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--- a +++ b/TcellAnalysis/notebooks/COVID19_Tcell_UMAPs-Updated.Rmd @@ -0,0 +1,536 @@ +--- +title: "COVID19: T cell UMAPs - Updated" +output: html_notebook +--- + +Plotting of different UMAPs based on: + +* T cell specific MNN-corrected PCs +* Gender-regressed T cell specific MNN-corrected PCs + +```{r, warning=FALSE, message=FALSE} +library(ggplot2) +library(ggsci) +library(cowplot) +library(scattermore) +library(RColorBrewer) +library(reshape2) +library(colorspace) +library(dplyr) +library(ggthemes) +library(scales) +library(ggrepel) +library(viridis) +library(ggrastr) +``` + + + +```{r, warning=FALSE, message=FALSE} +all.meta <- read.table("~/Dropbox/COVID19/Data/Updated/COVID19_scMeta-data.tsv", + sep="\t", header=TRUE, stringsAsFactors=FALSE) +rownames(all.meta) <- all.meta$CellID + +tcell.annotations <- read.table("~/Dropbox/COVID19/Data/Updated/Tcell_annotations_ext.tsv", + sep="\t", header=TRUE, stringsAsFactors=FALSE) +all.meta <- merge(all.meta, tcell.annotations, by='CellID') +all.meta$Status_on_day_collection_summary[all.meta$Status_on_day_collection_summary %in% c("LPS_90mins", "LPS_10hours")] <- "LPS" +``` + + +```{r, warning=FALSE, message=FALSE} +# gender.umap <- read.table("~/Dropbox/COVID19/Data/Updated/Cambridge_Sanger_Newcastle_Tcell_UMAPs.tsv", +# sep="\t", header=TRUE, stringsAsFactors=FALSE) +``` + + +```{r, warning=FALSE, message=FALSE} +# tcell.harmony.umap <- read.table("~/Dropbox/COVID19/Data/Updated/Cambridge_Sanger_Newcastle-Tcell_harmony_UMAPs.tsv", +# sep="\t", header=TRUE, stringsAsFactors=FALSE) +tcell.harmony.umap <- read.table("~/Dropbox/COVID19/Data/Updated/Cambridge_Sanger_Newcastle-Tcell_harmony-Gender_UMAPs.tsv", + sep="\t", header=TRUE, stringsAsFactors=FALSE) +colnames(tcell.harmony.umap) <- c("Harmony.UMAP1", "Harmony.UMAP2", "CellID") +``` + + +```{r, warning=FALSE, message=FALSE} +# harmony.umap <- read.table("~/Dropbox/COVID19/Data/Updated/COVID_Harmony_UMAP_OnlyT_TonlyHVGs.csv", +# sep=",", header=FALSE, stringsAsFactors=FALSE) +# colnames(harmony.umap) <- c("CellID", "Harmony.UMAP1", "Harmony.UMAP2") +``` + + +```{r} +cell.order <- c("CD4.Naive", "CD4.CM", "CD4.EM", "CD4.IL22", "CD4.Prolif", "CD4.Th1", "CD4.Th2", "CD4.Th17", "CD4.Tfh", + "Treg", "CD8.Naive", "CD8.Activated", "CD8.Prolif", "CD8.CM", "CD8.TE", "CD8.EM", "gdT", "MAIT", "NKT") + +all.qual.cols <- brewer.pal.info[brewer.pal.info$category %in% c("qual") & brewer.pal.info$colorblind, ] +col_vector = unlist(mapply(brewer.pal, all.qual.cols$maxcolors, rownames(all.qual.cols))) + +# cell.cols <- col_vector[c(1:7, 9:19)] +cell.cols <- col_vector[c(1:17, 19, 20)] +names(cell.cols) <- cell.order +``` + + + +```{r} +tcell.umap <- read.table("~/Dropbox/COVID19/Data/Updated/Cambridge_Sanger_Newcastle_Tcell_UMAPs.tsv", + sep="\t", header=TRUE, stringsAsFactors=FALSE) +# tcell.umap <- merge(tcell.umap, gender.umap, by='CellID') +tcell.umap <- merge(tcell.umap, tcell.harmony.umap, by='CellID', all.y=TRUE) +tcell.umap.merge <- merge(all.meta, tcell.umap, by='CellID') + + +# cell.order <- c("CD4.Naive", "CD4.CM", "CD4.EM", "CD4.IL22", "CD4.Th1", "CD4.Th2", "CD4.Th17", +# "Treg", "CD4.Tfh", "CD8.Naive", "CD8.TE", "CD8.EM", "gdT", "MAIT", "NKT") +# +# n.cells <- length(cell.order) +# cell.cols <- colorRampPalette(pal_futurama()(12))(n.cells) +# names(cell.cols) <- cell.order + +tcell.umap.merge$Sub.Annotation <- factor(tcell.umap.merge$Sub.Annotation, + levels=cell.order) + +group.cols <- c("#2ca02c", "#1f77b4", "#9467bd", "#fed976", "#fd8d3c", "#e31a1c", "#800026", "#252525") +names(group.cols) <- c("Healthy", "LPS", "Non_covid", "Asymptomatic", "Mild", "Moderate", "Severe", "Critical") +``` + + + +```{r, fig.height=4.35, fig.width=10.95} +ggplot(tcell.umap.merge, + aes(x=UMAP1, y=UMAP2)) + + geom_scattermore(data=tcell.umap.merge[, c("UMAP1", "UMAP2")], + colour='grey80', alpha=0.5) + + geom_scattermore(aes(colour=Site)) + + scale_colour_tron() + + facet_wrap(~Site) + + theme_cowplot() + + theme(strip.background=element_rect(colour='white', fill='white'), + strip.text=element_text(size=14)) + + guides(colour=guide_legend(title="Severity", override.aes = list(size=3))) + + # ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_UMAP-site.pdf", + # height=4.35, width=10.95, useDingbats=FALSE) + + NULL +``` + + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=UMAP1, y=UMAP2, colour=Sub.Annotation)) + + geom_scattermore() + + scale_colour_manual(values=cell.cols) + + theme_cowplot() + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + # ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_UMAP-annotation.pdf", + # height=3.55, width=5.15, useDingbats=FALSE) + + NULL +``` + +And the UMAP with gender regressed out. + +## Harmony UMAPs + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(aes(colour=Sub.Annotation)) + + scale_colour_manual(values=cell.cols) + + theme_cowplot() + + labs(x="UMAP1", y="UMAP2") + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP_annotation.pdf", + height=3.55, width=5.95, useDingbats=FALSE) + + NULL +``` + + + +```{r, fig.height=4.35, fig.width=10.95} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=tcell.umap.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.5) + + geom_scattermore(aes(colour=Site)) + + scale_colour_tron() + + facet_wrap(~Site) + + theme_cowplot() + + theme(strip.background=element_rect(colour='white', fill='white'), + strip.text=element_text(size=14)) + + guides(colour=guide_legend(title="Severity", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP_GenderRegress-site.pdf", + height=4.35, width=10.95, useDingbats=FALSE) + + NULL +``` + + + +```{r, fig.height=10.95, fig.width=10.95} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=tcell.umap.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.5) + + geom_scattermore(aes(colour=Sub.Annotation)) + + scale_colour_manual(values=cell.cols) + + theme_cowplot() + + facet_wrap(~Sub.Annotation) + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-annotation.pdf", + height=10.95, width=10.95, useDingbats=FALSE) + + NULL +``` + +Calculate the average UMAP position for each cluster. + +```{r, warning=FALSE, message=FALSE} +mean.umap.list <- list() + +for(i in seq_along(cell.order)){ + i.c <- cell.order[i] + i.dim1 <- mean(tcell.umap.merge$Harmony.UMAP1[tcell.umap.merge$Sub.Annotation %in% i.c]) + i.dim2 <- mean(tcell.umap.merge$Harmony.UMAP2[tcell.umap.merge$Sub.Annotation %in% i.c]) + mean.umap.list[[i.c]] <- data.frame("Sub.Annotation"=i.c, "Harmony.UMAP1"=i.dim1, "Harmony.UMAP2"=i.dim2) +} + +mean.umap.text <- do.call(rbind.data.frame, mean.umap.list) +``` + + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(aes(colour=Sub.Annotation)) + + geom_text(data=mean.umap.text, aes(label=Sub.Annotation)) + + scale_colour_manual(values=cell.cols) + + theme_cowplot() + + labs(x="UMAP1", y="UMAP2") + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + # ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP_annotation.pdf", + # height=3.55, width=5.95, useDingbats=FALSE) + + NULL +``` + +Only show the Th subsets. + +```{r, fig.height=7.55, fig.width=9.