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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")
# Presentation
library("glue")
library("knitr")
# Parallel
# Paths
library("here")
# Output
# Tidyverse
library("tidyverse")
```
```{r source, cache = FALSE}
source(here("R/output.R"))
source(here("R/crossover.R"))
```
```{r paths}
comb.path <- here("data/processed/Combined_clustered.Rds")
comb.neph.path <- here("data/processed/Combined_nephron.Rds")
o.path <- here("output/04_Organoids_Clustering/cluster_assignments.csv")
on.path <- here("output/04B_Organoids_Nephron/cluster_assignments.csv")
c.path <- here("output/07_Combined_Clustering/cluster_assignments.csv")
cn.path <- here("output/07B_Combined_Nephron/cluster_assignments.csv")
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)
}
```
```{r load-clusts, cache.extra = c(tools::md5sum(o.path), tools::md5sum(on.path), tools::md5sum(c.path), tools::md5sum(cn.path))}
orgs.clusts <- read_csv(o.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(Organoids = Cluster)
orgs.neph.clusts <- read_csv(on.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(OrgsNephron = Cluster)
comb.clusts <- read_csv(c.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(Combined = Cluster)
comb.neph.clusts <- read_csv(cn.path,
col_types = cols(
Cell = col_character(),
Dataset = col_character(),
Sample = col_integer(),
Barcode = col_character(),
Cluster = col_integer()
)) %>%
rename(CombNephron = Cluster)
clusts <- comb.clusts %>%
left_join(comb.neph.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode")) %>%
left_join(orgs.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode")) %>%
left_join(orgs.neph.clusts,
by = c("Cell", "Dataset", "Sample", "Barcode"))
```
Figure 2
========
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("#EC008C", "#00ADEF")) +
guides(colour = guide_legend(ncol = 1,
override.aes = list(size = 10))) +
theme_cowplot() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.justification = "center")
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, FIBIN, DLK1",
"C10 (Nephron)\nIGFBP7, EMBX2, MAL",
"C11 (Glial)\nTTYH1, FABP7, SOX2",
"C12 (Immune)\nPTPRC, TYROBP, FCER1G",
"C13 (Blood)\nHBG1, HBB, HBA2",
"C14 (Neural prog.)\nSOX11, ELAVL3, ELAVL4",
"C15 (Podocyte)\nPTPRO, TCF21, PODXL"
)
f2B <- 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_colour_discrete(labels = clust.labs) +
guides(colour = guide_legend(nrow = 3, override.aes = list(size = 10),
label.theme = element_text(size = 10))) +
theme_cowplot() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.justification = "center")
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}
l2A <- get_legend(f2A)
l2B <- get_legend(f2B)
l2AB <- plot_grid(l2A, l2B, nrow = 1, rel_widths = c(0.15, 1))
l2AB
```
Figure 2C
---------
```{r fig-2C}
f2C <- summariseClusts(clusts, Combined, Organoids) %>%
replace_na(list(Jaccard = 0)) %>%
ggplot(aes(x = Combined, y = Organoids, fill = Jaccard)) +
geom_tile() +
