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plasschaert2018.Rmd
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---
title: "Plasschaert et al., 2018"
output: html_notebook
---
Load libraries required for the further analysis.
```{r message=FALSE}
library(tidyverse)
library(data.table)
library(Seurat)
library(patchwork)
```
Load the datasets: human bronchial cells.
The data is normalized by library size and scaled by the mean library size.
Create Seurat object. Assign cell type identities stored in meta data.
```{r}
plasschaert.human.data <- fread("plasschaert/GSE102580_filtered_normalized_counts_human.tsv.gz",
skip=1, header=T, col.names=c("gene", paste0("cell_", c(0:2969))))
plasschaert.human.data_m <- as.matrix(plasschaert.human.data[,-1])
rownames(plasschaert.human.data_m) <- plasschaert.human.data$gene
plasschaert.human.meta <- fread("plasschaert/GSE102580_meta_filtered_counts_human.tsv.gz", skip=6)
row.names(plasschaert.human.meta) <- paste0("cell_", plasschaert.human.meta$V1)
plasschaert.human.seurat <- CreateSeuratObject(counts = log1p(plasschaert.human.data_m), project = "human_bronchi",
meta.data = plasschaert.human.meta)
Idents(plasschaert.human.seurat) <- "clusters_Fig1"
```
Load the dataset: mouse trachea. Select only uninjured cells.
The data is normalized by library size and scaled by the mean library size.
Create Seurat object. Assign cell type identities stored in meta data.
```{r}
plasschaert.mouse.data <- fread("plasschaert/GSE102580_filtered_normalized_counts_mouse.tsv.gz", skip=2,
col.names=c("gene", paste0("cell_", c(0:14162))))
plasschaert.mouse.meta <- fread("plasschaert/GSE102580_meta_filtered_counts_mouse.tsv.gz", skip=9)
plasschaert.mouse.uninj.meta <- filter(plasschaert.mouse.meta, timepoint=="uninjured", clusters_Fig1!="")
plasschaert.mouse.uninj.meta$cell <- paste0("cell_", plasschaert.mouse.uninj.meta$V1)
row.names(plasschaert.mouse.uninj.meta) <- plasschaert.mouse.uninj.meta$cell
plasschaert.mouse.data <- plasschaert.mouse.data %>%
select(gene, one_of(plasschaert.mouse.uninj.meta$cell))
plasschaert.mouse.data_m <- as.matrix(plasschaert.mouse.data[,-1])
rownames(plasschaert.mouse.data_m) <- plasschaert.mouse.data$gene
plasschaert.mouse.seurat <- CreateSeuratObject(counts = log1p(plasschaert.mouse.data_m), project = "mouse_trachea",
meta.data = plasschaert.mouse.uninj.meta)
Idents(plasschaert.mouse.seurat) <- "clusters_Fig1"
```
For SEFs in human bronchial cells dataset calculate average gene expression, average non-zero expression, and percent of cells expressing the gene.
Create two dotplots: for low expressed ACE2 and other SEFs.