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=tcell.umap.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_point_rast(data=tcell.umap.merge[tcell.umap.merge$Sub.Annotation %in% c("CD4.Th1", "CD4.Th2", "CD4.Th17", "CD4.Tfh"), ], + aes(colour=Sub.Annotation), size=0.1, raster.dpi=300) + + scale_colour_manual(values=cell.cols) + + facet_wrap(~Sub.Annotation) + + theme_cowplot() + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-Th_annotation.pdf", + height=7.55, width=9.15, useDingbats=FALSE) + + NULL +``` + + + +## Different Harmony UMAPs + +Calculate the average UMAP position for each cluster. + +```{r, warning=FALSE, message=FALSE} +mean.umap.list <- list() + +for(i in seq_along(cell.order)){ + i.c <- cell.order[i] + i.dim1 <- mean(tcell.umap.merge$Harmony.UMAP1[tcell.umap.merge$Sub.Annotation %in% i.c]) + i.dim2 <- mean(tcell.umap.merge$Harmony.UMAP2[tcell.umap.merge$Sub.Annotation %in% i.c]) + mean.umap.list[[i.c]] <- data.frame("Sub.Annotation"=i.c, "Harmony.UMAP1"=i.dim1, "Harmony.UMAP2"=i.dim2) +} + +mean.umap.text <- do.call(rbind.data.frame, mean.umap.list) +``` + + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(aes(colour=Sub.Annotation)) + + geom_text(data=mean.umap.text, aes(label=Sub.Annotation)) + + scale_colour_manual(values=cell.cols) + + theme_cowplot() + + labs(x="UMAP1", y="UMAP2") + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + # ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP_annotation.pdf", + # height=3.55, width=5.95, useDingbats=FALSE) + + NULL +``` + +Only show the Th subsets. + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=tcell.umap.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_point_rast(data=tcell.umap.merge[tcell.umap.merge$Sub.Annotation %in% c("CD4.Th1", "CD4.Th2", "CD4.Th17", "CD4.Tfh"), ], + aes(colour=Sub.Annotation), size=0.1, raster.dpi=300) + + scale_colour_manual(values=cell.cols) + + theme_cowplot() + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-Th_annotation.pdf", + height=3.55, width=5.15, useDingbats=FALSE) + + NULL +``` + +Maybe show the density of a given label? + +```{r} +get_density <- function(x, y, ...) { + dens <- MASS::kde2d(x, y, ...) + ix <- findInterval(x, dens$x) + iy <- findInterval(y, dens$y) + ii <- cbind(ix, iy) + return(dens$z[ii]) +} + +dens.umap.list <- list() +loop.cells <- intersect(cell.order, unique(as.character(tcell.umap.merge$Sub.Annotation))) + +for(i in seq_along(loop.cells)){ + i.c <- loop.cells[i] + # change this to 1% of all cells + i.n <- ceiling(sum(tcell.umap.merge$Sub.Annotation %in% i.c)/100) + if(i.n < 100){ + i.n <- 100 + } + i.dens <- get_density(tcell.umap.merge$Harmony.UMAP1[tcell.umap.merge$Sub.Annotation %in% i.c], + tcell.umap.merge$Harmony.UMAP2[tcell.umap.merge$Sub.Annotation %in% i.c], n=i.n) + + dens.umap.list[[i.c]] <- data.frame("Sub.Annotation"=i.c, + "CellID"=tcell.umap.merge$CellID[tcell.umap.merge$Sub.Annotation %in% i.c], + "Dens"=i.dens) +} + +umap.dens <- do.call(rbind.data.frame, dens.umap.list) +umap.dens.merge <- merge(tcell.umap.merge, umap.dens, by=c('Sub.Annotation', 'CellID')) +``` + + +```{r, fig.height=2.55, fig.width=3.15} +ggplot(umap.dens.