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320_cancer_cell_plotting_umap.Rmd
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---
title: "Cancer cell embeddings"
subtitle: "MSK SPECTRUM"
author:
- "Florian Uhlitz"
- "Ignacio Vazquez-Garcia"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
df_print: kable
number_sections: yes
toc: yes
code_folding: hide
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = F)
```
# Figure 3
```{r chunk_320_010}
library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
# library(colorblindr)
library(viridis)
# library(magick, lib.loc = "/home/uhlitzf/miniconda3/lib/R/library")
library(ggpubr)
```
```{r chunk_320_020}
## load global vars:
source("src/global_vars.R")
# scrna_meta_tbl
# clrs
# markers_v7
# markers_v7_super
# cell_type_super_lookup
```
```{r chunk_320_040}
## load full seurat objects with expression data
# seu_obj_tc <- read_rds("/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v5/T.cell_processed_filtered_sub.rds")
# seu_obj_cc <- read_rds(paste0("/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v5/Ovarian.cancer.cell_processed_filtered.rds"))
seu_obj_cc <- read_rds("/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v7/Ovarian.cancer.super_processed_filtered_annotated.rds")
marker_tbl <- read_tsv("/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v7/supplementary_tables/Ovarian.cancer.super_marker_table_annotated.tsv")
marker_tbl_top <- marker_tbl %>%
filter(avg_logFC > 0.5,
p_val_adj < 0.01,
pct.1 > 0.2,
pct.2 < 0.8,
!is.na(cluster_label)) %>%
group_by(cluster_label) %>%
slice(1:50)
myfeatures <- c("UMAP_1", "UMAP_2", "umapharmony_1", "umapharmony_2", "sample", "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score", "cell_type")
my_subtypes <- names(clrs$cluster_label$Ovarian.cancer.super)
coi <- "Ovarian.cancer.super"
<<<<<<< HEAD
mois <- c("HRD-Dup", "HRD-Del", "FBI")
gois <- c("HLA-A", "HLA-B", "HLA-C", "B2M", "HLA-DRA", "HLA-DRB1", "CD274")
sois <- c("Adnexa", "Omentum", "Ascites")
=======
sois <- c("HRD-Dup", "HRD-Del", "FBI", "TD")
gois <- c("HLA-A", "HLA-B", "HLA-C", "B2M", "HLA-DRA", "HLA-DRB1", "CD274")
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
pois_short <- c("JAK.STAT", "NFkB", "TNFa", "TGFb", "Hypoxia")
pois <- paste0(pois_short, ".pathway")
all_pws <- grep("pathway", colnames([email protected]), value = T)
all_pws_short <- str_remove_all(all_pws, "\\.pathway")
```
```{r chunk_320_050}
plot_data <- cbind(cell_id = colnames(seu_obj_cc), FetchData(seu_obj_cc, c("umapharmony_1", "umapharmony_2", "umappca_1", "umappca_2", "RNA_snn_res.0.2", "sample", "cluster_label", gois, all_pws))) %>%
as_tibble() %>%
left_join(scrna_meta_tbl, by = "sample") %>%
filter(!is.na(consensus_signature)) %>%
mutate(tumor_megasite = str_replace_all(tumor_megasite, "Other", "Non-adnexa")) %>%
mutate(cluster_label = ordered(cluster_label, levels = names(clrs$cluster_label$Ovarian.cancer.super)),
cluster_number = as.numeric(cluster_label)) %>%
mutate(umapscaled_1 = scales::rescale(umapharmony_1),
umapscaled_2 = scales::rescale(umapharmony_2))
base_umap <- ggplot(plot_data) +
coord_fixed() +
NoAxes() +
theme(legend.position = c(0, 1),
legend.justification = c("left", "top"),
legend.box.just = "left",
legend.margin = margin(1, 1, 1, 1),
legend.text = element_text(size = 14, margin = margin(0, 10, 0, 0)),
legend.spacing.x = unit(0, "npc"),
legend.title = element_blank(),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 22))
```
```{r chunk_320_060}
pt.size <- 0.1
pt.size2 <- 0.2
pt.size.mini <- 0.01
pt.alpha <- 0.05
pt.alpha.mini <- 0.02
dpi <- 150
median_tbl <- plot_data %>%
group_by(patient_id_short) %>%
summarise(umappca_1 = median(umappca_1),
umappca_2 = median(umappca_2),
umapscaled_1 = median(umapscaled_1),
umapscaled_2 = median(umapscaled_2))
median_tbl_cluster <- plot_data %>%
group_by(cluster_label, cluster_number) %>%
summarise(umapharmony_1 = median(umapharmony_1),
umapharmony_2 = median(umapharmony_2),
umapscaled_1 = median(umapscaled_1),
umapscaled_2 = median(umapscaled_2))
umap_pca_mutsig <- base_umap +
ggrastr::geom_point_rast(aes(umappca_1, umappca_2, color = consensus_signature),
size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
# geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
geom_label(aes(umappca_1, umappca_2, label = patient_id_short), color = "black",
data = median_tbl, label.size = unit(0, "mm"), label.r = unit(0, "mm"),