scale_fill_viridis_c(limits = c(0, 1), name = "Jaccard\nindex") +
scale_x_discrete(labels = paste0("C", 0:15)) +
scale_y_discrete(labels = paste0("O", 0:12)) +
coord_equal() +
xlab("Combined clusters") +
ylab("Organoid clusters") +
theme_minimal() +
theme(axis.text = element_text(size = 10, colour = "black"),
axis.ticks = element_blank(),
axis.title = element_text(size = 15),
legend.key.height = unit(30, "pt"),
legend.title = element_text(size = 15),
legend.text = element_text(size = 10),
panel.grid = 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}
labs <- paste0("C", 0:max(as.numeric(comb@meta.data$Cluster)))
plot.data <- comb@meta.data %>%
select(Cluster, Group) %>%
mutate(Cluster = paste0("C", Cluster)) %>%
mutate(Cluster = factor(Cluster, levels = labs)) %>%
mutate(Group = if_else(Group == "Lindstrom", "Fetal kidney", Group))
f2D <- ggplot(plot.data, aes(x = Cluster, fill = Cluster)) +
geom_bar() +
labs(y = "Number of cells") +
facet_wrap(~ Group, ncol = 1, scales = "free_y") +
theme_cowplot() +
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.text = element_text(size = 10),
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}
plot.data <- comb@meta.data %>%
select(Cluster, Group) %>%
mutate(Tissue = fct_collapse(Cluster,
Stroma = c("0", "1", "2", "3", "9"),
Nephron = c("6", "7", "10", "15"),
Endothelial = c("4"),
`Cell cycle` = c("5", "8"),
Other = c("11", "12", "13", "14"))) %>%
mutate(Tissue = fct_relevel(Tissue,
"Stroma", "Nephron", "Endothelial",
"Cell cycle", "Other")) %>%
group_by(Group, Tissue) %>%
summarise(Count = n()) %>%
mutate(Prop = Count / sum(Count)) %>%
ungroup() %>%
mutate(Group = if_else(Group == "Lindstrom", "Fetal kidney", Group))
f2E <- ggplot(plot.data, aes(x = Group, y = Prop, fill = Tissue)) +
geom_col() +
labs(y = "Proportion of cells") +
guides(fill = guide_legend(direction = "vertical", ncol = 2)) +
theme_cowplot() +
theme(axis.title.x = element_blank(),
legend.title = element_blank(),
legend.position = "bottom")
ggsave(here("output", DOCNAME, "figure2E.png"), f2E,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure2E.pdf"), f2E,
height = 8, width = 10)
f2E
```
Figure 2F
---------
```{r fig-2F}
genes <- c("HSPA1A", "HSPA1B", "HBG2", "HSP90AA1", "DNAJB1", "FOS", "HSPA6",
"HBG1", "JUN", "GPC3", "MARCKSL1", "MARCKS", "RPS26", "YBX1",
"HMGN2", "EEF1A1", "RPL41", "RPL39", "H3F3A", "RPS17")
gene.groups <- c(rep("Up-regulated in fetal kidney", 10),
rep("Up-regulated in organoid", 10)) %>%
fct_relevel("Up-regulated in fetal kidney", "Up-regulated in organoid")
names(gene.groups) <- genes
group.labs <- c("Fetal kidney", "Organoid")
plot.data <- data.frame(FetchData(comb.neph, vars.all = genes)) %>%
rownames_to_column("Cell") %>%
mutate(Group = comb.neph@meta.data$Group) %>%
gather(key = "Gene", value = "Expr", -Cell, -Group) %>%
group_by(Group, Gene) %>%
summarize(AvgExpr = mean(expm1(Expr)),
PctExpr = Seurat:::PercentAbove(Expr, threshold = 0) * 100) %>%
group_by(Gene) %>%
