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215_composition_plots_subset.Rmd
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215_composition_plots_subset.Rmd
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---
title: "MSK SPECTRUM data freeze: cell type compositions"
author: "Florian Uhlitz"
date: "`r Sys.Date()`"
output:
html_document:
df_print: kable
number_sections: yes
toc: yes
code_folding: hide
editor_options:
chunk_output_type: console
---
```{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)
```
```{r chunk_210_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)
```
# Composition plots
## load data modalities
### scRNA
```{r chunk_210_030}
## load global vars:
source("src/global_vars.R")
source("src/comp_plot.R")
# scrna_meta_tbl
# clrs
# markers_v7
# markers_v7_super
# cell_type_super_lookup
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "\\.", " ")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "Ovarian", "Ov")
## load data --------------------------------------
## load cohort embeddings
seu_tbl <- read_tsv("/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v7/outs_pre/cells.tsv") %>%
mutate(cell_type = ifelse(cell_type == "Monocyte", "Myeloid.cell", cell_type)) %>%
mutate(cell_type = ifelse(cell_type == "Ovarian.cancer.cell", "Ov.cancer.cell", cell_type))
## load consensus data
consOV_tbl <- read_tsv("/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v7/consensusOV/SPECTRUM_freeze_v7_consensusOV.tsv") %>%
mutate(consensusOV = ordered(consensusOV, levels = names(clrs$consensusOV))) %>%
select(cell_id, consensusOV)
## join data
seu_tbl_full <- seu_tbl %>%
left_join(scrna_meta_tbl, by = "sample") %>%
filter(therapy == "pre-Rx", cell_type != "Other", tumor_supersite != "Unknown") %>%
rename(UMAP_1 = umap50_1, UMAP_2 = umap50_2) %>%
mutate(cell_type_super = cell_type_super_lookup[cell_type]) %>%
mutate(cell_type = ordered(str_replace_all(cell_type, "\\.", " "),
levels = names(clrs$cell_type))) %>%
mutate(sort_short_x = ifelse(sort_short == "U" & cell_type_super == "Immune",
"CD45+", ifelse(sort_short == "U" & cell_type_super == "Stromal",
"CD45-", sort_short))) %>%
left_join(consOV_tbl, by = "cell_id")
# seu_tbl_full <- seu_tbl_full %>%
# sample_n(10000)
```
```{r chunk_210_040, fig.width=8, fig.height=4}
add_helper_columns <- . %>%
mutate(label_supersite = "Site",
label_therapy = "Rx",
label_mutsig = "Signature")
comp_tbl_sample <- seu_tbl_full %>%
filter(therapy == "pre-Rx", cell_type != "Other") %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature, cell_type) %>%
tally() %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature) %>%
mutate(nrel = n/sum(n)*100) %>%
add_helper_columns %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
mutate(sample_id = paste(tumor_subsite, patient_id_short, therapy, sort_short_x)) %>%
group_by(sample_id) %>%
mutate(ntotal = sum(n)) %>%
filter(ntotal > 0) %>%
ungroup()
comp_tbl_consOV <- seu_tbl_full %>%
filter(therapy == "pre-Rx", cell_type != "Other") %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature, consensusOV) %>%
tally %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature) %>%
mutate(nrel = n/sum(n)*100,
log10n = log10(n)) %>%
add_helper_columns %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
mutate(sample_id = paste(tumor_subsite, patient_id_short, therapy, sort_short_x)) %>%
ungroup
cois <- c("T.super", "Myeloid.super", "Fibroblast.super", "Ovarian.cancer.super")
read_comp_wrapper <- function(x, cluster_column) {
cluster_column <- enquo(cluster_column)
read_tsv(paste0("/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v7/", x, "_subtype_compositions.tsv")) %>%