alpha = 0.5) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
labs(title = "Uncorrected")
umap_mutsig <- base_umap +
ggrastr::geom_point_rast(aes(umapharmony_1, umapharmony_2, color = consensus_signature),
size = pt.size2, alpha = pt.alpha, raster.dpi = dpi) +
scale_color_manual(values = clrs$consensus_signature) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
guides(color = F) +
labs(title = "Corrected (harmony)")
umap_pca_cluster <- base_umap +
ggrastr::geom_point_rast(aes(umappca_1, umappca_2, color = cluster_label),
size = pt.size, alpha = pt.alpha, raster.dpi = dpi) +
geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
scale_color_manual(values = clrs$cluster_label$Ovarian.cancer.super) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
labs(title = "Cluster")
umap_cluster <- base_umap +
ggrastr::geom_point_rast(aes(umapharmony_1, umapharmony_2, color = cluster_label),
size = pt.size2, alpha = pt.alpha, raster.dpi = dpi) +
scale_color_manual(values = clrs$cluster_label$Ovarian.cancer.super) +
geom_point(aes(umapharmony_1, umapharmony_2),
color = "white", alpha = 0.5, size = 6,
data = median_tbl_cluster) +
geom_text(aes(umapharmony_1, umapharmony_2, label = cluster_number),
color = "black",
data = median_tbl_cluster) +
guides(color = guide_legend(override.aes = list(size = 4, alpha = 1),
ncol = 1,
label.position = "right")) +
guides(color = F) +
labs(title = "Cluster") +
annotate(geom = "text",
x = (max(plot_data$umapharmony_1) + min(plot_data$umapharmony_1))/2,
y = min(plot_data$umapharmony_2), color = "black",
label = paste0("n = ", nrow(distinct(plot_data, cell_id))))
cluster_legend <- cowplot::get_legend(umap_pca_cluster)
umap_pca_jak_stat <- base_umap +
# geom_point(aes(umappca_1, umappca_2), color = "grey80",
# size = pt.size, alpha = pt.alpha,
# data = filter(plot_data, JAK.STAT.pathway <= 0)) +
ggrastr::geom_point_rast(aes(umappca_1, umappca_2, color = JAK.STAT.pathway),
size = pt.size, alpha = pt.alpha, raster.dpi = dpi,
data = mutate(plot_data, JAK.STAT.pathway = ifelse(JAK.STAT.pathway > 4, 4, JAK.STAT.pathway))) +
geom_text(aes(umappca_1, umappca_2, label = patient_id_short), data = median_tbl) +
scale_color_gradientn(colours = viridis(9), breaks = c(0, 2, 4), limits = c(min(plot_data$JAK.STAT.pathway), 4), labels = c(0, 2, "≥4")) +
labs(title = "JAK-STAT signaling")
umap_jak_stat <- base_umap +
# geom_point(aes(umapharmony_1, umapharmony_2), color = "grey80",
# size = pt.size, alpha = pt.alpha,
# data = filter(plot_data, JAK.STAT.pathway <= 0)) +
ggrastr::geom_point_rast(aes(umapharmony_1, umapharmony_2, color = JAK.STAT.pathway),
size = pt.size2, alpha = pt.alpha, raster.dpi = dpi,
data = mutate(plot_data, JAK.STAT.pathway = ifelse(JAK.STAT.pathway > 4, 4, JAK.STAT.pathway))) +
scale_color_gradientn(colours = viridis(9), breaks = c(0, 2, 4), limits = c(min(plot_data$JAK.STAT.pathway), 4), labels = c(0, 2, "≥4")) +
labs(title = "JAK-STAT signaling")
umap_genes_data <- plot_data %>%
#sample_n(10000) %>%
select(umapharmony_1, umapharmony_2, gois) %>%
gather(key, value, -umapharmony_1, -umapharmony_2) %>%
mutate(value = ifelse(value > 2, 2, value)) %>%
filter(key %in% gois) %>%
mutate(key = ordered(key, levels = gois)) %>%
filter(key == "CD274")
umap_cd274 <- ggplot() +
ggrastr::geom_point_rast(aes(umapharmony_1, umapharmony_2), color = "grey80",
<<<<<<< HEAD
size = pt.size2, alpha = pt.alpha,
raster.dpi = 15, scale = 0.2,
data = filter(umap_genes_data, value == 0)) +
ggrastr::geom_point_rast(aes(umapharmony_1, umapharmony_2, color = value),
size = 2, alpha = pt.alpha,
raster.dpi = 15, scale = 0.2,
=======
size = pt.size2, alpha = pt.alpha, raster.dpi = 70,
data = filter(umap_genes_data, value == 0)) +
ggrastr::geom_point_rast(aes(umapharmony_1, umapharmony_2, color = value),
size = 2, alpha = pt.alpha, raster.dpi = 70,
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
data = filter(umap_genes_data, value > 0)) +
# scale_color_gradientn(colours = viridis(9)) +
# geom_point(aes(umapharmony_1, umapharmony_2),
# color = "white", alpha = 0.25, size = 6,
# data = median_tbl_cluster) +
# geom_text(aes(umapharmony_1, umapharmony_2, label = cluster_number),
# color = "black",
# data = median_tbl_cluster) +
scale_color_gradientn(colours = viridis(9), labels = c(0, 1, "≥2"),
breaks = c(0, 1, 2), limits = c(0, 2)) +
remove_xaxis +
remove_yaxis +
theme(aspect.ratio = 1) +
facet_wrap(~key, nrow = 1) +
labs(color = "Expr.")