```{r}
receptors.h <- c("ACE2", "TMPRSS2", "FURIN", "ANPEP", "DPP4")
plasschaert.human.sizes <- table(Idents(plasschaert.human.seurat)) %>% as.data.frame() %>%
rename(id = Var1, nCells = Freq) %>% filter(id %in% c("Basal", "Secretory", "Ciliated"))
plasschaert.human.receptors <- DotPlot(plasschaert.human.seurat, features = receptors.h)$data %>%
filter(id %in% c("Basal", "Secretory", "Ciliated")) %>%
merge(., plasschaert.human.sizes, by = "id", all = FALSE) %>%
transmute(gene = features.plot, id = factor(id, levels = rev(c("Basal", "Secretory", "Ciliated"))),
avg.nonzero.exp = log1p(avg.exp * nCells / (pct.exp * nCells / 100)),
avg.nonzero.exp = ifelse(is.nan(avg.nonzero.exp), 0, avg.nonzero.exp),
avg.exp = log1p(avg.exp), pct.exp)
# fwrite(plasschaert.human.receptors, "plasschaert/plasschaert.human.receptors.txt", sep="\t")
human_plot1 <- ggplot(filter(plasschaert.human.receptors, gene %in% c("ACE2")),
aes(x=gene, y=factor(id, levels = rev(c("Basal", "Secretory", "Ciliated"))),
color=avg.nonzero.exp, size=pct.exp)) +
geom_point() +
theme_classic() + labs(x="", y="", color="Average non-zero\nnormalized\nexpression", size="Percent\nexpressed") +
scale_color_gradient(low = "blue", high = "red",
limits = c(0, max(filter(plasschaert.human.receptors, gene %in% c("ACE2"))$avg.nonzero.exp,
filter(plasschaert.mouse.receptors, gene %in% c("Ace2"))$avg.nonzero.exp))) +
scale_size_continuous(limits = c(0, max(filter(plasschaert.human.receptors, gene %in% c("ACE2"))$pct.exp,
filter(plasschaert.mouse.receptors, gene %in% c("Ace2"))$pct.exp)),
breaks = c(0,5,10,15)) +
theme(axis.text.x = element_text(hjust = 1, angle = 45), axis.text = element_text(color = "black", size = 8),
legend.title = element_text(size = 6), legend.text = element_text(size = 6), legend.title.align = 0.5) +
guides(color = guide_colorbar(order = 1), size = guide_legend(order = 2))
human_plot2 <- ggplot(filter(plasschaert.human.receptors, gene %in% c("TMPRSS2", "FURIN", "ANPEP", "DPP4")),
aes(x=factor(gene, levels = c("TMPRSS2", "FURIN", "ANPEP", "DPP4")),
y=factor(id, levels = rev(c("Basal", "Secretory", "Ciliated"))), color=avg.nonzero.exp, size=pct.exp)) +
geom_point() +
theme_classic() + labs(x="", y="", color="Average non-zero\nnormalized\nexpression", size="Percent\nexpressed") +
scale_color_gradient(low = "blue", high = "red",
limits = c(0, max(plasschaert.human.receptors$avg.nonzero.exp,
plasschaert.mouse.receptors$avg.nonzero.exp))) +
scale_size_continuous(limits = c(0, max(plasschaert.human.receptors$pct.exp, plasschaert.mouse.receptors$pct.exp)),
breaks = c(0,15,30,45)) +
theme(axis.text.x = element_text(hjust = 1, angle = 45), axis.text = element_text(color = "black", size = 8),
legend.title = element_text(size = 6), legend.text = element_text(size = 6), legend.title.align = 0.5) +
guides(color = guide_colorbar(order = 1), size = guide_legend(order = 2))
human_plot1 +
guides(color = guide_colorbar(order = 1, barwidth = 0.7, barheight = 3),
size = guide_legend(order = 2, keywidth = 0.5, keyheight = 0.5)) +
human_plot2 +
guides(color = guide_colorbar(order = 1, barwidth = 0.7, barheight = 3),
size = guide_legend(order = 2, keywidth = 0.5, keyheight = 0.5)) +
plot_layout(widths = c(1, 2))
# ggsave("plasschaert/plots/plasschaert.human.receptors.nonzero.dp.png", width=7, height=3, dpi=300)
```
For SEFs in mouse trachea dataset calculate average gene expression, average non-zero expression, and percent of cells expressing the gene.
Create two dotplots: for low expressed Ace2 and other SEFs.