mutate(sort_short_x = ifelse(str_detect(sample_id_x, "CD45\\+"), "CD45+", "CD45-")) %>%
complete(sample, !!cluster_column, fill = list(n = 0, nrel = 0)) %>%
left_join(scrna_meta_tbl, by = "sample") %>%
arrange(sample, sample_id_x, !!cluster_column) %>%
group_by(sample) %>%
mutate(sample_id_x = ifelse(is.na(sample_id_x), sample_id_x[1], sample_id_x)) %>%
mutate(sort_short_x = ifelse(is.na(sort_short_x), sort_short_x[1], sort_short_x)) %>%
mutate(nsum = ifelse(is.na(nsum), nsum[1], nsum)) %>%
ungroup %>%
mutate(sample_id = sample_id_x)
}
comp_list <- lapply(cois[-1], read_comp_wrapper, cluster_label) %>% setNames(cois[-1])
comp_list$T.super <- read_comp_wrapper("T.super", cluster_label_sub) %>%
mutate(cell_type_naive = ifelse(str_detect(cluster_label_sub, "mem"), "T naive/mem", "other")) %>%
mutate(cell_type_dysfunc = ifelse(str_detect(cluster_label_sub, "dysfunc"), "T dysfunctional", "other"))
```
### mpIF
```{r chunk_210_050, fig.width=6, fig.height=3}
# mpif_pixel <- read_tsv("/work/shah/vazquezi/data/transfers/spectrum/results/mpif/v10/integrate/outputs/cohort_merge/patient/SPECTRUM/all/detection.tsv") %>%
# select(cell_id, compartment = Parent)
#
# mpif_cell_type <- read_tsv("/work/shah/vazquezi/data/transfers/spectrum/results/mpif/v10/integrate/outputs/cohort_merge/patient/SPECTRUM/all/cell_type_manual.tsv") %>%
# left_join(mpif_pixel, by = "cell_id") %>%
# mutate(cell_id = str_remove_all(cell_id, "CD68.TOX.PD1.PDL1.CD8.panCK_CK8-18.DAPI_|_component_data - resolution #1")) %>%
# separate(cell_id, into = c("patient_id", "tumor_subsite", "fov_id", "cell_idx"),
# sep = "_", remove = F) %>%
# mutate(fov_id = paste0(patient_id, "_", tumor_subsite, "_", fov_id),
# slide_id = paste0(patient_id, "_", tumor_subsite))
#
# ## expand for double and triple positive cells (cell type markers)
# mpif_ncell <- mpif_cell_type %>%
# # sample_n(10000) %>%
# mutate(CD68_state_log = CD68_state == "CD68+",
# CD8_state_log = CD8_state == "CD8+",
# panCK_state_log = panCK_state == "panCK+") %>%
# group_by(cell_id) %>%
# mutate(n_cells = sum(CD68_state_log, CD8_state_log, panCK_state_log)) %>%
# select(cell_id, n_cells) %>%
# deframe
#
# mpif_cell_type_expanded <- bind_rows(
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 0]),
# TOX_state == "TOX+" | PD1_state == "PD1+" | PDL1_state == "PDL1+") %>%
# mutate(cell_id = paste0(cell_id, "_0"),
# cell_type = c("Other")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 0]),
# TOX_state == "TOX-" & PD1_state == "PD1-" & PDL1_state == "PDL1-") %>%
# mutate(cell_id = paste0(cell_id, "_0"),
# cell_type = c("Unknown")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# CD8_state == "CD8+") %>%
# mutate(cell_id = paste0(cell_id, "_1"),
# cell_type = c("CD8+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# CD68_state == "CD68+") %>%
# mutate(cell_id = paste0(cell_id, "_2"),
# cell_type = c("CD68+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 1]),
# panCK_state == "panCK+") %>%
# mutate(cell_id = paste0(cell_id, "_3"),
# cell_type = c("panCK+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# CD8_state == "CD8+") %>%
# mutate(cell_id = paste0(cell_id, "_1"),
# cell_type = c("CD8+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# CD68_state == "CD68+") %>%
# mutate(cell_id = paste0(cell_id, "_2"),
# cell_type = c("CD68+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 2]),
# panCK_state == "panCK+") %>%
# mutate(cell_id = paste0(cell_id, "_3"),
# cell_type = c("panCK+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# CD8_state == "CD8+") %>%
# mutate(cell_id = paste0(cell_id, "_1"),
# cell_type = c("CD8+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# CD68_state == "CD68+") %>%
# mutate(cell_id = paste0(cell_id, "_2"),
# cell_type = c("CD68+")),
# filter(mpif_cell_type, cell_id %in% names(mpif_ncell[mpif_ncell == 3]),
# panCK_state == "panCK+") %>%
# mutate(cell_id = paste0(cell_id, "_3"),
# cell_type = c("panCK+"))
# )
#
# mpif_cell_state_expanded <- mpif_cell_type_expanded %>%
# mutate(cell_state = case_when(
# cell_type == "CD8+" ~ paste0(CD8_state, PD1_state, TOX_state),
# cell_type == "CD68+" ~ paste0(CD68_state, PDL1_state),
# cell_type == "panCK+" ~ paste0(panCK_state, PDL1_state),
# cell_type == "Unknown" ~ "Unknown",
# cell_type == "Other" ~ "Other"
# ))
#
# write_tsv(mpif_cell_state_expanded, "/work/shah/users/uhlitzf/data/SPECTRUM/mpIF/cell_type_manual_expanded_v10.tsv")
mpif_cell_state_expanded <- read_tsv("/work/shah/users/uhlitzf/data/SPECTRUM/mpIF/cell_type_manual_expanded_v10.tsv") %>%
select(cell_id, slide_id, fov_id, compartment, cell_type, cell_state, contains("state")) %>%
left_join(select(mpif_slide_meta_tbl, slide_id, patient_id_short, tumor_supersite, tumor_subsite, therapy, sample_id, consensus_signature, tumor_megasite), by = "slide_id") %>%
na.omit() %>%
mutate(sort_short_x = compartment) %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
rename(cell_phenotype_short_mpif = cell_type,
cell_phenotype_mpif = cell_state)
mpif_cell_state_expanded <- mpif_cell_state_expanded %>%
mutate(cell_type_pd1n = ifelse(cell_phenotype_mpif %in% c("CD8+PD1-TOX-", "CD8+PD1-TOX+"), "CD8+PD1-", "other")) %>%
mutate(cell_type_pd1p = ifelse(cell_phenotype_mpif %in% c("CD8+PD1+TOX-", "CD8+PD1+TOX+"), "CD8+PD1+", "other")) %>%
mutate(cell_type_toxn = ifelse(cell_phenotype_mpif %in% c("CD8+PD1+TOX-", "CD8+PD1-TOX-"), "CD8+TOX-", "other")) %>%
mutate(cell_type_toxp = ifelse(cell_phenotype_mpif %in% c("CD8+PD1+TOX+", "CD8+PD1-TOX+"), "CD8+TOX+", "other"))
## cell state composition fov lvl
mpif_cell_state_n <- mpif_cell_state_expanded %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, fov_id, consensus_signature, cell_phenotype_mpif, cell_phenotype_short_mpif, cell_type_pd1n, cell_type_pd1p, cell_type_toxn, cell_type_toxp) %>%
tally() %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, fov_id, consensus_signature) %>%
mutate(nrel = n/sum(n)) %>%
ungroup() %>%
add_helper_columns
## cell state composition slide lvl
mpif_cell_state_n_slide <- mpif_cell_state_expanded %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, cell_phenotype_mpif, cell_phenotype_short_mpif, cell_type_pd1n, cell_type_pd1p, cell_type_toxn, cell_type_toxp) %>%
tally() %>%
ungroup %>%
complete(sample_id, cell_phenotype_mpif, fill = list(n = 0)) %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature) %>%
mutate(nrel = n/sum(n)) %>%
ungroup() %>%
add_helper_columns %>%
mutate(sort_short_x = "mpIF")
## cell state composition slide lvl compartment
mpif_cell_state_n_slide_compartment <- mpif_cell_state_expanded %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x, cell_phenotype_mpif, cell_phenotype_short_mpif, cell_type_pd1n, cell_type_pd1p, cell_type_toxn, cell_type_toxp) %>%
tally() %>%
ungroup %>%
complete(sample_id, nesting(cell_phenotype_mpif, sort_short_x),
fill = list(n = 0)) %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup() %>%
add_helper_columns
## cell type composition slide lvl compartment
mpif_cell_type_n_slide_compartment <- mpif_cell_state_expanded %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x, cell_phenotype_mpif, cell_phenotype_short_mpif, cell_type_pd1n, cell_type_pd1p, cell_type_toxn, cell_type_toxp) %>%
tally() %>%
ungroup %>%
complete(sample_id, nesting(cell_phenotype_mpif, sort_short_x),