umap_pathways <- plot_data %>%
#sample_n(10000) %>%
select(umapharmony_1, umapharmony_2, contains("pathway")) %>%
gather(key, value, -umapharmony_1, -umapharmony_2) %>%
mutate(value = ifelse(value > 4, 4, value),
value = ifelse(value < -1, -1, value)) %>%
filter(key %in% pois) %>%
mutate(key = ordered(str_remove_all(key, ".pathway"),
levels = str_remove_all(pois, ".pathway"))) %>%
ggplot() +
ggrastr::geom_point_rast(aes(umapharmony_1, umapharmony_2, color = value),
size = pt.size2, alpha = pt.alpha, raster.dpi = 10, scale = 0.1) +
# scale_color_gradientn(colours = viridis(9)) +
geom_point(aes(umapharmony_1, umapharmony_2),
color = "white", alpha = 0.25, size = 6,
data = median_tbl_cluster) +
geom_text(aes(umapharmony_1, umapharmony_2, label = cluster_number),
color = "black",
data = median_tbl_cluster) +
scale_color_gradientn(colours = viridis(9), labels = c("≤-1", 0, 2, "≥4"),
breaks = c(-1, 0, 2, 4), limits = c(-1, 4)) +
remove_xaxis +
remove_yaxis +
theme(aspect.ratio = 1) +
facet_wrap(~key, nrow = 1) +
labs(color = "PROGENy\nscore")
```
## UMAP panels
### pca and harmony grids
```{r chunk_320_070, fig.width=20, fig.height=5}
cancer_grid_pca <- ggdraw() +
draw_plot(add_umap_coord(umap_pca_mutsig),
x = 0, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_pca_cluster + guides(color = F)),
x = 0.24, y = 0, width = 0.25, height = 1) +
draw_grob(cluster_legend, x = 0.5, y = -0.15, height = 1) +
draw_plot(add_umap_coord(umap_pca_jak_stat),
x = 0.7, y = 0, width = 0.25, height = 1)
cancer_grid_harmony <- ggdraw() +
draw_plot(add_umap_coord(umap_mutsig),
x = 0, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_cluster + guides(color = F)),
x = 0.24, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_jak_stat),
x = 0.7, y = 0, width = 0.25, height = 1)
cancer_grid <- ggdraw() +
draw_plot(add_umap_coord(umap_pca_mutsig),
x = 0, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_mutsig + guides(color = F)),
x = 0.24, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_cluster + guides(color = F)),
x = 0.48, y = 0, width = 0.25, height = 1) +
draw_plot(add_umap_coord(umap_jak_stat),
x = 0.73, y = 0, width = 0.25, height = 1)
cancer_grid_pca
cancer_grid_harmony
cancer_grid
# ggsave"figures/320_cancer_cell_plotting_umap/003_umap_grid.png", cancer_grid, width = 20, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_umap_grid_pca.png", cancer_grid_pca, width = 20, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_umap_grid_pca.pdf", cancer_grid_pca, width = 20, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_umap_grid_harmony.png", cancer_grid_harmony, width = 20, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_umap_grid_harmony.pdf", cancer_grid_harmony, width = 20, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_umap_grid.png", cancer_grid, width = 20, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_umap_grid.pdf", cancer_grid, width = 20, height = 5)
```
[figures/320_cancer_cell_plotting_umap/003_umap_grid_pca.png](figures/320_cancer_cell_plotting_umap/003_umap_grid_pca.png)
[figures/320_cancer_cell_plotting_umap/003_umap_grid_pca.pdf](figures/320_cancer_cell_plotting_umap/003_umap_grid_pca.pdf)
[figures/320_cancer_cell_plotting_umap/003_umap_grid_harmony.png](figures/320_cancer_cell_plotting_umap/003_umap_grid_harmony.png)
[figures/320_cancer_cell_plotting_umap/003_umap_grid_harmony.pdf](figures/320_cancer_cell_plotting_umap/003_umap_grid_harmony.pdf)
[figures/320_cancer_cell_plotting_umap/003_umap_grid.png](figures/320_cancer_cell_plotting_umap/003_umap_grid.png)
[figures/320_cancer_cell_plotting_umap/003_umap_grid.pdf](figures/320_cancer_cell_plotting_umap/003_umap_grid.pdf)
[figures/320_cancer_cell_plotting_umap/003_umap_genes.pdf](figures/320_cancer_cell_plotting_umap/003_umap_genes.pdf)
<<<<<<< HEAD
[figures/320_cancer_cell_plotting_umap/003_umap_genes.png](figures/320_cancer_cell_plotting_umap/003_umap_genes.png)
### pathways of interest
```{r chunk_320_072, fig.width=10, fig.height=2.5}
# umap_pathways_grid <- ggdraw() +
# draw_plot(add_umap_coord(umap_pathways + remove_guides), x = 0, y = 0, width = 1, height = 1) +
# draw_grob(get_legend(umap_pathways), x = 0.7, y = -0.25)
# umap_pathways_grid
add_umap_coord(umap_pathways, x_offset = -0.15, textsize = 3)
ggsave("figures/320_cancer_cell_plotting_umap/003_umap_pathways.pdf",
add_umap_coord(umap_pathways, x_offset = -0.15, textsize = 3),
width = 10, height = 2.5)
ggsave("figures/320_cancer_cell_plotting_umap/003_umap_pathways.png",
add_umap_coord(umap_pathways, x_offset = -0.15, textsize = 3),
width = 10, height = 2.5)
```
