https://github.com/Terkild/CITE-seq_optimization
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Antibody-titration.md
CITE-seq optimization - Antibody concentration titration
================
Terkild Brink Buus
30/3/2020
## Load utilities
Including libraries, plotting and color settings and custom utility
functions
``` r
set.seed(114)
require("Seurat", quietly=T)
require("tidyverse", quietly=T)
library("Matrix", quietly=T)
library("patchwork", quietly=T)
## Load ggplot theme and defaults
source("R/ggplot_settings.R")
## Load helper functions
source("R/Utilities.R")
## Load predefined color schemes
source("R/color.R")
## Load feature_rankplot functions
source("R/feature_rankplot.R")
source("R/feature_rankplot_hist.R")
source("R/feature_rankplot_hist_custom.R")
outdir <- "figures"
data.Seurat <- "data/5P-CITE-seq_Titration.rds"
data.abpanel <- "data/Supplementary_Table_1.xlsx"
data.markerStats <- "data/markerByClusterStats.tsv"
## Make a custom function for formatting the concentration scale
scaleFUNformat <- function(x) sprintf("%.2f", x)
```
## Load Seurat object
Subset to only focus on conditions with 50µl staining volume (thus
comparing DF1 to DF4) in both PBMC and lung samples.
``` r
object <- readRDS(file=data.Seurat)
## Show number of cells from each sample
table(object$group)
```
##
## PBMC_50ul_1_1000k PBMC_50ul_4_1000k PBMC_25ul_4_1000k PBMC_25ul_4_200k
## 1777 1777 1777 1777
## Lung_50ul_1_500k Lung_50ul_4_500k Doublet Negative
## 1681 1681 0 0
``` r
object <- subset(object, subset=volume == "50µl")
object
```
## An object of class Seurat
## 33572 features across 6916 samples within 3 assays
## Active assay: RNA.kallisto (33514 features)
## 2 other assays present: HTO.kallisto, ADT.kallisto
## 3 dimensional reductions calculated: pca, tsne, umap
## Load Ab panel annotation and concentrations
Marker stats is reused in other comparisons and was calculated in the
end of the preprocessing vignette.
``` r
abpanel <- data.frame(readxl::read_excel(data.abpanel))
rownames(abpanel) <- abpanel$Marker
markerStats <- read.table(data.markerStats)
markerStats.PBMC <- markerStats[markerStats$tissue == "PBMC",]
rownames(markerStats) <- paste(markerStats$marker,markerStats$tissue,sep="_")
## Make a ordering vector ordering markers per concentration and total UMI count
marker.order <- markerStats.PBMC$marker[order(markerStats.PBMC$conc_µg_per_mL, markerStats.PBMC$UMItotal, decreasing=TRUE)]
head(abpanel)
```
## Marker Category Alias Clone Isotype_Mouse Corresponding_gene
## CD103 CD103 B <NA> BerACT8 IgG1 ITGAE
## CD107a CD107a B LAMP1 H4A3 IgG1 LAMP1
## CD117 CD117 E C-kit 104D2 IgG1 KIT
## CD11b CD11b B <NA> ICRF44 IgG1 ITGAM
## CD123 CD123 E <NA> 6H6 IgG1 IL3RA
## CD127 CD127 E IL7Ralpha A019D5 IgG1 IL7R
## TotalSeqC_Tag BioLegend_Cat Stock_conc_µg_per_mL conc_µg_per_mL
## CD103 0145 350233 500 1.250
## CD107a 0155 328649 500 2.500
## CD117 0061 313243 500 2.500
## CD11b 0161 301359 500 0.625
## CD123 0064 306045 500 0.500
## CD127 0390 351356 500 1.250
## dilution_1x
## CD103 400
## CD107a 200
## CD117 200
## CD11b 800
## CD123 1000
## CD127 400
``` r