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=umap.dens.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_scattermore(data=umap.dens.merge[umap.dens.merge$Sub.Annotation %in% c("CD4.Tfh"), ], + aes(colour=Dens)) + + scale_colour_viridis() + + theme_cowplot() + + theme(legend.key.size = unit(0.2, "cm"), + legend.title=element_text(size=8), + legend.text=element_text(size=6)) + + guides(colour=guide_colourbar(title="CD4.Tfh density", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-Tfh_density.pdf", + height=2.55, width=2.95, useDingbats=FALSE) + + NULL +``` + +```{r, fig.height=2.55, fig.width=3.15} +ggplot(umap.dens.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=umap.dens.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_point_rast(data=umap.dens.merge[umap.dens.merge$Sub.Annotation %in% c("CD8.TE"), ], + aes(colour=Dens), size=0.1) + + scale_colour_viridis() + + theme_cowplot() + + theme(legend.key.size = unit(0.2, "cm"), + legend.title=element_text(size=8), + legend.text=element_text(size=6)) + + guides(colour=guide_colourbar(title="CD8.TE density", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-CD8TE_density.pdf", + height=2.55, width=2.95, useDingbats=FALSE) + + NULL +``` + + + + +```{r, fig.height=1.55, fig.width=1.95} +ggplot(umap.dens.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=umap.dens.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_point_rast(data=umap.dens.merge[umap.dens.merge$Sub.Annotation %in% c("CD4.Th1"), ], + aes(colour=Dens), size=0.1) + + scale_colour_viridis() + + theme_cowplot() + + labs(x="UMAP1", y="UMAP2") + + theme(aspect=1, + legend.key.size = unit(0.2, "cm"), + axis.text=element_blank(), + axis.title=element_blank(), + legend.title=element_text(size=8), + legend.text=element_blank()) + + guides(colour=guide_colourbar(title="CD4.Th1\ndensity", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-Th1_density.pdf", + height=1.55, width=1.95, useDingbats=FALSE) + + NULL +``` + + + +```{r, fig.height=1.55, fig.width=1.95} +ggplot(umap.dens.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=umap.dens.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_point_rast(data=umap.dens.merge[umap.dens.merge$Sub.Annotation %in% c("CD4.Th2"), ], + aes(colour=Dens), size=0.1) + + scale_colour_viridis() + + theme_cowplot() + + labs(x="UMAP1", y="UMAP2") + + theme(aspect=1, + legend.key.size = unit(0.2, "cm"), + axis.text=element_blank(), + axis.title=element_blank(), + legend.title=element_text(size=8), + legend.text=element_blank()) + + guides(colour=guide_colourbar(title="CD4.Th2\ndensity", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-Th2_density.pdf", + height=1.55, width=1.95, useDingbats=FALSE) + + NULL +``` + + + +```{r, fig.height=1.55, fig.width=1.95} +ggplot(umap.dens.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=umap.dens.