[figures/320_cancer_cell_plotting_umap/003_umap_pathways.png](figures/320_cancer_cell_plotting_umap/003_umap_pathways.png)
=======
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
[figures/320_cancer_cell_plotting_umap/003_umap_pathways.pdf](figures/320_cancer_cell_plotting_umap/003_umap_pathways.pdf)
## Kernel density UMAPs
```{r chunk_320_073, fig.width=7, fig.height=5}
source("src/umap_kernel.R")
rdbu <- rev(RColorBrewer::brewer.pal(7, "RdBu"))
kernel_umap_wrapper <- function(
kernel_subsets_a = c("FBI"),
kernel_subsets_b = c("HRD-Dup", "HRD-Del", "TD"),
quench = 0.015,
kernel_group_var = consensus_signature,
n = 200,
h = 0.2,
kernel_size = 0.01,
plot_title = paste0(kernel_subsets_a, "\nvs ",
kernel_subsets_b)
) {
kernel_group_var <- enquo(kernel_group_var)
data_tbl <- plot_data
if(as_label(kernel_group_var) == "consensus_signature") {
data_tbl <- plot_data %>% filter(tumor_supersite != "Ascites")
}
if(as_label(kernel_group_var) == "patient_id_short") {
data_tbl <- plot_data %>% filter(tumor_supersite != "Ascites")
}
kernel_tbl <- kde2d_contrast(
data_tbl = data_tbl,
x = umapharmony_1, y = umapharmony_2, kernel_group = !!kernel_group_var,
kernel_subsets_a = kernel_subsets_a, kernel_subsets_b = kernel_subsets_b,
quench = quench, n = n, h = h
) %>%
filter(value.x > 0.001 & value.y > 0.001)
kernel_umap <- ggplot() +
ggrastr::geom_point_rast(aes(umapscaled_1, umapscaled_2),
color = "grey80", size = 0.01, alpha = 0.02,
data = plot_data,
raster.dpi = 10, scale = 0.1) +
ggrastr::geom_point_rast(aes(x, y, color = value_scaled),
data = kernel_tbl, size = kernel_size,
raster.dpi = 10, scale = 0.1) +
# geom_point(aes(umapscaled_1, umapscaled_2), color = "white",
# data = median_tbl_cluster, alpha = 0.1, size = 6) +
geom_text(aes(umapscaled_1, umapscaled_2, label = cluster_number),
color = "black",
data = median_tbl_cluster) +
scale_color_gradientn(colours = rdbu,
values = scales::rescale(c(min(kernel_tbl$value_scaled), 0,
max(kernel_tbl$value_scaled)))) +
guides(color = guide_colorbar(label.position = "right",
title.position = "top",
title.hjust = 0,
title.vjust = 1,
direction = "vertical")) +
theme(aspect.ratio = 1,
legend.key.height = unit(0.08, "npc"),
legend.key.width = unit(0.025, "npc"),
legend.position = c(-0.1, 0.95),
legend.justification = c("left", "top"),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 18)) +
labs(color = paste0("Enrichm."),
title = plot_title) +
remove_xaxis +
remove_yaxis
return(kernel_umap)
}
k1 <- kernel_umap_wrapper("HRD-Dup", c("HRD-Del"), 0.015)
k2 <- kernel_umap_wrapper("HRD-Dup", c("FBI"), 0.015) + remove_guides
k3 <- kernel_umap_wrapper("HRD-Del", c("FBI"), 0.015) + remove_guides
# k4 <- kernel_umap_wrapper("TD", c("FBI", "HRD-Dup", "HRD-Del"), 0.015) + remove_guides
ks1 <- kernel_umap_wrapper("Non-adnexa", c("Adnexa"), 0.015, tumor_megasite)
ks2 <- kernel_umap_wrapper("Ascites", c("Adnexa"), 0.030, tumor_megasite) + remove_guides
ks3 <- kernel_umap_wrapper("Ascites", c("Non-adnexa"), 0.030, tumor_megasite) + remove_guides
<<<<<<< HEAD
kernel_grid <- ggdraw() +
draw_plot(plot_grid(k1 + remove_guides,
k2, k3,
ks1 + remove_guides,
ks2, ks3,
nrow = 2, align = "hv"),
width = 0.88, height = 1, x = -0.02) +
draw_grob(get_legend(k1), x = 0.97, y = -0.25)
kernel_grid
ggsave("figures/320_cancer_cell_plotting_umap/320_umap_cancer_kernels.pdf", width = 7, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/320_umap_cancer_kernels.png", width = 7, height = 5)
=======
```{r chunk_320_072, fig.width=10, fig.height=2.5}
# umap_pathways_grid <- ggdraw() +
# draw_plot(add_umap_coord(umap_pathways + remove_guides), x = 0, y = 0, width = 1, height = 1) +
# draw_grob(get_legend(umap_pathways), x = 0.7, y = -0.25)
# umap_pathways_grid
add_umap_coord(umap_pathways, x_offset = -0.15, textsize = 3)
ggsave_png("figures/320_cancer_cell_plotting_umap/003_umap_pathways.png",
add_umap_coord(umap_pathways, x_offset = -0.15, textsize = 3),
width = 10, height = 2.5)
ggsave_pdf("figures/320_cancer_cell_plotting_umap/003_umap_pathways.pdf",
add_umap_coord(umap_pathways, x_offset = -0.15, textsize = 3),
width = 10, height = 2.5)
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
```
```{r chunk_320_074}
patient_ids <- plot_data %>%
filter(tumor_supersite != "Ascites") %>%
filter(consensus_signature %in% mois) %>%
group_by(consensus_signature) %>%
summarise(patient_id_short = unique(patient_id_short)) %>%
arrange(consensus_signature, patient_id_short) %>%
ungroup() %>%
select(patient_id_short, consensus_signature) %>%
deframe()
kernel_umap_wrapper("003", names(patient_ids[patient_ids != "HRD-Dup"]), quench = 0.03, kernel_group_var = patient_id_short, n = 50, h = 0.5, kernel_size = 5) + remove_guides
