knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", fig.align = "center", out.width = "75%" )
scbp is a collection of utility functions for single cell analyses. Mostly plotting and thin wrapper functions extending Seurat
utilities.
remotes::install_github("rnabioco/scbp")
library(scbp) library(Seurat) so <- get_example_data() plot_tsne(so, c("groups", "CST3"))
plot_tsne(so, c("groups", "CST3"), group = "letter.idents")
plot_tsne(so, "RNA_snn_res.1", label_text = TRUE, # show group labels minimal_theme = TRUE, # no axes or legend .cols = palette_OkabeIto) # use other colors
Wrappers for common projections:
plot_feature()
plot_umap()
plot_pca()
plot_tsne()
plot_harmony()
Generate heatmaps with annotations for meta.data
variables (or plot average per group).
genes <- VariableFeatures(so) plot_heatmap(so, features = genes, group = "RNA_snn_res.1", annotations = c("nCount_RNA", "letter.idents"), average = FALSE)
genes <- VariableFeatures(so) plot_heatmap(so, features = genes, group = "RNA_snn_res.1", annotations = c("nCount_RNA", "letter.idents"), average = TRUE)
Make violin plots
plot_violins(so, group = "RNA_snn_res.1", features = VariableFeatures(so)[1:10], split_by = "groups")
get_metadata(so)
plot_cell_proportions(so, "groups", "RNA_snn_res.1")
plot_cell_proportions(so, "groups", "RNA_snn_res.1", facet_by = "letter.idents")
plot_cell_proportions(so, "groups", "RNA_snn_res.1", facet_by = "letter.idents", summary_boxplot = TRUE)
so <- calc_diversity(so, sample_id = "letter.idents", group_id = "RNA_snn_res.1") plot_tsne(so, "entropy")
Wrappers around UCSC cellbrowser generation routines. Uses data.table fwrite to speed up generating files.
make_cellbrowser(so, column_list = c("orig.ident", "groups"), project = "pbmcs", primary_color_palette = RColorbrewer::brewer.pal("Paired"), secondary_color_palette = scbp::palette_OkabeIto, secondary_cols = "groups", outdir = "cell_browser", marker_file = "path/to/presto_or_seurat_marker_file.tsv", ident = "groups", embeddings = c("pca"), config = list(priority = 4, # options for cellbrowser.conf radius = 5, tags = "10x"), description = list( # options for summary.html title = "Example cellbrowser", description = "An informative description of this dataset" ) build_cellbrowser(dataset_paths = "pbmcs/cell_browser/cellbrowser.conf", outdir = file.path(cb_outdir, "pbmc_browser"))
preprocess_bustools
read in bustools output and process with DropletUtils::emptyDrops()
plot_bc()
plot barcode distribution generated by DropletUtils::barcodeRanks()
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