bug fixes (22344406) · Commits · github_fork / Plot1cell

R/plot_circlize.R

+10 −3

Original line number	Diff line number	Diff line
		@@ -161,6 +161,8 @@ prepare_circlize_data <- function(
		#' @param pt.size Point size of the graph
		#' @param kde2d.n Number of grid points in each direction. A kde2d parameter
		#' @param contour.nlevels Total number of levels in contour
		#' @param col.use Colors used to label the cell type
		#' @param repel Whether or not to repel the cell type names on umap
		#' @return Return a circlize plot
		#' @export
		plot_circlize <- function(
		@@ -171,7 +173,8 @@ plot_circlize <- function(
		kde2d.n = 1000,
		contour.nlevels = 100,
		bg.color='#F9F2E4',
		col.use=NULL
		col.use=NULL,
		repel=FALSE
		) {
		data_plot %>%
		dplyr::group_by(Cluster) %>%
		@@ -207,9 +210,13 @@ plot_circlize <- function(
		points(data_plot$x,data_plot$y, pch = 19, col = alpha(data_plot$Colors,0.2), cex = pt.size);
		contour(z, drawlabels=F, nlevels= 100, levels = contour.levels,col = '#ae9c76', add=TRUE)
		if(do.label){
		if(repel){
		textplot(x=centers$x, y=centers$y, words = centers$Cluster,cex = 0.8, new = F,show.lines=F)
		} else {
		text(centers$x,centers$y, labels=centers$Cluster, cex = 0.8, col = 'black')
		}
		}
		}

		#' Add tracks to the circlize plot
		#'

README.md

+4 −4

Original line number	Diff line number	Diff line
		@@ -16,7 +16,7 @@ iri.integrated <- Install.example()

		# Please note that this Seurat object is just for demo purpose and
		# is not exactly the same as we published on PNAS.
		# It take about 2 hours to run in a linux server with 500GB RAM and 32 CPU cores.
		# It takes about 2 hours to run in a linux server with 500GB RAM and 32 CPU cores.
		# You can skip this step and use your own Seurat object instead
		```

		@@ -29,12 +29,12 @@ colnames(iri.integrated@meta.data)
		circ_data <- prepare_circlize_data(iri.integrated, scale = 0.8 )
		set.seed(1234)
		cluster_colors<-rand_color(length(levels(iri.integrated)))
		group_colors<-rand_color(length(table(iri.integrated$Group)))
		rep_colors<-group_colors<-rand_color(length(table(iri.integrated$orig.ident)))
		group_colors<-rand_color(length(names(table(iri.integrated$Group))))
		rep_colors<-rand_color(length(names(table(iri.integrated$orig.ident))))

		###plot and save figures
		png(filename = 'circlize_plot.png', width = 6, height = 6,units = 'in', res = 300)
		plot_circlize(circ_data,do.label = T, pt.size = 0.01, col.use = cluster_colors ,bg.color = 'white', kde2d.n = 200)
		plot_circlize(circ_data,do.label = T, pt.size = 0.01, col.use = cluster_colors ,bg.color = 'white', kde2d.n = 200, repel = T)
		add_track(circ_data, group = "Group", colors = group_colors) ## can change it to one of the columns in the meta data of your seurat object
		add_track(circ_data, group = "orig.ident",colors = rep_colors) ## can change it to one of the columns in the meta data of your seurat object
		dev.off()

data/circlize_plot.png

+114 KiB (941 KiB)

Loading image diff...

man/plot_circlize.Rd

+6 −1

Original line number	Diff line number	Diff line
		@@ -12,7 +12,8 @@ plot_circlize(
		kde2d.n = 1000,
		contour.nlevels = 100,
		bg.color = "#F9F2E4",
		col.use = NULL
		col.use = NULL,
		repel = FALSE
		)
		}
		\arguments{
		@@ -29,6 +30,10 @@ plot_circlize(
		\item{contour.nlevels}{Total number of levels in contour}

		\item{bg.color}{Canvas background color}

		\item{col.use}{Colors used to label the cell type}

		\item{repel}{Whether or not to repel the cell type names on umap}
		}
		\value{
		Return a circlize plot

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