kernel_list <- list()
kernel_patient_wrapper <- function(csoi) {
lapply(names(patient_ids[patient_ids == csoi]),
function(x) kernel_umap_wrapper(x, names(patient_ids[patient_ids != csoi]), 0.015, patient_id_short, n = 50, h = 0.5, kernel_size = 5, plot_title = x))
}
kernel_list$`HRD-Dup` <- lapply(kernel_patient_wrapper("HRD-Dup"), add, remove_guides)
kernel_list$`HRD-Del` <- lapply(kernel_patient_wrapper("HRD-Del"), add, remove_guides)
kernel_list$`FBI` <- lapply(kernel_patient_wrapper("FBI"), add, remove_guides)
kernel_patient_grid <- plot_grid(plotlist = unlist(kernel_list, recursive = F))
ggsave("figures/320_cancer_cell_plotting_umap/kernel_patient_grid.pdf", kernel_patient_grid, width = 12, height = 12)
```
## Kernel density UMAPs
```{r chunk_320_073}
source("src/umap_kernel.R")
rdbu <- rev(RColorBrewer::brewer.pal(7, "RdBu"))
kernel_umap_wrapper <- function(kernel_subsets_a = c("FBI"),
kernel_subsets_b = c("HRD-Dup", "HRD-Del", "TD"),
quench = 0.015) {
kernel_tbl <- kde2d_contrast(
data_tbl = plot_data %>% filter(tumor_supersite != "Ascites"),
x = umapharmony_1, y = umapharmony_2, kernel_group = consensus_signature,
kernel_subsets_a = kernel_subsets_a, kernel_subsets_b = kernel_subsets_b,
quench = quench
) %>%
filter(value.x > 0.001 & value.y > 0.001)
kernel_umap <- ggplot() +
ggrastr::geom_point_rast(aes(umapscaled_1, umapscaled_2),
color = "grey80", size = 0.01, alpha = 0.02, data = plot_data,
raster.dpi = 50, raster.width = 10, raster.height = 10) +
ggrastr::geom_point_rast(aes(x, y, color = value_scaled), data = kernel_tbl, size = 0.01,
raster.dpi = 50, raster.width = 10, raster.height = 10) +
geom_point(aes(umapscaled_1, umapscaled_2), color = "white",
data = median_tbl_cluster, alpha = 0.1, size = 6) +
geom_text(aes(umapscaled_1, umapscaled_2, label = cluster_number),
color = "black",
data = median_tbl_cluster) +
scale_color_gradientn(colours = rdbu,
values = scales::rescale(c(min(kernel_tbl$value_scaled), 0,
max(kernel_tbl$value_scaled)))) +
guides(color = guide_colorbar(label.position = "right",
title.position = "top",
title.hjust = 0,
title.vjust = 1,
direction = "vertical")) +
theme(aspect.ratio = 1,
legend.key.height = unit(0.05, "npc"),
legend.key.width = unit(0.03, "npc"),
legend.position = c(-0.1, 0.95),
legend.justification = c("left", "top"),
plot.title = element_text(hjust = 0.5, vjust = 0.5, face = "plain", size = 18)) +
labs(color = paste0("Enrichm."),
title = kernel_subsets_a) +
remove_xaxis +
remove_yaxis
return(kernel_umap)
}
k1 <- kernel_umap_wrapper("HRD-Dup", c("FBI", "HRD-Del", "TD"), 0.015)
k2 <- kernel_umap_wrapper("HRD-Del", c("FBI", "HRD-Dup", "TD"), 0.015) + remove_guides
k3 <- kernel_umap_wrapper("FBI", c("HRD-Dup", "HRD-Del", "TD"), 0.015) + remove_guides
k4 <- kernel_umap_wrapper("TD", c("FBI", "HRD-Dup", "HRD-Del"), 0.015) + remove_guides
kernel_grid <- ggdraw() +
draw_plot(plot_grid(k1 + remove_guides, k2, k3, k4, ncol = 2, align = "hv"),
width = 0.8, height = 1) +
draw_grob(get_legend(k1), x = 0.88, y = -0.25)
kernel_grid
ggsave_pdf("figures/320_cancer_cell_plotting_umap/320_umap_cancer_kernels.pdf", width = 4.5, height = 4)
ggsave_png("figures/320_cancer_cell_plotting_umap/320_umap_cancer_kernels.png", width = 4.5, height = 4)
```
## Cluster marker heatmap
```{r chunk_320_075}
plot_data_markers <- as_tibble(FetchData(seu_obj_cc, c("cluster_label", myfeatures, unique(marker_tbl_top$gene)))) %>%
gather(gene, value, -c(1:(length(myfeatures)+1))) %>%
left_join(scrna_meta_tbl, by = "sample") %>%
mutate(cluster_label = ordered(cluster_label, levels = names(clrs$cluster_label$Ovarian.cancer.super))) %>%
group_by(cluster_label, gene) %>%
summarise(value = mean(value, na.rm = T)) %>%
group_by(gene) %>%
mutate(value = scales::rescale(value)) %>%
left_join(select(marker_tbl_top, cluster_label_x = cluster_label, gene), by = "gene") %>%
## reverse names vector to flip row-order in heatmap
mutate(cluster_label_x = ordered(cluster_label_x, levels = names(clrs$cluster_label$Ovarian.cancer.super))) %>%
na.omit()
```
```{r chunk_320_080, fig.width=3.75, fig.height=5}
library(ComplexHeatmap)
highlight_genes <- marker_tbl_top %>%
group_by(cluster_label) %>%
slice(1:2) %>%
## reverse levels also here for row-order flip
mutate(cluster_label_x = ordered(cluster_label, levels = names(clrs$cluster_label[[coi]]))) %>%
ungroup() %>%
select(cluster_label_x, gene) %>%
na.omit %>%
mutate(highlight = T)
plot_data_markers_mat <- plot_data_markers %>%
spread(cluster_label, value) %>%
left_join(highlight_genes, by = c("gene", "cluster_label_x")) %>%