head(markerStats)
```
## marker tissue fineCluster nCells UMIsum nth median f90 UMItotal
## CD103_PBMC CD103 PBMC 1 638 1740 5.0 2 5.0 5082
## CD103_Lung CD103 Lung 16 132 7084 187.0 5 187.0 60252
## CD107a_PBMC CD107a PBMC 1 638 7757 26.3 8 26.3 13396
## CD107a_Lung CD107a Lung 12 260 11674 99.2 15 99.2 23273
## CD117_PBMC CD117 PBMC 1 638 1318 4.0 2 4.0 3316
## CD117_Lung CD117 Lung 21 32 1695 41.0 41 130.4 5878
## Category Alias Clone Isotype_Mouse Corresponding_gene
## CD103_PBMC B <NA> BerACT8 IgG1 ITGAE
## CD103_Lung B <NA> BerACT8 IgG1 ITGAE
## CD107a_PBMC B LAMP1 H4A3 IgG1 LAMP1
## CD107a_Lung B LAMP1 H4A3 IgG1 LAMP1
## CD117_PBMC E C-kit 104D2 IgG1 KIT
## CD117_Lung E C-kit 104D2 IgG1 KIT
## TotalSeqC_Tag BioLegend_Cat Stock_conc_µg_per_mL conc_µg_per_mL
## CD103_PBMC 145 350233 500 1.25
## CD103_Lung 145 350233 500 1.25
## CD107a_PBMC 155 328649 500 2.50
## CD107a_Lung 155 328649 500 2.50
## CD117_PBMC 61 313243 500 2.50
## CD117_Lung 61 313243 500 2.50
## dilution_1x marker.y DSB.cutoff positive count pct
## CD103_PBMC 400 CD103 7 14 1777 0.79
## CD103_Lung 400 CD103 7 501 1681 29.80
## CD107a_PBMC 200 CD107a 7 122 1777 6.87
## CD107a_Lung 200 CD107a 7 150 1681 8.92
## CD117_PBMC 200 CD117 7 3 1777 0.17
## CD117_Lung 200 CD117 7 32 1681 1.90
## Cell type and tissue overview
Make tSNE plots colored by cell type, cluster and tissue of origin.
``` r
p.tsne.tissue <- DimPlot(object, group.by="tissue", reduction="tsne", pt.size=0.1, combine=FALSE)[[1]] + theme_get() + facet_wrap(~"Tissue") + scale_color_manual(values=color.tissue)
p.tsne.cluster <- DimPlot(object, group.by="supercluster", reduction="tsne", pt.size=0.1, combine=FALSE)[[1]] + theme_get() + scale_color_manual(values=color.supercluster) + facet_wrap(~"Cell types")
p.tsne.finecluster <- DimPlot(object, label=TRUE, label.size=3, reduction="tsne", pt.size=0.1, group.by="fineCluster", combine=FALSE)[[1]] + theme_get() + facet_wrap( ~"Clusters") + guides(col=F)
p.tsne.cluster + p.tsne.finecluster + p.tsne.tissue
```
<!-- -->
## Overall ADT counts
Extract UMI data and calculate UMI sum per marker within each condition.
``` r
## Get the data
ADT.matrix <- data.frame(GetAssayData(object, assay="ADT.kallisto", slot="counts"))
ADT.matrix$marker <- rownames(ADT.matrix)
ADT.matrix$conc <- abpanel[ADT.matrix$marker,"conc_µg_per_mL"]
ADT.matrix <- ADT.matrix %>% pivot_longer(c(-marker,-conc))
## Get cell annotations
cell.annotation <- FetchData(object, vars=c("dilution", "tissue"))
## Calculate marker sum from each dilution within both tissues
ADT.matrix.agg <- ADT.matrix %>% group_by(dilution=cell.annotation[name,"dilution"], tissue=cell.annotation[name,"tissue"], marker, conc) %>% summarise(sum=sum(value))
## Order markers by concentration
ADT.matrix.agg$marker.byConc <- factor(ADT.matrix.agg$marker, levels=marker.order)
## Extract marker annotation
ann.markerConc <- abpanel[marker.order,]
ann.markerConc$Marker <- factor(marker.order, levels=marker.order)
ADT.matrix.agg.total <- ADT.matrix.agg
```
## Plot overall ADT counts by conditions
Samples stained with diluted Ab panel have reduced ADT counts.