mutate(AvgExprScale = scale(AvgExpr)) %>%
mutate(AvgExprScale = Seurat::MinMax(AvgExprScale,
max = 2.5, min = -2.5)) %>%
ungroup() %>%
mutate(GeneGroup = gene.groups[Gene]) %>%
mutate(Gene = factor(Gene, levels = genes))
f2F <- ggplot(plot.data,
aes(x = Gene, y = Group, size = PctExpr,
colour = Group, alpha = AvgExprScale)) +
geom_point() +
scale_radius(range = c(0, 10)) +
scale_color_manual(values = c("#EC008C", "#00ADEF")) +
scale_y_discrete(labels = group.labs) +
facet_grid(~ GeneGroup, scales = "free_x") +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
panel.spacing = unit(x = 1, units = "lines"),
strip.background = element_blank(),
strip.placement = "outside",
strip.text = element_text(size = 15,
margin = margin(0, 0, 2, 0, "pt")),
legend.position = "none")
ggsave(here("output", DOCNAME, "figure2F.png"), f2F,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure2F.pdf"), f2F,
height = 8, width = 10)
f2F
```
Figure 2G
---------
```{r fig-2G}
genes.list <- list(
`0` = c("GPC3", "SULT1E1", "CCBE1", "LBH", "CKS2", "STMN2", "NRK", "ACTA2",
"PDGFRA", "ALX1", "COL6A3", "ALDH1A2", "TAGLN"),
`1` = c("DKK2", "WNT2B", "S100A10", "SERPINI1", "PALLD", "DKK1", "ACTA2_1",
"SFRP2", "POSTN", "IGF1", "LUM", "COL1A1", "DCN"),
`2` = c("ANGPT2", "MCAM", "TUBA1B", "ENG", "ID1", "REN", "FTL", "SSBP4",
"PDLIM1", "MEF2C", "PDGFRB", "GATA3", "MGP"),
`3` = c("ROBO2", "ANGPT1", "NR2F1", "ACTA2_2", "SNHG8", "DKK1_1", "VAMP2",
"VIM", "TBX3", "SOX4", "HNRNPA1", "CTSK", "ZFHX4"),
`9` = c("IGF1_1", "ITM2B", "PLPP3", "COL1A1_1", "COL11A1", "COL9A1",
"PEG10", "CNMD", "OGN", "FIBIN", "LUM_1", "DCN_1", "COL9A3"),
`4` = c("CXCL8", "CXCL1", "CXCL2", "ICAM1", "HEY1", "CXCR4", "APLN",
"PECAM1", "PLXND1", "CD34", "ESAM", "CLDN5", "GNG11"),
`11` = c("PMP22", "MPZ", "ERBB3", "ARHGAP15", "MSX1", "ZFP36L1", "FABP7",
"AP1S2", "VIM_1", "SOX2", "PLP1", "TTYH1", "S100B"),
`14` = c("STMN1", "NCAM1", "RBP1", "ATOH1", "ELAVL4", "SOX11", "ELAVL3",
"DCX", "TUBA1A", "STMN2_1", "CRABP1", "TUBB2B", "HES6"),
`12` = c("CXCL8_1", "IL1B", "HLA-DPA1", "HLA-DPB1", "LGALS1", "CTSG", "MPO",
"PRTN3", "TYROBP", "S100A9", "SRGN", "CD74", "PTPRC"),
`13` = c("HBA1", "HBA2", "HBB", "HBG1", "AHSP", "HBM", "GNG5", "EGFL7",
"FSCN1", "FKBP1A", "GPX1", "SLC25A3", "GYPC")
)
clust.labs <- c("C0\nStr", "C1\nStr", "C2\nStr", "C3\nStr", "C9\nStr",
"C4\nEndo", "C11\nGlia", "C14\nNeural", "C12\nImmune",
"C13\nBlood")
plot.data <- lapply(names(genes.list), function(name) {
genes.raw <- genes.list[[name]]
genes <- str_remove(genes.raw, "_[0-9]")
names(genes.raw) <- genes
comb %>%
FetchData(vars.all = genes) %>%
as_data_frame() %>%
rownames_to_column("Cell") %>%
mutate(Cluster = as.numeric(as.character(comb@ident)),
Group = comb@meta.data$Group) %>%
gather(key = "Gene", value = "Expr", -Cell, -Cluster, -Group) %>%
group_by(Cluster, Gene, Group) %>%
summarize(AvgExpr = mean(expm1(Expr)),
PctExpr = Seurat:::PercentAbove(Expr, threshold = 0) * 100) %>%
group_by(Gene) %>%
mutate(AvgExprScale = scale(AvgExpr)) %>%
