Dot plot adapted from Seurat:::DotPlot, see ?Seurat:::DotPlot for details
Dot plot adapted from Seurat:::DotPlot, see ?Seurat:::DotPlot for details
dotPlot(
markers,
count.matrix,
cell.groups,
marker.colour = "black",
cluster.colour = "black",
xlab = "Marker",
ylab = "Cluster",
n.cores = 1,
text.angle = 45,
gene.order = NULL,
cols = c("blue", "red"),
col.min = -2.5,
col.max = 2.5,
dot.min = 0,
dot.scale = 6,
scale.by = "radius",
scale.min = NA,
scale.max = NA,
verbose = TRUE,
...
)markers |
Vector of gene markers to plot |
count.matrix |
Merged count matrix, cells in rows and genes in columns |
cell.groups |
Named factor containing cell groups (clusters) and cell names as names |
marker.colour |
Character or numeric vector (default="black") |
cluster.colour |
Character or numeric vector (default="black") |
xlab |
string X-axis title (default="Marker") |
ylab |
string Y-axis title (default="Cluster") |
n.cores |
integer Number of cores (default=1) |
text.angle |
numeric Angle of text displayed (default=45) |
gene.order |
Either factor of genes passed to dplyr::mutate(levels=gene.order), or a boolean. (default=NULL) If TRUE, gene.order is set to the unique markers. If FALSE, gene.order is set to NULL. If NULL, the argument is ignored. |
cols |
Colors to plot (default=c("blue", "red")). The name of a palette from 'RColorBrewer::brewer.pal.info', a pair of colors defining a gradient, or 3+ colors defining multiple gradients (if 'split.by' is set). |
col.min |
numeric Minimum scaled average expression threshold (default=-2.5). Everything smaller will be set to this. |
col.max |
numeric Maximum scaled average expression threshold (default=2.5). Everything larger will be set to this. |
dot.min |
numeric The fraction of cells at which to draw the smallest dot (default=0). All cell groups with less than this expressing the given gene will have no dot drawn. |
dot.scale |
numeric Scale the size of the points, similar to cex (default=6) |
scale.by |
string Scale the size of the points by 'size' or by 'radius' (default="radius") |
scale.min |
numeric Set lower limit for scaling, use NA for default (default=NA) |
scale.max |
numeric Set upper limit for scaling, use NA for default (default=NA) |
verbose |
boolean Verbose output (default=TRUE) |
... |
Additional inputs passed to sccore::plapply(), see man for description. |
ggplot2 object
library(dplyr)
## Create merged count matrix
## In this example, cms is a list of count matrices from, e.g., Cellranger count,
## where cells are in columns and genes in rows
## cm <- sccore:::mergeCountMatrices(cms, transposed = FALSE) %>% Matrix::t()
## If coming from Conos, this can be extracted like so
## cm <- conos.obj$getJointCountMatrix(raw = FALSE) # Either normalized or raw values can be used
## Here, we create a random sparse matrix
cm <- Matrix::rsparsematrix(30,3,0.5) %>% abs(.) %>%
`dimnames<-`(list(1:30,c("gene1","gene2","gene3")))
## Create marker vector
markers <- c("gene1","gene2","gene3")
## Additionally, color vectors can be included.
## These should have the same length as the input (markers, cell groups)
## Otherwise, they are recycled
col.markers <- c("black","black","red") # or c(1,1,2)
col.clusters <- c("black","red","black") # or c(1,2,1)
## Create annotation vector
annotation <- c(rep("cluster1",10),rep("cluster2",10),rep("cluster3",10)) %>%
factor() %>% setNames(1:30)
## Plot. Here, the expression colours range from gray (low expression) to purple (high expression)
sccore:::dotPlot(markers = markers, count.matrix = cm, cell.groups = annotation,
marker.colour = col.markers, cluster.colour = col.clusters, cols=c("gray","purple"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.