``` r
p.UMIcountsPerCondition <- ggplot(ADT.matrix.agg.total[order(-ADT.matrix.agg$conc, -ADT.matrix.agg$sum),], aes(x=dilution, y=sum/10^6, fill=conc)) +
geom_bar(stat="identity", col=alpha(col="black",alpha=0.05)) +
scale_fill_viridis_c(trans="log2", labels=scaleFUNformat, breaks=c(0.0375,0.15,0.625,2.5,10)) +
scale_y_continuous(expand=c(0,0,0,0.05)) +
labs(fill="DF1\nµg/mL", y=bquote("ADT UMI counts ("~10^6~")")) +
guides(fill=guide_colourbar(reverse=T)) +
facet_wrap(~tissue) +
theme(panel.grid.major=element_blank(), axis.title.x=element_blank(), panel.border=element_blank(), axis.line = element_line(), legend.position="right")
p.UMIcountsPerCondition
```
<!-- -->
## Compare total UMI counts per marker
Plot total UMI counts for each marker at the investigated dilution
factors (DF1 vs. DF4). To ease readability, we place dashed lines
between each concentration.
``` r
## Calculate "breaks" where concentration change.
lines <- length(marker.order)-cumsum(sapply(split(ann.markerConc$Marker,ann.markerConc$conc_µg_per_mL),length))+0.5
lines <- data.frame(breaks=lines[-length(lines)])
## Make a marker by concentration "heatmap"
p.markerByConc <- ggplot(ann.markerConc, aes(x=1, y=Marker, fill=conc_µg_per_mL)) +
geom_tile(col=alpha(col="black",alpha=0.2)) +
geom_hline(data=lines,aes(yintercept=breaks), linetype="dashed", alpha=0.5) +
scale_fill_viridis_c(trans="log2") +
labs(fill="µg/mL") +
theme_get() +
theme(axis.ticks.x=element_blank(), axis.title = element_blank(), axis.text.x=element_blank(), panel.grid=element_blank(), legend.position="right", plot.margin=unit(c(0.1,0.1,0.1,0.1),"mm")) + scale_x_continuous(expand=c(0,0))
## Make UMI counts per Marker plot
p.UMIcountsPerMarker <- ggplot(ADT.matrix.agg, aes(x=marker.byConc,y=log2(sum))) +
geom_line(aes(group=marker), size=1.2, color="#666666") +
geom_point(aes(group=dilution, fill=dilution), pch=21, size=0.7) +
geom_vline(data=lines,aes(xintercept=breaks), linetype="dashed", alpha=0.5) +
scale_fill_manual(values=color.dilution) +
facet_wrap(~tissue) +
scale_y_continuous(breaks=c(9:17)) +
ylab("log2(UMI sum)") +
guides(fill=guide_legend(override.aes=list(size=1.5), reverse=TRUE)) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), legend.position="bottom", legend.justification="left", legend.title.align=0, legend.key.width=unit(0.2,"cm"), legend.title=element_blank()) +
coord_flip()
## Combine plot with markerByConc annotation heatmap
plotUMIcountsPerMarker <- p.markerByConc + guides(fill=F) + p.UMIcountsPerMarker + guides(fill=F) + plot_spacer() + guide_area() + plot_layout(ncol=4, widths=c(1,30,0.1), guides='collect')
plotUMIcountsPerMarker
```
<!-- -->
## Compare change in UMI/cell within expressing cluster
Using a specific percentile may be prone to outliers in small clusters
(i.e. the 90th percentile of a cluster of 30 will be the \#3 higest cell
making it prone to outliers). We thus set a threshold of the value to
only be the 90th percentile if cluster contains more than 100 cells. For
smaller clusters, the median is used. Expressing cluster is identified
in the “preprocessing” vignette.
``` r
## Get the data
ADT.matrix <- data.frame(GetAssayData(object, assay="ADT.kallisto", slot="counts"))
ADT.matrix$marker <- rownames(ADT.matrix)
ADT.matrix$conc <- abpanel[ADT.matrix$marker,"conc_µg_per_mL"]
ADT.matrix <- ADT.matrix %>% pivot_longer(c(-marker,-conc))
## Get cell annotations
cell.annotation <- FetchData(object, vars=c("dilution", "tissue", "fineCluster"))
## Calculate marker statistics from each dilution within each cluster
ADT.matrix.agg <- ADT.matrix %>% group_by(dilution=cell.annotation[name,"dilution"], tissue=cell.annotation[name,"tissue"], fineCluster=cell.annotation[name,"fineCluster"], marker, conc) %>% summarise(sum=sum(value), median=quantile(value, probs=c(0.9)), nth=nth(value))
