fig.size <- function (height, width) {
options(repr.plot.height = height, repr.plot.width = width)
}
# Colors for PBMCs
pbmc_colors = c("B" = "#66C2A5",
"DC" = "#FC8D62",
"HSC" = "#8DA0CB",
"MK" = "#E78AC3",
"Mono_CD14" = "#A6D854",
"Mono_CD16" = "#f2ec72",
"NK" = "#62AAEA",
"T_CD4" = "#D1C656",
"T_CD8" = "#968763")
# Colors for pancreas
celltype.colors = c('alpha'="#ed2bb1",
'beta'="#239eb3",
'gamma'="#d1bfec",
'delta'= "#FF6347",
'stellate'="#11e38c",
'immune'="#812050",
'ductal'="#b2d27a",
'endothelial'="#4e2da6",
'acinar'="#f6bb86",
'schwann'="#115d52",
'epsilon'="#a1def0",
'mast'="#8fec2f")
# Colors for pancreas query donors (Baron et al., 2016)
querydonor.colors = c('human1' = '#b9dbf0',
'human2' = '#77a1ba',
'human3' = '#6c7ca8',
'human4' = '#364261',
'mouse1' = '#e68c8c',
'mouse2' = '#b35757')
# Colors for fetal liver hematopoeisis
group.colors = c( 'B cell'='#f2bd80',
'DC precursor'='#1d6d1f',
'DC1'='#8c3ba0',
'DC2'='#6533ed',
'Early Erythroid'='#83e3f0',
'Early lymphoid/T'='#fd5917',
'Endothelial cell'='#4f8c9d',
'Fibroblast'='#eb1fcb',
'Hepatocyte'='#f5cdaf',
'HSC_MPP'='#9698dc',
'ILC precursor'='#20f53d',
'Kupffer Cell'='#f283e3',
'Late Erythroid'='#ffb2be',
'Mast cell'='#f3d426',
'Megakaryocyte'='#5ebf72',
'MEMP'='#a67649',
'Mid Erythroid'='#2f5bb1',
'Mono-Mac'='#90a479',
'Monocyte'='#f6932e',
'Monocyte precursor'='#d59e9a',
'Neut-myeloid prog.'='#caf243',
'NK'='#38b5fc',
'pDC precursor'='#c82565',
'Pre pro B cell'='#d6061a',
'pre-B cell'='#e36f6f',
'pro-B cell'='#1dfee1',
'VCAM1+ EI macro.'='#506356',
'centroid' ='black')
# Custom ordering to match original author publication ordering of states
group.ordering = c("HSC_MPP", "Pre pro B cell", 'pro-B cell', 'pre-B cell', 'B cell',
'ILC precursor', 'Early lymphoid/T', 'NK', 'Neut-myeloid prog.',
'pDC precursor','DC precursor', 'DC1', 'DC2', 'Monocyte precursor', 'Monocyte',
'Mono-Mac', 'Kupffer Cell', 'VCAM1+ EI macro.', 'MEMP', 'Mast cell',
'Megakaryocyte', 'Early Erythroid', 'Mid Erythroid', 'Late Erythroid',
'Endothelial cell', 'Fibroblast', 'Hepatocyte')
#' Basic function to plot cells, colored and faceted by metadata variables
#'
#' @param metadata metadata, with UMAP labels in UMAP1 and UMAP2 slots
#' @param title Plot title
#' @param color.by metadata column name for phenotype labels
#' @param facet.by metadata column name for faceting
#' @param color.mapping custom color mapping
#' @param show.legend Show cell type legend
plotBasic = function(umap_labels, # metadata, with UMAP labels in UMAP1 and UMAP2 slots
title = 'Query', # Plot title
color.by = 'cell_type', # metadata column name for coloring
facet.by = NULL, # (optional) metadata column name for faceting
color.mapping = NULL, # custom color mapping
legend.position = 'right') { # Show cell type legend
p = umap_labels %>%
dplyr::sample_frac(1L) %>% # permute rows randomly
ggplot(aes(x = UMAP1, y = UMAP2)) +
geom_point_rast(aes(col = get(color.by)), size = 0.3, stroke = 0.2, shape = 16)
if (!is.null(color.mapping)) { p = p + scale_color_manual(values = color.mapping) }
# Default formatting
p = p + theme_bw() +
labs(title = title, color = color.by) +
theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position=legend.position) +
theme(legend.text = element_text(size=8), legend.title=element_text(size=12)) +
guides(colour = guide_legend(override.aes = list(size = 4))) + guides(alpha = 'none')
if(!is.null(facet.by)) {
p = p + facet_wrap(~get(facet.by)) +
theme(strip.text.x = element_text(size = 12)) }
return(p)
}
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