mutate(AvgExprScale = Seurat::MinMax(AvgExprScale,
max = 2.5, min = -2.5)) %>%
filter(Cluster == as.numeric(name)) %>%
ungroup() %>%
mutate(Gene = genes.raw[Gene])
})
plot.data <- plot.data %>%
bind_rows() %>%
mutate(Cluster = factor(Cluster,
levels = names(genes.list),
labels = clust.labs)) %>%
mutate(Gene = factor(Gene, levels = unlist(genes.list)))
f2G <- ggplot(plot.data,
aes(x = Group, y = Gene, size = PctExpr,
colour = Group, alpha = AvgExprScale)) +
geom_point() +
scale_radius(range = c(0, 8)) +
scale_alpha(range = c(0.1, 1)) +
scale_colour_manual(values = c("#EC008C", "#00ADEF")) +
scale_x_discrete(labels = c("Fetal kidney", "Organoid")) +
scale_y_discrete(labels = str_remove(unlist(genes.list), "_[0-9]"),
breaks = unlist(genes.list)) +
facet_wrap(~ Cluster, scales = "free", nrow = 1) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
panel.spacing = unit(x = 1, units = "lines"),
strip.background = element_blank(),
strip.placement = "outside",
legend.position = "none")
ggsave(here("output", DOCNAME, "figure2G.png"), f2G,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure2G.pdf"), f2G,
height = 8, width = 10)
f2G
```
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"),
nrow = 1, labels = c("A", "B"),
label_size = 20)
p2 <- plot_grid(f2C, f2D, f2E,
nrow = 1, rel_widths = c(1.1, 1, 0.5),
labels = c("C", "D", "E"),
label_size = 20)
panel <- plot_grid(p1, l2AB, p2, f2F, f2G, ncol = 1,
labels = c("", "", "", "F", "G"),
rel_heights = c(1, 0.22, 0.7, 0.4, 0.9),
label_size = 20)
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
```
Figure 3
========
Figure 3A
---------
```{r fig-3A}
plot.data <- comb.neph %>%
GetDimReduction("tsne", slot = "cell.embeddings") %>%
data.frame() %>%
rownames_to_column("Cell") %>%
mutate(Group = comb.neph@meta.data$Group) %>%
mutate(Group = if_else(Group == "Lindstrom", "Fetal kidney", Group))
f3A <- ggplot(plot.data, aes(x = tSNE_1, y = tSNE_2, colour = Group)) +
geom_point(size = 3) +
scale_color_manual(values = c("#EC008C", "#00ADEF")) +
guides(colour = guide_legend(ncol = 4, override.aes = list(size = 12),
label.theme = element_text(size = 12))) +
theme_cowplot() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.justification = "center")
ggsave(here("output", DOCNAME, "figure3A.png"), f3A,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure3A.pdf"), f3A,
height = 8, width = 10)
f3A
```
Figure 3B
---------
```{r fig-3B}
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 progenitor)\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"
)
f3B <- ggplot(plot.data, aes(x = tSNE_1, y = tSNE_2, colour = Cluster)) +
geom_point(size = 3) +
geom_text(data = lab.data, aes(label = Label), colour = "black", size = 8) +
#scale_color_brewer(palette = "Set1", labels = clust.labs) +
scale_colour_discrete(labels = clust.labs) +
guides(colour = guide_legend(ncol = 4, override.aes = list(size = 12),
label.theme = element_text(size = 12))) +