## Use data for the previously determined expressing cluster.
Cluster.max <- markerStats[,c("marker","tissue","fineCluster")]
Cluster.max$fineCluster <- factor(Cluster.max$fineCluster)
Cluster.max$tissue <- factor(Cluster.max$tissue, levels=c("PBMC","Lung"))
ADT.matrix.aggByClusterMax <- Cluster.max %>% left_join(ADT.matrix.agg)
ADT.matrix.aggByClusterMax$marker.byConc <- factor(ADT.matrix.aggByClusterMax$marker, levels=marker.order)
p.UMIinExpressingCells <- ggplot(ADT.matrix.aggByClusterMax, aes(x=marker.byConc, y=log2(nth))) +
geom_line(aes(group=marker), size=1.2, color="#666666") +
geom_point(aes(group=dilution, fill=dilution), pch=21, size=0.7) +
geom_vline(data=lines,aes(xintercept=breaks), linetype="dashed", alpha=0.5) +
geom_text(aes(label=paste0(fineCluster," ")), y=Inf, adj=1, size=1.5) +
facet_wrap(~tissue) +
scale_fill_manual(values=color.dilution) +
scale_y_continuous(breaks=c(0:11), labels=2^c(0:11), expand=c(0.05,0.5)) +
ylab("90th percentile UMI of expressing cluster") +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), legend.position="right", legend.justification="left", legend.title.align=0, legend.key.width=unit(0.2,"cm")) +
coord_flip()
## Combine plot with markerByConc annotation heatmap
UMIinExpressingCells <- p.markerByConc + theme(legend.position="none") + p.UMIinExpressingCells + theme(legend.position="none") + plot_spacer() + plot_layout(ncol=4, widths=c(1,30,0.1), guides='collect')
UMIinExpressingCells
```
<!-- -->
## ADT expression per cluster expression overview
Get data and calculate expression level and frequencies within positive
cells from each cluster
``` r
## Get the data
data.ADT.DSB <- GetAssayData(object, assay="ADT.kallisto", slot="data")
data.meta <- FetchData(object, vars=c("fineCluster","supercluster","dilution","tissue"))
## Determine expression level within positive fraction of each cluster
data.ADT.DSB.above <- as.data.frame(data.ADT.DSB) %>%
mutate(marker=rownames(.)) %>%
pivot_longer(-marker) %>%
filter(data.meta[name,"dilution"]=="DF1" &
value >= markerStats[paste(marker,data.meta[name,"tissue"],sep="_"),"DSB.cutoff"]) %>%
group_by(marker,
fineCluster=data.meta[name,"fineCluster"]) %>%
summarize(count=length(value),
mean=mean(value),
sd=sd(value),
median=median(value),
nth=nth(value)) %>%
arrange(fineCluster, marker)
## Convert from long into wide format using "mean" ADT signal within expression cells
data.ADT.DSB.above.matrix <- data.ADT.DSB.above %>%
select(marker, fineCluster, mean) %>%
pivot_wider(names_from=marker, values_from=mean, id_cols=fineCluster) %>%
replace(is.na(.), 0) %>%
tibble::column_to_rownames("fineCluster")
## Get number of cells in each cluster
data.clusterCount <- as.numeric(table(data.meta$fineCluster))
names(data.clusterCount) <- levels(data.meta$fineCluster)
## Calculate positive frequency within each cluster
data.ADT.DSB.above.freq.matrix <- data.ADT.DSB.above %>%
select(marker, fineCluster, count) %>%
mutate(count=count/data.clusterCount[as.character(fineCluster)]) %>%
pivot_wider(names_from=marker, values_from=count, id_cols=fineCluster) %>%
replace(is.na(.), 0) %>%
tibble::column_to_rownames("fineCluster")
```
Calculate annotation data
``` r
### Calculate annotation data
ann.conc <- log2(abpanel[colnames(data.ADT.DSB.above.matrix),"conc_µg_per_mL"])
## Determin
ann.fineCluster <- data.meta %>%