ungroup %>%
arrange(desc(cluster_label_x), gene) %>%
select(cluster_label_x, gene, highlight, everything())
ha_row <- rowAnnotation(
`Cell type` = plot_data_markers_mat$cluster_label_x,
col = list(`Cell type` = clrs$cluster_label[[coi]]),
show_legend = F,
annotation_name_side = "top"
)
ha_col <- columnAnnotation(
`Cell type` = anno_block(gp = gpar(fill = clrs$cluster_label[[coi]], col = NA),
labels = as.character(1:10)),
show_legend = F,
annotation_name_side = "left",
annotation_name_rot = 0
)
gene_idx <- plot_data_markers_mat$highlight == TRUE
ha_genes <- rowAnnotation(
link = anno_mark(
at = which(gene_idx),
labels = plot_data_markers_mat$gene[which(gene_idx)],
labels_gp = gpar(fontsize = 10), padding = unit(1, "mm"),
side = "left",
labels_rot = 0
)
)
marker_heatmap <- Heatmap(
as.matrix(plot_data_markers_mat[,-c(1:3)]),
heatmap_legend_param = list(
title = "Scaled expression",
title_position = "leftcenter-rot"
),
row_order = 1:length(plot_data_markers_mat$cluster_label_x),
row_split = plot_data_markers_mat$cluster_label_x,
column_split = 1:length(colnames(plot_data_markers_mat)[-c(1:3)]),
column_order = colnames(plot_data_markers_mat)[-c(1:3)],
column_names_side = "top",
right_annotation = ha_row,
left_annotation = ha_genes,
top_annotation = ha_col,
cluster_rows = F,
row_title = NULL,
column_title = NULL,
col = viridis(9)
)
# marker_heatmap
heatmap_grob <- grid.grabExpr(draw(marker_heatmap), width = 3.75, height = 5)
heat_grid <- ggdraw() +
draw_grob(heatmap_grob)
heat_grid
ggsave("figures/320_cancer_cell_plotting_umap/003_cluster_marker_heatmap.png", heat_grid, width = 3.75, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_cluster_marker_heatmap.pdf", heat_grid, width = 3.75, height = 5)
```
[figures/320_cancer_cell_plotting_umap/003_cluster_marker_heatmap.png](figures/320_cancer_cell_plotting_umap/003_cluster_marker_heatmap.png)
[figures/320_cancer_cell_plotting_umap/003_cluster_marker_heatmap.pdf](figures/320_cancer_cell_plotting_umap/003_cluster_marker_heatmap.pdf)
## Violins
```{r chunk_320_081, fig.width=22, fig.height=3.5}
plot_data_vio <- plot_data %>%
# sample_n(10000) %>%
<<<<<<< HEAD
select(umapharmony_1, umapharmony_2, cluster_label, consensus_signature, consensus_signature_short, tumor_supersite, tumor_megasite, all_of(gois), all_of(pois), sample_id, patient_id_short) %>%
gather(key, value, -umapharmony_1, -umapharmony_2, -cluster_label, -consensus_signature, -consensus_signature_short, -tumor_supersite, -tumor_megasite, -sample_id, -patient_id_short) %>%
# mutate(value = ifelse(value > 3, 3, value)) %>%
filter(str_detect(cluster_label, "^Cancer.cell")) %>%
mutate(cluster_label_num = str_remove_all(cluster_label, "Cancer.cell.")) %>%
mutate(tumor_megasite = str_replace_all(tumor_megasite, "Other", "Non-adnexa")) %>%
mutate(tumor_megasite = ordered(tumor_megasite, levels = names(clrs$tumor_megasite))) %>%
=======
select(umapharmony_1, umapharmony_2, cluster_label, consensus_signature, consensus_signature_short, gois, pois, sample_id) %>%
gather(key, value, -umapharmony_1, -umapharmony_2, -cluster_label, -consensus_signature, -consensus_signature_short, -sample_id) %>%
# mutate(value = ifelse(value > 3, 3, value)) %>%
filter(str_detect(cluster_label, "^Cancer.cell")) %>%
mutate(cluster_label_num = str_remove_all(cluster_label, "Cancer.cell.")) %>%
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
mutate(key = str_remove_all(key, ".pathway")) %>%
mutate(key = ordered(key, levels = c(gois, pois_short)))
plot_data_vio_summary <- plot_data_vio %>%
group_by(sample_id, key) %>%
mutate(value = mean(value, na.rm = T)) %>%
distinct(sample_id, key, .keep_all = T) %>%
ungroup()
my_comparisons <- list(
c("HRD-Dup", "HRD-Del"),
c("HRD-Dup", "FBI"),
c("HRD-Del", "FBI")
)
my_comparisons2 <- list(
c("HRD", "HRP")
)
<<<<<<< HEAD
# violin_wrapper(cluster_label, "Cluster", pois_short, ".all.", "Expression", c(-1,8), 10, mut_filter = levels(unique(scrna_meta_tbl$consensus_signature)), site_filter = names(clrs$tumor_supersite), facet_site = F)
violin_wrapper <- function(group_column, group_var_label, keys, comps, y_label, ylims, ncol, plabel = "p.signif", mut_filter = mois, site_filter = names(clrs$tumor_supersite), facet_site = T) {
group_column <- enquo(group_column)
yrange <- ylims[2]-ylims[1]
ylabels <- yrange*c(0.85,0.75,0.65)+ylims[1]
clrs$cluster_label <- clrs$cluster_label$Ovarian.cancer.super
p <- plot_data_vio %>%
filter(key %in% keys) %>%
filter(consensus_signature %in% mut_filter) %>%
filter(tumor_supersite %in% site_filter) %>%
=======
boxplot_wrapper <- function(group_column, group_var_label, keys, comps, y_label, ylims, ncol, plabel = "p.signif", sois_filter = sois) {