theme_cowplot() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.justification = "center")
ggsave(here("output", DOCNAME, "figure3B.png"), f3B,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure3B.pdf"), f3B,
height = 8, width = 10)
f3B
```
Figure 3AB legend
-----------------
```{r fig-3AB-legend}
l3A <- get_legend(f3A)
l3B <- get_legend(f3B)
l3AB <- plot_grid(l3A, l3B, nrow = 1, rel_widths = c(0.3, 1))
l3AB
```
Figure 3C
---------
```{r fig-3C}
f3C <- summariseClusts(clusts, CombNephron, OrgsNephron) %>%
replace_na(list(Jaccard = 0)) %>%
ggplot(aes(x = CombNephron, y = OrgsNephron, fill = Jaccard)) +
geom_tile() +
scale_fill_viridis_c(limits = c(0, 1), name = "Jaccard\nindex") +
scale_x_discrete(labels = paste0("CN", 0:7)) +
scale_y_discrete(labels = paste0("ON", 0:4)) +
coord_equal() +
xlab("Combined nephron clusters") +
ylab("Organoid nephron clusters") +
theme_minimal() +
theme(axis.text = element_text(size = 10, colour = "black"),
axis.ticks = element_blank(),
axis.title = element_text(size = 16),
legend.key.height = unit(30, "pt"),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
panel.grid = element_blank())
ggsave(here("output", DOCNAME, "figure3C.png"), f3C,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure3C.pdf"), f3C,
height = 8, width = 10)
f3C
```
Figure 3D
---------
```{r fig-3D}
plot.data <- comb.neph@meta.data %>%
select(NephCluster, Group) %>%
mutate(NephCluster = paste0("CN", NephCluster)) %>%
mutate(Group = if_else(Group == "Lindstrom", "Fetal kidney", Group))
f3D <- ggplot(plot.data, aes(x = NephCluster, fill = NephCluster)) +
geom_bar() +
#scale_fill_brewer(palette = "Set1") +
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, "figure3D.png"), f3D,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure3D.pdf"), f3D,
height = 8, width = 10)
f3D
```
Figure 3E
---------
```{r fig-3E}
genes <- c( "TCF21", "ROBO2", "NPHS2", "NPHS1", "SYNPO",
"LYPD1", "PAX8", "DAPL1", "CDH6", "PAX2", "HEY1",
"SIX1", "TMEM100", "CITED1", "MEOX1", "EYA1", "CRABP2",
"CTGF", "OLFM3", "MAFB", "PODXL", "VAMP8",
"WFDC2", "EMX2", "MAL", "MECOM", "TMEM52B",
"IGFBP7", "FXYD2", "CLU", "MPC2",
"COL1A1", "COL3A1", "SNAI2", "POSTN", "LGALS1",
"PTPRO", "TPPP3", "CLIC5", "ENPEP", "GSN", "TSPAN2")
gene.groups <- rev(c(rep("Podocyte", 5),
rep("Differentiating\nnephron progenitor", 6),
rep("Nephron\nprogenitor", 6),
rep("Podocyte\nprecursor", 5),
rep("Distal", 5),
rep("Proximal", 4),
rep("Stroma", 5),
rep("hFK\nPodocyte", 6))) %>%
fct_relevel("Podocyte", "Differentiating\nnephron progenitor",
"Nephron\nprogenitor", "Podocyte\nprecursor", "Distal",
"Proximal", "Stroma", "hFK\nPodocyte")
clust.labs <- c(
"hFK CN0", "Org CN0",
"hFK CN1", "Org CN1",
"hFK CN2", "Org CN2",
"hFK CN3", "Org CN3",
"hFK CN4", "Org CN4",
"hFK CN5", "Org CN5",
"hFK CN6", "Org CN6",
"hFK CN7", "Org CN7"
)
f3E <- SplitDotPlotGG(comb.neph, "Group", genes, gene.groups,
cols.use = c("#EC008C", "#00ADEF"), dot.scale = 5,
do.return = TRUE) +