group_by(fineCluster, supercluster) %>%
summarize(PBMC.count = sum(tissue=="PBMC"),
Lung.count = sum(tissue=="Lung"),
count = length(tissue)) %>%
mutate(tissue=ifelse((PBMC.count-Lung.count)>0,"PBMC","Lung")) %>%
filter(count > 1 & fineCluster %in% rownames(data.ADT.DSB.above.matrix)) %>%
tibble::column_to_rownames("fineCluster")
ann.fineCluster <- ann.fineCluster %>% mutate(count.scaled=scales::rescale(count,to=c(0, 0.7), from=c(0,max(count))))
ann.fineCluster <- data.frame(ann.fineCluster)
## Calclu
ann.UMIuse <- ADT.matrix.agg.total %>% filter(dilution=="DF1")
ann.UMIuse.PBMC <- ann.UMIuse %>% filter(tissue=="PBMC") %>% as.data.frame()
rownames(ann.UMIuse.PBMC) <- ann.UMIuse.PBMC$marker
ann.UMIuse.Lung <- ann.UMIuse %>% filter(tissue=="Lung") %>% as.data.frame()
rownames(ann.UMIuse.Lung) <- ann.UMIuse.Lung$marker
```
Heatmap showing the fraction of positive cells (circle size) and their
expression level (color) of each antibody within each cluster. Including
additional annotation such as UMI counts within each tissue and antibody
concentration.
``` r
library(ComplexHeatmap)
## Set color schemes
col_fun = circlize::colorRamp2(c(0, 7.5, 15, 22.5, 30), c(viridis::inferno(5)))
col_fun.count = circlize::colorRamp2(c(0, 0.25, 0.5, 1), c("blue", "white", "yellow", "red"))
col_fun.conc = circlize::colorRamp2(c(-4, -2.25, -0.5, 1.25, 3), c(viridis::viridis(5)))
## Make row annotation object
row_ha = rowAnnotation("Tissue"=ann.fineCluster[,"tissue"],
"Cell Type"=ann.fineCluster[,"supercluster"],
"Cluster size"=anno_simple(ann.fineCluster$count.scaled,
col=col_fun.count,
pch = 21,
pt_gp = gpar(col="black",
fill=col_fun.count(ann.fineCluster$count.scaled),
lwd=0.5),
pt_size = unit(ann.fineCluster$count.scaled+0.2, "npc"),
gp = gpar(fill=NA,
lwd=0.5,
col="black")),
col=list("Cell Type"=color.supercluster, "Tissue"=color.tissue),
annotation_legend_param = list(legend_direction = "horizontal"),
show_legend=FALSE,
gp=gpar(color="black", lwd=0.5),
show_annotation_name=TRUE,
annotation_name_gp=gpar(fontsize=5, fontface="bold"),
annotation_width=unit(c(3,3,3),"mm"))
## Make column annotation object
column_ha = HeatmapAnnotation("UMI count (PBMC)"=ann.UMIuse.PBMC[colnames(data.ADT.DSB.above.matrix),"sum"]/10^3,
"UMI count (Lung)"=ann.UMIuse.Lung[colnames(data.ADT.DSB.above.matrix),"sum"]/10^3,
"DF1 conc."=ann.conc,
col=list("UMI count (PBMC)"=col_fun,
"UMI count (Lung)"=col_fun,
"DF1 conc."=col_fun.conc),
gp=gpar(color="black",
lwd=0.5),
annotation_legend_param = list(legend_direction = "horizontal"),
show_legend=FALSE,
show_annotation_name=TRUE,
annotation_name_gp=gpar(fontsize=5, fontface="bold"),
simple_anno_size=unit(3,"mm"),
annotation_name_side="left",
which="column")
## Circle function for ComplexHeatmap
drawCircle <- function(j, i, x, y, width, height, fill){
selected <- which(Cluster.max[,"fineCluster"] == rownames(data.ADT.DSB.above.matrix)[i] & Cluster.max[,"marker"]==colnames(data.ADT.DSB.above.matrix)[j])
if(length(selected) > 0){
grid.rect(x=x, y=y, width=width, height=height, gp=gpar(col=color.tissue[Cluster.max[selected,"tissue"]],
fill=NA
))
}
radius <- data.ADT.DSB.above.freq.matrix[i, j]*1.8
## If positive population is less than a few percent percent, do not show it
# What is the actual cutoff? this threhold is based on arbitrary values with arbitrary cutoff
radius <- ifelse(radius > 0.02*1.8,radius+0.2, 0)