group_column <- enquo(group_column)
yrange <- ylims[2]-ylims[1]
ylabels <- yrange*c(0.95,0.85,0.75)+ylims[1]
clrs$cluster_label <- clrs$cluster_label$Ovarian.cancer.super
p <- plot_data_vio %>%
filter(key %in% keys) %>%
filter(consensus_signature %in% sois_filter) %>%
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
ggplot(aes(!!group_column, value)) +
geom_violin(aes(!!group_column, value, fill = !!group_column), color = "white", adjust = 2, alpha = 0.5, width = 1.5) +
geom_boxplot(aes(!!group_column, value, color = !!group_column),
width = 0.5, size = 0.75, outlier.shape = NA) +
geom_boxplot(aes(!!group_column, value, fill = !!group_column),
color = "white", width = 0.5, outlier.shape = NA, size = 0.5) +
<<<<<<< HEAD
=======
facet_wrap(~key, ncol = ncol) +
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
scale_color_manual(values = clrs[[as_label(group_column)]]) +
scale_fill_manual(values = clrs[[as_label(group_column)]]) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title.x = element_blank()) +
<<<<<<< HEAD
coord_cartesian(clip = "on", ylim = ylims) +
=======
coord_cartesian(clip = "off", ylim = ylims) +
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
labs(y = y_label, color = group_var_label,
fill = group_var_label) +
remove_guides
<<<<<<< HEAD
if (facet_site) {
p <- p + facet_grid(tumor_megasite~key)
} else {
p <- p + facet_grid(~key)
}
=======
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
if (any(unlist(comps) != ".all.")) {
p <- p +
stat_compare_means(comparisons = comps,
data = filter(plot_data_vio_summary,
key %in% keys,
<<<<<<< HEAD
consensus_signature %in% mut_filter),
=======
consensus_signature %in% sois),
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
label = plabel, label.y = ylabels,
tip.length = 0.01,
hide.ns = F)
}
if (all(unlist(comps) == ".all.")) {
p <- p +
stat_compare_means(ref.group = ".all.",
method.args = list(alternative = "greater"),
data = filter(plot_data_vio_summary,
key %in% keys,
<<<<<<< HEAD
consensus_signature %in% mut_filter),
=======
consensus_signature %in% sois),
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
label.y = ylabels[1], hide.ns = T,
label = plabel)
}
return(p)
}
```
<<<<<<< HEAD
```{r chunk_320_082, fig.width=22, fig.height=3.5}
boxplot_wrapper <- function(group_column, group_var_label, keys, comps, y_label, ylims, ncol, plabel = "p.signif", mut_filter = mois, site_filter = names(clrs$tumor_supersite), facet_site = T) {
group_column <- enquo(group_column)
yrange <- ylims[2]-ylims[1]
ylabels <- yrange*c(0.85,0.75,0.65)+ylims[1]
clrs$cluster_label <- clrs$cluster_label$Ovarian.cancer.super
boxplot_data <- filter(plot_data_vio_summary,
key %in% keys,
consensus_signature %in% mut_filter,
tumor_supersite %in% site_filter,
tumor_megasite %in% c("Adnexa", "Non-adnexa"))
boxplot_data_summary <- boxplot_data %>%
group_by(!!group_column, tumor_megasite, key) %>%
tally
p <- ggplot(boxplot_data, aes(!!group_column, value)) +
geom_boxplot(aes(!!group_column, value, color = !!group_column),
width = 0.5, size = 0.75, outlier.shape = NA) +
geom_boxplot(aes(!!group_column, value, fill = !!group_column),
color = "white", width = 0.5, outlier.shape = NA, size = 0.5) +
geom_jitter(aes(!!group_column, value),
color = "black", width = 0.15, size = 0.25, alpha = 0.5) +
geom_text(aes(x = !!group_column, color = !!group_column, label = n),
y = min(boxplot_data$value)-0.1*(max(boxplot_data$value)-min(boxplot_data$value)),
data = boxplot_data_summary) +
scale_color_manual(values = clrs[[as_label(group_column)]]) +
scale_fill_manual(values = clrs[[as_label(group_column)]]) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title.x = element_blank()) +
coord_cartesian(clip = "on", ylim = ylims) +
labs(y = y_label, color = group_var_label,
fill = group_var_label) +
remove_guides
if (facet_site) {
p <- p + facet_grid(tumor_megasite~key)
} else {
p <- p + facet_grid(~key)
}
if (any(unlist(comps) != ".all.")) {
p <- p +
stat_compare_means(comparisons = comps,
data = boxplot_data,
label = plabel, label.y = ylabels,
tip.length = 0,
hide.ns = F)
}
if (all(unlist(comps) == ".all.")) {
p <- p +
stat_compare_means(ref.group = ".all.",
method.args = list(alternative = "greater"),
data = boxplot_data,
label.y = ylabels[1], hide.ns = T,
label = plabel)
}
return(p)
}
# boxplot_wrapper(consensus_signature, "Mutational\nsignature", pois_short, my_comparisons, "PROGENy score", c(-1,3), 10)
```
```{r chunk_320_083, fig.width=5, fig.height=5}
