/
07D_Combined_Figures.Rmd
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07D_Combined_Figures.Rmd
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---
title: "Combined Figures"
output:
workflowr::wflow_html:
code_folding: hide
df_print: paged
---
```{r knitr, include = FALSE}
DOCNAME = "07D_Combined_Figures"
knitr::opts_chunk$set(autodep = TRUE,
cache = TRUE,
cache.path = paste0("cache/", DOCNAME, "/"),
cache.comments = FALSE,
echo = TRUE,
error = FALSE,
fig.align = "center",
fig.width = 10,
fig.height = 8,
message = FALSE,
warning = FALSE)
```
```{r libaries, cache = FALSE}
# scRNA-seq
library("Seurat")
library("monocle")
# Plotting
library("clustree")
library("cowplot")
library("paletteer")
# Presentation
library("glue")
library("knitr")
# Parallel
# Paths
library("here")
# Output
# Tidyverse
library("tidyverse")
```
```{r source, cache = FALSE}
source(here("R/output.R"))
```
```{r paths}
comb.path <- here("data/processed/Combined_clustered.Rds")
comb.neph.path <- here("data/processed/Combined_nephron.Rds")
dir.create(here("output", DOCNAME), showWarnings = FALSE)
```
Introduction
============
In this document we are going to look at all of the organoids analysis results
and produce a series of figures for the paper.
```{r load-comb, cache.extra = tools::md5sum(comb.path), cache.lazy = FALSE}
if (file.exists(comb.path)) {
comb <- read_rds(comb.path)
} else {
stop("Clustered Combined dataset is missing. ",
"Please run '07_Combined_Clustering.Rmd' first.",
call. = FALSE)
}
```
```{r load-comb-neph, cache.extra = tools::md5sum(comb.neph.path)}
if (file.exists(comb.neph.path)) {
comb.neph <- read_rds(comb.neph.path)
} else {
stop("Clustered Combined nephron dataset is missing. ",
"Please run '07B_Organoids_Nephron.Rmd' first.",
call. = FALSE)
}
```
Figure 2A
=========
```{r fig-2A}
plot.data <- comb %>%
GetDimReduction("tsne", slot = "cell.embeddings") %>%
data.frame() %>%
rownames_to_column("Cell") %>%
mutate(Group = comb@meta.data$Group) %>%
mutate(Group = if_else(Group == "Lindstrom", "Fetal kidney", Group)) %>%
mutate(Cluster = comb@ident)
f2A <- ggplot(plot.data, aes(x = tSNE_1, y = tSNE_2, colour = Group)) +
geom_point() +
scale_color_manual(values = c("#F47920", "#7A52C7")) +
guides(colour = guide_legend(override.aes = list(size = 10))) +
theme_cowplot() +
theme(legend.position = "right",
legend.title = element_blank())
ggsave(here("output", DOCNAME, "figure2A.png"), f2A,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure2A.pdf"), f2A,
height = 8, width = 10)
f2A
```
Figure 2B
=========
```{r fig-2B}
lab.data <- plot.data %>%
group_by(Cluster) %>%
summarise(tSNE_1 = mean(tSNE_1),
tSNE_2 = mean(tSNE_2)) %>%
mutate(Label = paste0("C", Cluster))
clust.labs <- c(
"C0 (Stroma)\nTAGLN, ALDH1A2, COL6A3",
"C1 (Stroma)\nDCN, LUM, POSTN",
"C2 (Stroma)\nMGP, GATA3, NDUFA4L2",
"C3 (Stroma)\nZFHX4, CTSK, HNRNPA1",
"C4 (Endothelium)\nGNG11, CLDN5, CD34",
"C5 (Cell cycle)\nHIST1H4C, PCLAF, DUT",
"C6 (Committing nephron prog.)\nLYPD1, DAPL1, PAX8",
"C7 (Podocyte)\nPODXL, MAFB, TCF21",
"C8 (Cell cycle)\nCENPF, HMGB2, UBE2C",
"C9 (Stroma)\nCOL9A3, DCN, LUM, IGFBP6",
"C10 (Nephron)\nIGFBP7, EMBX2, MAL",