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_point_rast(data=umap.dens.merge[umap.dens.merge$Sub.Annotation %in% c("CD4.Th17"), ], + aes(colour=Dens), size=0.1) + + scale_colour_viridis() + + theme_cowplot() + + labs(x="UMAP1", y="UMAP2") + + theme(aspect=1, + legend.key.size = unit(0.2, "cm"), + axis.text=element_blank(), + axis.title=element_blank(), + legend.title=element_text(size=8), + legend.text=element_blank()) + + guides(colour=guide_colourbar(title="CD4.Th17\ndensity", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-Th17_density.pdf", + height=1.55, width=1.95, useDingbats=FALSE) + + NULL +``` + + + +```{r, fig.height=1.55, fig.width=2.95} +ggplot(umap.dens.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=umap.dens.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_point_rast(data=umap.dens.merge[umap.dens.merge$Sub.Annotation %in% c("CD4.Th1", "CD4.Th2", "CD4.Th17"), ], + aes(colour=Dens), size=0.1) + + scale_colour_viridis() + + theme_cowplot() + + labs(x="UMAP1", y="UMAP2") + + theme(legend.key.size = unit(0.2, "cm"), + axis.text=element_blank(), + legend.title=element_text(size=8), + legend.text=element_blank()) + + guides(colour=guide_colourbar(title="CD4.Th density", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-Thall_density.pdf", + height=1.55, width=2.95, useDingbats=FALSE) + + NULL +``` + + + + +```{r, fig.height=10.95, fig.width=12.95} +# generate a series of PDFs, one for each cell type +for(x in seq_along(cell.order)){ + x.c <- cell.order[x] + + ggplot(umap.dens.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2)) + + geom_scattermore(data=umap.dens.merge[, c("Harmony.UMAP1", "Harmony.UMAP2")], + colour='grey80', alpha=0.2) + + geom_point_rast(data=umap.dens.merge[umap.dens.merge$Sub.Annotation %in% x.c, ], + aes(colour=Dens), size=0.01) + + scale_colour_viridis() + + labs(x="UMAP1", y="UMAP2") + + theme_cowplot() + + guides(colour=guide_colourbar(title="Density")) + + theme(legend.key.size = unit(0.2, "cm"), + axis.text=element_blank(), + legend.title=element_text(size=8), + legend.text=element_blank()) + + ggsave(paste0("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-", x.c, "_density_annotation.pdf"), + height=1.55, width=2.55, useDingbats=FALSE) + + NULL +} + +``` + + + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2, colour=Sex)) + + geom_scattermore() + + scale_colour_colorblind() + + theme_cowplot() + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-gender.pdf", + height=3.55, width=5.15, useDingbats=FALSE) + + NULL +``` + + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2, colour=Status_on_day_collection_summary)) + + geom_scattermore() + + scale_colour_manual(values=group.cols) + + theme_cowplot() + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-severity.pdf", + height=3.55, width=5.15, useDingbats=FALSE) + + NULL +``` + + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2, colour=Smoker)) + + geom_scattermore() + + scale_colour_aaas() + + theme_cowplot() + + guides(colour=guide_legend(title="Cell type", override.aes = list(size=3))) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-ethnicity.pdf", + height=3.55, width=5.15, useDingbats=FALSE) + + NULL +``` + + + +```{r, fig.height=3.55, fig.width=5.15} +ggplot(tcell.umap.merge, + aes(x=Harmony.UMAP1, y=Harmony.UMAP2, colour=Age)) + + geom_scattermore() + + scale_colour_gradient(low="blue", high="orange") + + theme_cowplot() + + guides(colour=guide_colourbar(title="Age")) + + ggsave("~/Dropbox/COVID19/Updated_plots/Tcell_Harmony-UMAP-age.pdf", + height=3.55, width=5.15, useDingbats=FALSE) + + NULL +``` + + + + + + +