violins_pw_mutsig_site <- violin_wrapper(consensus_signature, "Mutational\nsignature", pois_short, my_comparisons, "PROGENy score", c(-1,5), 10)
violins_pw_mutsig_site
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_site.pdf",
violins_pw_mutsig_site, width = 5, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_site.png",
violins_pw_mutsig_site, width = 5, height = 5)
violins_pw_mutsig_pval_site <- violin_wrapper(consensus_signature, "Mutational\nsignature", pois_short, my_comparisons, "PROGENy score", c(-1,5), 10, "p.format")
violins_pw_mutsig_pval_site
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_pval_site.pdf",
violins_pw_mutsig_pval_site, width = 5, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_pval_site.png",
violins_pw_mutsig_pval_site, width = 5, height = 5)
```
```{r chunk_320_084, fig.width=5, fig.height=5}
boxplot_pw_mutsig_site <- boxplot_wrapper(consensus_signature, "Mutational\nsignature", pois_short, my_comparisons, "PROGENy score", c(-1.25,3), 10)
boxplot_pw_mutsig_site
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_boxplot_site.pdf",
boxplot_pw_mutsig_site, width = 5, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_boxplot_site.png",
boxplot_pw_mutsig_site, width = 5, height = 5)
boxplot_pw_mutsig_pval_site <- boxplot_wrapper(consensus_signature, "Mutational\nsignature", pois_short, my_comparisons, "PROGENy score", c(-1.25,3), 10, "p.format")
boxplot_pw_mutsig_pval_site
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_boxplot_pval_site.pdf",
boxplot_pw_mutsig_pval_site, width = 5, height = 5)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_boxplot_pval_site.png",
boxplot_pw_mutsig_pval_site, width = 5, height = 5)
```
```{r chunk_320_085, fig.width=5, fig.height=3}
violins_pw_mutsig <- violin_wrapper(consensus_signature, "Mutational\nsignature", pois_short, my_comparisons, "PROGENy score", c(-1,5), 10, site_filter = names(clrs$tumor_supersite), facet_site = F)
violins_pw_mutsig
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins.pdf",
violins_pw_mutsig, width = 5, height = 3)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins.png",
violins_pw_mutsig, width = 5, height = 3)
violins_pw_mutsig_pval <- violin_wrapper(consensus_signature, "Mutational\nsignature", pois_short, my_comparisons, "PROGENy score", c(-1,5), 10, "p.format", site_filter = names(clrs$tumor_supersite), facet_site = F)
violins_pw_mutsig_pval
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_pval.pdf",
violins_pw_mutsig_pval, width = 5, height = 3)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_pval.png",
violins_pw_mutsig_pval, width = 5, height = 3)
```
```{r chunk_320_086, fig.width=5, fig.height=3}
violins_pw_mutsig_short <- violin_wrapper(consensus_signature_short, "Mutational\nsignature", pois_short, my_comparisons2, "PROGENy score", c(-1,5), 10, mut_filter = levels(unique(scrna_meta_tbl$consensus_signature)), site_filter = names(clrs$tumor_supersite), facet_site = F)
violins_pw_mutsig_short
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_short.pdf",
violins_pw_mutsig_short, width = 5, height = 3)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_short.png",
violins_pw_mutsig_short, width = 5, height = 3)
violins_pw_mutsig_short_pval <- violin_wrapper(consensus_signature_short, "Mutational\nsignature", pois_short, my_comparisons2, "PROGENy score", c(-1,5), 10, "p.format", mut_filter = levels(unique(scrna_meta_tbl$consensus_signature)), site_filter = names(clrs$tumor_supersite), facet_site = F)
violins_pw_mutsig_short_pval
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_short_pval.pdf",
violins_pw_mutsig_short_pval, width = 5, height = 3)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_short_pval.png",
violins_pw_mutsig_short_pval, width = 5, height = 3)
```
```{r chunk_320_087, fig.width=8, fig.height=3}
violins_pw_cluster <- violin_wrapper(cluster_label, "Cluster", pois_short, ".all.", "Expression", c(-1,8), 10, mut_filter = levels(unique(scrna_meta_tbl$consensus_signature)), site_filter = names(clrs$tumor_supersite), facet_site = F)
violins_pw_cluster
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_cluster.pdf",
violins_pw_cluster, width = 8, height = 3)
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_cluster.png",
violins_pw_cluster, width = 8, height = 3)
violins_pw_cluster_pval <- violin_wrapper(cluster_label, "Cluster", pois_short, ".all.", "Expression", c(-1,8), 10, "p.format", mut_filter = levels(unique(scrna_meta_tbl$consensus_signature)), site_filter = names(clrs$tumor_supersite), facet_site = F)
violins_pw_cluster_pval
ggsave("figures/320_cancer_cell_plotting_umap/003_pw_violins_cluster_pval.pdf",