grid.circle(x = x, y = y, r = radius*min(unit.c(width, height)),gp = gpar(fill = col_fun(data.ADT.DSB.above.matrix[i, j]), col=alpha("black",0.5), size=0.1))
}
## Make the heatmap
ht_list <- Heatmap(data.ADT.DSB.above.matrix,
cell_fun=drawCircle,
rect_gp = gpar(type = "none", lwd=0.5, col="black"),
col = col_fun,
top_annotation=column_ha,
left_annotation=row_ha,
border = FALSE,
name = "DSB",
heatmap_legend_param = list(legend_direction = "horizontal"),
show_heatmap_legend=FALSE,
heatmap_width = unit(7, "in"),
heatmap_height = unit(3, "in"),
row_names_gp = gpar(fontsize = 6, fontface="bold"),
row_names_side = "left",
column_names_gp = gpar(fontsize = 6),
row_dend_width = unit(3, "mm"),
column_dend_height = unit(3, "mm"),
column_dend_gp = gpar(lwd=0.5),
row_dend_gp = gpar(lwd=0.5)
)
## Draw the heatmap
draw_heatmap_now <- function(){
draw(ht_list) + decorate_heatmap_body("DSB", {grid.grill(h=(0:21+0.5)/22, v=(0:52+0.5)/52, gp=gpar(alpha=0.05, col="black"))})
popViewport()
}
draw_heatmap_now()
```
<!-- -->
## Final plot
``` r
### First row of plots: tSNE overview
A <- p.tsne.cluster +
theme(axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_text(),
legend.position=c(0.5,0.01),
legend.justification=c(0.5,1),
legend.margin=margin(0,0,0,0),
legend.title = element_blank(),
legend.background=element_blank(),
plot.margin = unit(c(1,1,5,1),"mm")) +
guides(color=guide_legend(ncol=2,
override.aes=list(shape=19, size=1.5),
keyheight=unit(0.25, "cm"),
keywidth=unit(0.15, "cm")))
B <- p.tsne.finecluster +
theme(axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.title.y=element_blank(),
axis.title.x=element_text())
C <- p.tsne.tissue +
theme(axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.title.y=element_blank(),
axis.title.x=element_blank(),
legend.position=c(0.5,0.01),
legend.justification=c(0.5,2),
legend.margin=margin(0,0,0,0),
legend.title = element_blank(),
legend.direction="horizontal",
legend.background=element_blank()) +
guides(color=guide_legend(override.aes=list(shape=19, size=1.5),
keyheight=unit(0.25, "cm"),
keywidth=unit(0.15, "cm")))
D <- p.UMIcountsPerCondition +
theme(legend.key.width=unit(0.3,"cm"),
legend.key.height=unit(0.4,"cm"),
legend.text=element_text(size=unit(5,"pt")))
## Combine A-D panels.
AD <- cowplot::plot_grid(A, B, C, D,
labels=c("A", "B", "C", "D"),
label_size=panel.label_size,
nrow=1,
vjust=panel.label_vjust,
hjust=panel.label_hjust,
align="h",
axis="tb",
rel_widths = c(2.2,2,2,2.2))
### Second row: Heatmap
## Make ComplexHeatmap output into a grid object for cowplot compatibility
E <- grid::grid.grabExpr(draw_heatmap_now(), wrap=TRUE)
## Make custom legend for the E (heatmap) panel. As other figures use "ggplot" legend style, we will remake the heatmap color bar in this style
E.legend <- cowplot::get_legend(ggplot(data.frame(values=c(0, 7.5, 15, 22.5, 30)),
aes(x=values,y=values,col=values)) +
scale_color_gradientn(colours=col_fun(c(0, 7.5, 15, 22.5, 30)),
values=c(0, 7.5, 15, 22.5, 30)/30,
breaks=c(0,30),
labels=c("Low","High")) +
geom_point() +
theme(legend.title=element_blank(),
legend.background=element_blank(),
legend.box.background=element_blank(),
legend.key=element_blank(),
legend.key.height=unit(2,"mm"),
legend.key.width=unit(3,"mm")))