scale_y_discrete(labels = clust.labs) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.text.x = element_text(margin = margin(0, 0, 2, 0, "pt")))
for (y in seq(2.5, 14.5, 2)) {
f3E <- f3E + geom_hline(yintercept = y, size = 0.2, colour = "grey70")
}
ggsave(here("output", DOCNAME, "figure3E.png"), f3E,
height = 4, width = 20)
ggsave(here("output", DOCNAME, "figure3E.pdf"), f3E,
height = 8, width = 10)
f3E
```
Figure 3F
---------
```{r fig-3F}
genes.list <- list(
c("LYPD1", "PDGFC", "ITGA8", "TUBA1A", "PTPRO", "WT1", "ANXA1", "VAMP8"),
c("CCND1", "CXCR4", "TUBA1B", "STMN1", "ATOX1", "BNIP3", "GNAS", "FTL"),
c("TMEM100", "MEOX1", "ROBO2", "DAPL1", "MYOD1", "MYOG", "PITX2", "TPM2"),
c("HNRNPR", "CXCL1", "OLFM3", "CCND1_1", "PTPRO_1", "WT1_1", "VEGFA",
"SYNPO"),
c("PAPPA2", "LIMCH1", "MAL", "PTGR1", "CITED2", "FTL_1", "BNIP3_1",
"LHX1"),
c("VCAN", "PSMA2", "CA2", "CCND1_2", "VAMP8_1", "BNIP3_2", "LHX1_1",
"HNF1B"),
c("SFRP1", "CTDNEP1", "HNRNPAB", "PCSK1N", "PITX2_1", "H1FX", "MID1",
"LINC00632"),
c("TNNI1", "TNNT2", "MRGPRF", "MYL9", "COL4A4", "TUBB2A", "HMGCS1", "MSMO1")
)
clust.labs <- c("CN0\nPodocyte", "CN1\nDiff. NP", "CN2\nNP", "CN3\nPod. Pre.",
"CN4\nDistal", "CN5\nProximal", "CN6\nStroma", "CN7\nPodocyte")
plot.data <- lapply(seq_along(genes.list), function(idx) {
genes.raw <- genes.list[[idx]]
genes <- str_remove(genes.raw, "_[0-9]")
names(genes.raw) <- genes
comb.neph %>%
FetchData(vars.all = genes) %>%
as_data_frame() %>%
rownames_to_column("Cell") %>%
mutate(Cluster = as.numeric(as.character(comb.neph@ident)),
Group = comb.neph@meta.data$Group) %>%
gather(key = "Gene", value = "Expr", -Cell, -Cluster, -Group) %>%
group_by(Cluster, Gene, Group) %>%
summarize(AvgExpr = mean(expm1(Expr)),
PctExpr = Seurat:::PercentAbove(Expr, threshold = 0) * 100) %>%
group_by(Gene) %>%
mutate(AvgExprScale = scale(AvgExpr)) %>%
mutate(AvgExprScale = Seurat::MinMax(AvgExprScale,
max = 2.5, min = -2.5)) %>%
filter(Cluster == idx - 1) %>%
ungroup() %>%
mutate(Gene = genes.raw[Gene])
})
plot.data <- plot.data %>%
bind_rows() %>%
mutate(Cluster = factor(Cluster, labels = clust.labs)) %>%
mutate(Gene = factor(Gene, levels = unlist(genes.list)))
f3F <- ggplot(plot.data,
aes(x = Group, y = Gene, size = PctExpr,
colour = Group, alpha = AvgExprScale)) +
geom_point() +
scale_radius(range = c(0, 10)) +
scale_alpha(range = c(0.1, 1)) +
scale_colour_manual(values = c("#EC008C", "#00ADEF")) +
scale_x_discrete(labels = c("Fetal kidney", "Organoid")) +
scale_y_discrete(labels = str_remove(unlist(genes.list), "_[0-9]"),
breaks = unlist(genes.list)) +
facet_wrap(~ Cluster, scales = "free", nrow = 1) +
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
panel.spacing = unit(x = 1, units = "lines"),
strip.background = element_blank(),
strip.placement = "outside",
legend.position = "none")
ggsave(here("output", DOCNAME, "figure3F.png"), f3F,
height = 8, width = 10)
ggsave(here("output", DOCNAME, "figure3F.pdf"), f3F,
height = 8, width = 10)
f3F
```
Figure 3 Panel
--------------
