENT #> + ... omitted several edges #> number of geneset clusters: 9 #> Core genes: #> First six genes shown #> XENOBIOTIC METABOLISM : LIFR DNAJB9 CD36 ACOX1 IDH1 ECH1 ... #> E2F TARGETS : SAC3D1 KIF11 KIF23 RACGAP1 NUMA1 KIF2C ... #> ESTROGEN RESPONSE EARLY : JAG1 CTNNB1 GNAI1 FDFT1 DHCR7 FASN ... #> APOPTOSIS : ATF3 IER3 BIRC3 JUN EGR3 IL1B ... #> INTERFERON ALPHA RESPONSE : MX1 ISG15 IFIT3 IFI44 IFI35 IRF7 ... #> HEDGEHOG SIGNALING : VEGFA VLDLR MYH9 ERO1A DDIT4 STC2 ... #> APICAL JUNCTION : EGFR ADAM10 CLTC AP2M1 ARF1 MAPK1 ... #> TGF BETA SIGNALING : TGFB1 PMEPA1 SERPINE1 ID2 THBS1 PPP1R15A ... #> IL6 JAK STAT3 SIGNALING : IL4R IFNGR1 IL1R2 IL3RA TNFRSF1B CSF1 ...