### Third row: Titration "line" plots.
F1 <- p.markerByConc + theme(legend.position="none")
F2 <- p.UMIcountsPerMarker + theme(legend.position="none")
G1 <- p.UMIinExpressingCells + theme(legend.position="none")
F.legend <- cowplot::get_legend(p.UMIcountsPerMarker +
theme(legend.position="bottom",
legend.direction="horizontal",
legend.background=element_blank(),
legend.box.background=element_blank(),
legend.key=element_blank()))
## Combine F and G panels
FG <- cowplot::plot_grid(F1, F2, F1, G1,
nrow=1,
align="h",
axis="tb",
labels=c("F", "", "G", ""),
label_size=panel.label_size,
vjust=panel.label_vjust,
hjust=panel.label_hjust,
rel_widths=c(2,10,2,10))
## Combine A-D, E and F-G panels.
p.figure1 <- cowplot::ggdraw(cowplot::plot_grid(AD, E, FG,
ncol=1,
rel_heights=c(2.2,3.4,4.8),
align="v",
axis="l",
labels=c("", "E", ""),
label_size=panel.label_size,
vjust=panel.label_vjust,
hjust=panel.label_hjust)) +
## Add custom legend for F and G panel
cowplot::draw_plot(F.legend,0.08,0.017,0.2,0.00001) +
## Add custom legend for E (heatmap) panel
cowplot::draw_plot(E.legend,1,0.47,0.12,0.001,hjust=1, vjust=0)
## Make PNG of final figure
png(file=file.path(outdir,"Figure 1.png"),
width=figure.width.full,
height=9.5,
units = figure.unit,
res=figure.resolution,
antialias=figure.antialias)
p.figure1
dev.off()
```
## png
## 2
``` r
p.figure1
```
<!-- -->
## Titration examples
Reduction of antibody concentration had different inpacts on different
markers. We divided the markers into five categories and show examples
of each category below:
``` r
## Make helper function for plotting titration plots
titrationPlot <- function(marker, gate.PBMC=NULL, gate.Lung=NULL, y.axis=FALSE, show.gate=TRUE){
curMarker.name <- marker
## Get antibody concentration for legends
curMarker.DF1conc <- abpanel[curMarker.name, "conc_µg_per_mL"]
if(show.gate==TRUE){
## Load gating percentages from manually set DSB thresholds
gate <- markerStats[markerStats$marker == curMarker.name,c("tissue","pct")]
colnames(gate) <- c("wrap","gate")
gate$gate <- 1-(gate$gate/100)
rownames(gate) <- gate$wrap
## Allow manual gating
if(!is.null(gate.PBMC)) gate["PBMC","gate"] <- gate.PBMC
if(!is.null(gate.Lung)) gate["Lung","gate"] <- gate.Lung
gate$wrap <- factor(gate$wrap, levels=c("PBMC","Lung"))
} else {
gate <- NULL
}
p <- feature_rankplot_hist_custom(data=object,
marker=paste0("adt_",curMarker.name),
group="dilution",
wrap="tissue",
barcodeGroup="supercluster",
conc=curMarker.DF1conc,
legend=FALSE,
yaxis.text=y.axis,
gates=gate,
histogram.colors=color.dilution,
title=curMarker.name)
return(p)
}
## Make titration plots for selected markers from each "category"
titrationPlots <- list()
# Category A
titrationPlots[["CD30"]] <- titrationPlot("CD30", y.axis=TRUE)
titrationPlots[["CD183"]] <- titrationPlot("CD183")
titrationPlots[["TCRgd"]] <- titrationPlot("TCRgd")
# Category B
titrationPlots[["HLA-DR"]] <- titrationPlot("HLA-DR", y.axis=TRUE, gate.PBMC=0.47, gate.Lung=0.295)
titrationPlots[["CD4"]] <- titrationPlot("CD4", gate.PBMC=0.28, gate.Lung=0.50)
titrationPlots[["CD19"]] <- titrationPlot("CD19", gate.PBMC=0.92, gate.Lung=0.75)
# Category C
titrationPlots[["CD3"]] <- titrationPlot("CD3", y.axis=TRUE, gate.PBMC=0.56, gate.Lung=0.46)