"C11 (Neural prog.)\nTTHY1, SOX2, S100B",
"C12 (Leukocyte)\nCD74, TYROBP, FCER1G",
"C13 (Blood)\nCYPC, SLC25A39, GPX1",
"C14 (Neural prog.)\nOrganoid only",
"C15 (Podocyte)\nFetal kidney only"
)
f2B <- ggplot(plot.data, aes(x = tSNE_1, y = tSNE_2, colour = Cluster)) +
geom_point(data = filter(plot.data, Cluster %in% c(6, 7, 10, 15)),
colour = "grey30", size = 4) +
geom_point() +
geom_text(data = lab.data, aes(label = Label), colour = "black", size = 8) +
scale_color_paletteer_d("ggthemes", "gdocs", labels = clust.labs) +
guides(colour = guide_legend(ncol = 1, override.aes = list(size = 10),
label.theme = element_text(size = 8))) +
theme_cowplot() +
theme(legend.position = "right",
legend.title = element_blank())
ggsave(here("output", DOCNAME, "figure2B.png"), f2B,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure2B.pdf"), f2B,
height = 8, width = 10)
f2B
```
Figure 2AB legend
=================
```{r fig-2AB-legend}
lA <- get_legend(f2A)
lB <- get_legend(f2B)
lAB <- plot_grid(lA, lB, ncol = 1, rel_heights = c(0.1, 1))
lAB
```
Figure 2C
=========
```{r fig-2C}
plot.data <- comb.neph %>%
GetDimReduction("tsne", slot = "cell.embeddings") %>%
data.frame() %>%
rownames_to_column("Cell") %>%
mutate(Cluster = comb.neph@ident)
lab.data <- plot.data %>%
group_by(Cluster) %>%
summarise(tSNE_1 = mean(tSNE_1),
tSNE_2 = mean(tSNE_2)) %>%
mutate(Label = paste0("CN", Cluster))
clust.labs <- c(
"CN0 (Podocyte)\nTCF21, PODXL, SYNPO",
"CN1 (Differentiating nephron prog.)\nLYPD1, PAX8, DAPL1",
"CN2 (Nephron prog.)\nCRABP2, SIX1, TMEM100, CITED1",
"CN3 (Podocyte precursor)\nCTGF, OLFM3, MAFB",
"CN4 (Distal)\nWFDC2, EMX2, MAL, GATA3",
"CN5 (Proximal)\nIGFBP7, MPC2, FXYD2, CDH6",
"CN6 (Stroma)\nfetal kidney only",
"CN7 (Podocyte)\nfetal kidney only"
)
f2C <- ggplot(plot.data, aes(x = tSNE_1, y = tSNE_2, colour = Cluster)) +
geom_point() +
geom_text(data = lab.data, aes(label = Label), colour = "black", size = 8) +
scale_color_brewer(palette = "Dark2", labels = clust.labs) +
guides(colour = guide_legend(ncol = 4, override.aes = list(size = 10),
label.theme = element_text(size = 8))) +
theme_cowplot() +
theme(legend.position = "bottom",
legend.title = element_blank())
ggsave(here("output", DOCNAME, "figure2C.png"), f2C,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure2C.pdf"), f2C,
height = 8, width = 10)
f2C
```
Figure 2D
=========
```{r fig-2D}
plot.data <- comb.neph@meta.data %>%
select(NephCluster, Group) %>%
mutate(NephCluster = paste0("CN", NephCluster)) %>%
mutate(Group = if_else(Group == "Lindstrom", "Fetal kidney", Group))
f2D <- ggplot(plot.data, aes(x = NephCluster, fill = NephCluster)) +
geom_bar() +
scale_fill_brewer(palette = "Dark2") +
labs(y = "Number of cells") +
facet_wrap(~ Group, ncol = 1, scales = "free_y") +
theme_cowplot() +
theme(legend.position = "none",
axis.title.x = element_blank(),
strip.text = element_text(size = 15,
margin = margin(0, 0, 2, 0, "pt")),
strip.background = element_blank(),
strip.placement = "outside")
ggsave(here("output", DOCNAME, "figure2D.png"), f2D,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure2D.pdf"), f2D,
height = 8, width = 10)
f2D
```
Figure 2E
=========
```{r fig-2E}