```{r}
receptors.m <- c("Ace2", "Tmprss2", "Furin", "Anpep", "Dpp4")
plasschaert.mouse.sizes <- table(Idents(plasschaert.mouse.seurat)) %>% as.data.frame() %>%
rename(id = Var1, nCells = Freq) %>% filter(id %in% c("Basal", "Secretory", "Ciliated"))
plasschaert.mouse.receptors <- DotPlot(plasschaert.mouse.seurat, features = receptors.m)$data %>%
filter(id %in% c("Basal", "Secretory", "Ciliated")) %>%
merge(., plasschaert.mouse.sizes, by = "id", all = FALSE) %>%
transmute(gene = features.plot, id = factor(id, levels = rev(c("Basal", "Secretory", "Ciliated"))),
avg.nonzero.exp = log1p(avg.exp * nCells / (pct.exp * nCells / 100)),
avg.nonzero.exp = ifelse(is.nan(avg.nonzero.exp), 0, avg.nonzero.exp),
avg.exp = log1p(avg.exp), pct.exp)
# fwrite(plasschaert.mouse.receptors, "plasschaert/plasschaert.mouse.receptors.txt", sep="\t")
mouse_plot1 <- ggplot(filter(plasschaert.mouse.receptors, gene %in% c("Ace2")),
aes(x=gene, y=factor(id, levels = rev(c("Basal", "Secretory", "Ciliated"))),
color=avg.nonzero.exp, size=pct.exp)) +
geom_point() +
theme_classic() + labs(x="", y="", color="Average non-zero\nnormalized\nexpression", size="Percent\nexpressed") +
scale_color_gradient(low = "blue", high = "red",
limits = c(0, max(filter(plasschaert.human.receptors, gene %in% c("ACE2"))$avg.nonzero.exp,
filter(plasschaert.mouse.receptors, gene %in% c("Ace2"))$avg.nonzero.exp))) +
scale_size_continuous(limits = c(0, max(filter(plasschaert.human.receptors, gene %in% c("ACE2"))$pct.exp,
filter(plasschaert.mouse.receptors, gene %in% c("Ace2"))$pct.exp)),
breaks = c(0,5,10,15)) +
theme(axis.text.x = element_text(hjust = 1, angle = 45), axis.text = element_text(color = "black", size = 8),
legend.title = element_text(size = 6), legend.text = element_text(size = 6), legend.title.align = 0.5) +
guides(color = guide_colorbar(order = 1), size = guide_legend(order = 2))
mouse_plot2 <- ggplot(filter(plasschaert.mouse.receptors, gene %in% c("Tmprss2", "Furin", "Anpep", "Dpp4")),
aes(x=factor(gene, levels = c("Tmprss2", "Furin", "Anpep", "Dpp4")),
y=factor(id, levels = rev(c("Basal", "Secretory", "Ciliated"))), color=avg.nonzero.exp, size=pct.exp)) +
geom_point() +
theme_classic() + labs(x="", y="", color="Average non-zero\nnormalized\nexpression", size="Percent\nexpressed") +
scale_color_gradient(low = "blue", high = "red",
limits = c(0, max(plasschaert.human.receptors$avg.nonzero.exp,
plasschaert.mouse.receptors$avg.nonzero.exp))) +
scale_size_continuous(limits = c(0, max(plasschaert.human.receptors$pct.exp, plasschaert.mouse.receptors$pct.exp)),
breaks = c(0,15,30,45)) +
theme(axis.text.x = element_text(hjust = 1, angle = 45), axis.text = element_text(color = "black", size = 8),
legend.title = element_text(size = 6), legend.text = element_text(size = 6), legend.title.align = 0.5) +
guides(color = guide_colorbar(order = 1), size = guide_legend(order = 2))
mouse_plot1 +
guides(color = guide_colorbar(order = 1, barwidth = 0.7, barheight = 3),
size = guide_legend(order = 2, keywidth = 0.5, keyheight = 0.5)) +
mouse_plot2 +
guides(color = guide_colorbar(order = 1, barwidth = 0.7, barheight = 3),
size = guide_legend(order = 2, keywidth = 0.5, keyheight = 0.5)) +
plot_layout(widths = c(1, 2))
# ggsave("plasschaert/plots/plasschaert.mouse.receptors.nonzero.dp.png", width=7, height=3, dpi=300)
```