titrationPlots[["CD14"]] <- titrationPlot("CD14", gate.PBMC=0.68, gate.Lung=0.86)
titrationPlots[["CD39"]] <- titrationPlot("CD39", gate.PBMC=0.54, gate.Lung=0.33)
# Category D
titrationPlots[["HLA-ABC"]] <- titrationPlot("HLA-ABC", y.axis=TRUE)
titrationPlots[["CD44"]] <- titrationPlot("CD44")
titrationPlots[["CD45"]] <- titrationPlot("CD45")
# Category E
titrationPlots[["CD56"]] <- titrationPlot("CD56", y.axis=TRUE)
titrationPlots[["CD117"]] <- titrationPlot("CD117")
titrationPlots[["CD127"]] <- titrationPlot("CD127")
## Make a panel row for each category
A <- cowplot::plot_grid(plotlist=titrationPlots[1:3], nrow=1)
B <- cowplot::plot_grid(plotlist=titrationPlots[4:6], nrow=1)
C <- cowplot::plot_grid(plotlist=titrationPlots[7:9], nrow=1)
D <- cowplot::plot_grid(plotlist=titrationPlots[10:12], nrow=1)
E <- cowplot::plot_grid(plotlist=titrationPlots[13:15], nrow=1)
# a bit of a hack to make celltype legend
p.legend <- cowplot::get_legend(ggplot(data.frame(supercluster=object$supercluster),
aes(color=supercluster,x=1,y=1)) +
geom_point(shape=15, size=1.5) +
scale_color_manual(values=color.supercluster) +
theme(legend.title=element_blank(),
legend.margin=margin(0,0,0,0),
legend.key.size = unit(0.15,"cm"),
legend.position = c(0.98,1.1),
legend.justification=c(1,1),
legend.direction="horizontal"))
## Combine final figure
p.figure2 <- cowplot::plot_grid(NULL, p.legend, A, NULL, B, NULL, C, NULL, D, NULL, E,
labels=c("","","A","","B","","C","","D","","E"),
ncol=1,
rel_heights= c(1.3,1.5,20,1.5,20,1.5,20,1.5,20,1.5,20),
vjust=-0.5,
hjust=panel.label_hjust,
label_size=panel.label_size)
## Make PNG of final figure
png(file=file.path(outdir,"Figure 2.png"),
units=figure.unit,
res=figure.resolution,
width=figure.width.full,
height=10,
antialias=figure.antialias)
p.figure2
dev.off()
```
## png
## 2
``` r
p.figure2
```
<!-- -->
## Individual titration plots
For supplementary information.
``` r
plots.columns = 3
markers <- abpanel[rownames(object[["ADT.kallisto"]]),]
markers <- markers[order(markers$Category, markers$Marker),]
## Divide titration plots into categories
plotCat <- list("A"=list(),"B"=list(),"C"=list(),"D"=list(),"E"=list())
## Make individual plots for each marker
for(i in 1:nrow(markers)){
curMarker <- markers[i,]
curMarker.name <- curMarker$Marker
y.axis <- ifelse(length(plotCat[[curMarker$Category]]) %in% c(0,3,6,9,12),TRUE,FALSE)
plotCat[[curMarker$Category]][[curMarker.name]] <- titrationPlot(curMarker.name, y.axis=y.axis)
}
## Make a supplementary figure for each category
for(i in seq_along(plotCat)){
category <- names(plotCat)[i]
plots <- plotCat[[category]]
plots.num <- length(plots)
plots.rows <- ceiling(plots.num/plots.columns)
curPlots <- cowplot::plot_grid(plotlist=plots,ncol=plots.columns, align="h", axis="tb")
curPlots.layout <- cowplot::plot_grid(NULL, p.legend, curPlots, vjust=-0.5, hjust=panel.label_hjust, label_size=panel.label_size, ncol=1, rel_heights= c(0.5, 1.3, 70/5*plots.rows))
png(file=file.path(outdir,paste0("Supplementary Figure 2",category,".png")),
units=figure.unit,
res=figure.resolution,
width=figure.width.full,
height=(2*plots.rows),
antialias=figure.antialias)
print(curPlots.layout)
dev.off()
print(curPlots.layout)
}
```
<!-- --><!-- --><!-- --><!-- --><!-- -->