genes <- c("TCF21", "PODXL", "VEGFA", "NPHS1", "PTPRO", "CTGF", "OLFM3", "MAFB",
"DAPL1", "LYPD1", "SIX1", "CRABP2", "IGFBP7", "FXYD2", "CDH6",
"HNF1B", "EPCAM", "EMX2", "SPP1", "MAL", "PAX2", "HSPB1", "HSPA1A",
"HSPA4")
gene.groups <- rev(c(rep("Podocyte", 5),
rep("Pod. precursor", 3),
rep("Nephron progenitor", 4),
rep("Proximal precursor", 4),
rep("Distal precursor", 5),
rep("Heat shock", 3))) %>%
fct_relevel("Podocyte", "Pod. precursor", "Nephron progenitor",
"Proximal precursor","Distal precursor", "Heat shock")
clust.labs <- c(
"Fetal kidney CN0", "Organoid CN0",
"Fetal kidney CN1", "Organoid CN1",
"Fetal kidney CN2", "Organoid CN2",
"Fetal kidney CN3", "Organoid CN3",
"Fetal kidney CN4", "Organoid CN4",
"Fetal kidney CN5", "Organoid CN5",
"Fetal kidney CN6", "Organoid CN6",
"Fetal kidney CN7", "Organoid CN7"
)
f2E <- SplitDotPlotGG(comb.neph, "Group", genes, gene.groups,
cols.use = c("#F47920", "#7A52C7"), do.return = TRUE) +
scale_y_discrete(labels = clust.labs) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
ggsave(here("output", DOCNAME, "figure2E.png"), f2E,
height = 4, width = 20)
ggsave(here("output", DOCNAME, "figure2E.pdf"), f2E,
height = 8, width = 10)
f2E
```
Figure 2 Panel
==============
```{r fig2-panel, fig.height = 20, fig.width = 16}
p1 <- plot_grid(f2A + theme(legend.position = "none"),
f2B + theme(legend.position = "none"),
lAB, nrow = 1, rel_widths = c(1, 1, 0.3),
labels = c("A", "B", ""))
p2 <- plot_grid(f2C, f2D, nrow = 1,
labels = c("C", "D", ""))
panel <- plot_grid(p1, p2, f2E, ncol = 1, labels = c("", "", "E"),
rel_heights = c(1, 1, 0.5))
ggsave(here("output", DOCNAME, "figure2_panel.png"), panel,
height = 20, width = 16)
ggsave(here("output", DOCNAME, "figure2_panel.pdf"), panel,
height = 20, width = 16)
panel
```
Summary
=======
Output files
------------
This table describes the output files produced by this document. Right click
and _Save Link As..._ to download the results.
```{r output}
kable(data.frame(
File = c(
glue("[figure2A.png]({getDownloadURL('figure2A.png', DOCNAME)})"),
glue("[figure2A.pdf]({getDownloadURL('figure2A.pdf', DOCNAME)})"),
glue("[figure2B.png]({getDownloadURL('figure2B.png', DOCNAME)})"),
glue("[figure2B.pdf]({getDownloadURL('figure2B.pdf', DOCNAME)})"),
glue("[figure2C.png]({getDownloadURL('figure2C.png', DOCNAME)})"),
glue("[figure2C.pdf]({getDownloadURL('figure2C.pdf', DOCNAME)})"),
glue("[figure2D.png]({getDownloadURL('figure2D.png', DOCNAME)})"),
glue("[figure2D.pdf]({getDownloadURL('figure2D.pdf', DOCNAME)})"),
glue("[figure2E.png]({getDownloadURL('figure2E.png', DOCNAME)})"),
glue("[figure2E.pdf]({getDownloadURL('figure2E.pdf', DOCNAME)})"),
glue("[figure2_panel.png]({getDownloadURL('figure2_panel.png', DOCNAME)})"),
glue("[figure2_panel.pdf]({getDownloadURL('figure2_panel.pdf', DOCNAME)})")
),
Description = c(
"Figure 1A in PNG format",
"Figure 1A in PDF format",
"Figure 1B in PNG format",
"Figure 1B in PDF format",
"Figure 1C in PNG format",
"Figure 1C in PDF format",
"Figure 1D in PNG format",
"Figure 1D in PDF format",
"Figure 1E in PNG format",
"Figure 1E in PDF format",
"Figure panel in PNG format",
"Figure panel in PDF format"
)
))
```