SectorPlot {ggsector}

Draw sector for seurat object

Description

A better alternative to Seurat::DotPlot(). For more details, please type vignette("ggsector").

Usage

SectorPlot(
  object,
  features,
  features.level,
  assay,
  slot = c("data", "scale.data", "counts"),
  group.by,
  group.level,
  split.by,
  split.level,
  col_low = "blue",
  col_mid = "white",
  col_high = "red",
  col_midpoint,
  ...
)

Arguments

object

Seurat object

features

Input vector of genes list.

features.level

Levels of genes list.

assay

Specific assay to get data from or set data for; defaults to the default assay.

slot

Specific assay data to get or set.

group.by

Column of metadata to group the cells by, default is Idents().

group.level

Levels of group.

split.by

Column of metadata to split the cells by, default is NULL.

split.level

Levels of split vars.

col_low

Colours for low ends of the gradient.

col_mid

Colour for mid point.

col_high

Colours for high ends of the gradient.

col_midpoint

The midpoint (in data value) of the diverging scale.

...

Other arguments for ggplot2::facet_wrap(). Defaults to quantile(exp, 0.5)

Value

ggplot

Examples

## Download pbmc data from
# https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz
library(Seurat)
path <- paste0(tempdir(), "/pbmc3k.tar.gz")
file <- paste0(tempdir(), "/filtered_gene_bc_matrices/hg19")
download.file(
    "https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz",
    path
)
untar(path, exdir = tempdir())
pbmc.data <- Read10X(data.dir = file)
pbmc <- CreateSeuratObject(
    counts = pbmc.data,
    project = "pbmc3k",
    min.cells = 3,
    min.features = 200
)
pbmc <- NormalizeData(pbmc)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
pbmc <- ScaleData(pbmc, features = rownames(pbmc))
pbmc <- RunPCA(pbmc)
pbmc <- RunUMAP(pbmc, dim = 1:10)
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 1)
pbmc <- FindClusters(pbmc, resolution = 0.5)
markers <- tibble::tribble(
    ~type, ~marker,
    "Naive CD4+ T", "IL7R,CCR7",
    "CD14+ Mono", "CD14,LYZ",
    "Memory CD4+", "IL7R,S100A4",
    "B", "MS4A1",
    "CD8+ T", "CD8A",
    "FCGR3A+ Mono", "FCGR3A,MS4A7",
    "NK", "GNLY,NKG7",
    "DC", "FCER1A,CST3",
    "Platelet", "PPBP",
) %>%
    tidyr::separate_rows(marker, sep = ", *") %>%
    dplyr::distinct()

# Dotplot
DotPlot(pbmc, features = unique(markers$marker)) + coord_flip()

# contrast with DotPlot
SectorPlot(pbmc, markers$marker, features.level = unique(rev(markers$marker)))

SectorPlot(pbmc, markers$marker, group.by = "RNA_snn_res.1")

# split plot
# Assume a variable 'day', expressed as the number of days of cell development.
set.seed(1)
pbmc[["day"]] <- sample(1:3, ncol(pbmc), TRUE)
SectorPlot(pbmc, markers$marker, group.by = "RNA_snn_res.0.5", split.by = "day")
SectorPlot(
    pbmc, markers$marker,
    group.by = "day", split.by = "RNA_snn_res.0.5", nrow = 1
)