```{r fig3-panel, fig.height = 20, fig.width = 16}
p1 <- plot_grid(f3A + theme(legend.position = "none"),
f3B + theme(legend.position = "none"),
nrow = 1, labels = c("A", "B"),
label_size = 20)
p2 <- plot_grid(f3C, f3D,
nrow = 1, labels = c("C", "D"),
label_size = 20)
p3 <- plot_grid(p1,
l3AB,
p2,
ncol = 1, rel_heights = c(1, 0.2, 0.6))
panel <- plot_grid(p3, f3E, f3F, ncol = 1, labels = c("", "E", "F"),
rel_heights = c(1, 0.4, 0.4),
label_size = 20)
ggsave(here("output", DOCNAME, "figure3_panel.png"), panel,
height = 20, width = 16)
ggsave(here("output", DOCNAME, "figure3_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("[figure2F.png]({getDownloadURL('figure2F.png', DOCNAME)})"),
glue("[figure2F.pdf]({getDownloadURL('figure2F.pdf', DOCNAME)})"),
glue("[figure2G.png]({getDownloadURL('figure2G.png', DOCNAME)})"),
glue("[figure2G.pdf]({getDownloadURL('figure2G.pdf', DOCNAME)})"),
glue("[figure2_panel.png]",
"({getDownloadURL('figure2_panel.png', DOCNAME)})"),
glue("[figure2_panel.pdf]",
"({getDownloadURL('figure2_panel.pdf', DOCNAME)})"),
glue("[figure3A.png]({getDownloadURL('figure3A.png', DOCNAME)})"),
glue("[figure3A.pdf]({getDownloadURL('figure3A.pdf', DOCNAME)})"),
glue("[figure3B.png]({getDownloadURL('figure3B.png', DOCNAME)})"),
glue("[figure3B.pdf]({getDownloadURL('figure3B.pdf', DOCNAME)})"),
glue("[figure3C.png]({getDownloadURL('figure3C.png', DOCNAME)})"),
glue("[figure3C.pdf]({getDownloadURL('figure3C.pdf', DOCNAME)})"),
glue("[figure3D.png]({getDownloadURL('figure3D.png', DOCNAME)})"),
glue("[figure3D.pdf]({getDownloadURL('figure3D.pdf', DOCNAME)})"),
glue("[figure3E.png]({getDownloadURL('figure3E.png', DOCNAME)})"),
glue("[figure3E.pdf]({getDownloadURL('figure3E.pdf', DOCNAME)})"),
glue("[figure3F.png]({getDownloadURL('figure3F.png', DOCNAME)})"),
glue("[figure3F.pdf]({getDownloadURL('figure3F.pdf', DOCNAME)})"),
glue("[figure3_panel.png]",
"({getDownloadURL('figure3_panel.png', DOCNAME)})"),
glue("[figure3_panel.pdf]",
"({getDownloadURL('figure3_panel.pdf', DOCNAME)})")
),
Description = c(
"Figure 2A in PNG format",
"Figure 2A in PDF format",
"Figure 2B in PNG format",
"Figure 2B in PDF format",
"Figure 2C in PNG format",
"Figure 2C in PDF format",
"Figure 2D in PNG format",
"Figure 2D in PDF format",
"Figure 2E in PNG format",
"Figure 2E in PDF format",
"Figure 2F in PNG format",
"Figure 2F in PDF format",
"Figure 2G in PNG format",
"Figure 2G in PDF format",
"Figure 2 panel in PNG format",
"Figure 2 panel in PDF format",
"Figure 3A in PNG format",
"Figure 3A in PDF format",
"Figure 3B in PNG format",
"Figure 3B in PDF format",
"Figure 3C in PNG format",
"Figure 3C in PDF format",
"Figure 3D in PNG format",
"Figure 3D in PDF format",
"Figure 3E in PNG format",
"Figure 3E in PDF format",
"Figure 3F in PNG format",
"Figure 3F in PDF format",
"Figure 3 panel in PNG format",
"Figure 3 panel in PDF format"
)
))
```
Session information
-------------------
```{r session-info, cache = FALSE}
devtools::session_info()
```