fill = list(n = 0)) %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup() %>%
add_helper_columns
## CD8 cell state composition slide lvl compartment
mpif_cell_state_cd8_n_slide_compartment <- mpif_cell_state_n_slide_compartment %>%
filter(cell_phenotype_short_mpif == "CD8+") %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup()
## CD8 cell state composition slide lvl
mpif_cell_state_cd8_n_slide <- mpif_cell_state_n_slide %>%
filter(cell_phenotype_short_mpif == "CD8+") %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup()
```
### H&E
```{r chunk_210_060, fig.width=6, fig.height=3}
hne_cell_type <- read_tsv("/work/shah/vazquezi/data/transfers/spectrum/results/hne/v15/qupath/outputs/detection_summary/cohort/SPECTRUM.tsv")
hne_cell_type_expanded <- hne_cell_type %>%
filter(ROI != "Geometry") %>%
dplyr::select(image_hid, compartment=Parent, cell_type=Class, count, proportion) %>%
left_join(hne_meta_tbl, by = c("image_hid"="image_id")) %>%
filter(patient_id %in% scrna_patients) %>%
mutate(sort_short_x = compartment) %>%
mutate(sample_id = paste(tumor_subsite, patient_id, therapy, sort_short_x)) %>%
mutate(n = count, nrel = 100*proportion) %>%
mutate(cell_type_hne = cell_type) %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$tumor_supersite)))) %>%
# Keep included patients and pre-Rx samples
filter(patient_id %in% included_patients, therapy == "pre-Rx") %>%
# Keep adjacent H&E slides
filter(is_adjacent | is_site_matched) %>%
# Keep tumor and stroma compartments
filter(compartment %in% c("Tumor", "Stroma"))
## cell type composition slide lvl
hne_cell_type_n_slide <- hne_cell_type_expanded %>%
# mutate(n = count, nrel = 100*proportion) %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, cell_type_hne) %>%
mutate(n = sum(count)) %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup() %>%
add_helper_columns
## cell type composition slide lvl compartment
hne_cell_type_n_slide_compartment <- hne_cell_type_expanded %>%
# mutate(n = count, nrel = 100*proportion) %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x, cell_type_hne) %>%
mutate(n = sum(count)) %>%
group_by(patient_id_short, tumor_supersite, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x) %>%
mutate(nrel = n/sum(n)*100) %>%
ungroup() %>%
add_helper_columns
```
## Cancer cell figure
### Mutsig comp scRNA
```{r chunk_210_100, fig.width=3, fig.height=4.5}
plist_cancer.3 <- default_comp_grid_list(filter(comp_list$Ovarian.cancer.super,
sort_short_x == "CD45-"),
cluster_label, "Cancer.cell.3", cluster_label,
vec_plot = F, site_box = F, yaxis = T,
super_type = "Ovarian.cancer.super", nmax = 10000)
pgrid_cancer.3 <- plot_grid(plotlist = plist_cancer.3,
ncol = 1, align = "v",
rel_heights = c(0.3, 0.3, 0.4))
pgrid_cancer.3
ggsave("figures/215_composition_plots_subset/215_comp_plot_Ovarian.cancer.cell_cluster3_mutsig.pdf", pgrid_cancer.3, width = 3, height = 4.5)
ggsave("figures/215_composition_plots_subset/215_comp_plot_Ovarian.cancer.cell_cluster3_mutsig.png", pgrid_cancer.3, width = 3, height = 4.5)
```
## T cell figure
### Site comp scrna
```{r chunk_210_110, fig.width=6, fig.height=9}
plist1 <- default_comp_grid_list(
comp_list$T.super, cell_type_naive, "T naive/mem",
cluster_label_sub, super_type_sub = "T.super",
mutsig_box = T, site_box = T, vec_plot = T,
nmax = 5000, highlight = T)
plist2 <- default_comp_grid_list(
comp_list$T.super, cell_type_dysfunc, "T dysfunctional",
cluster_label_sub, super_type_sub = "T.super",
mutsig_box = T, site_box = T, vec_plot = T,
nmax = 5000, highlight = T)
comp_grid1 <- plot_grid(plotlist = plist1[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
comp_grid2 <- plot_grid(plotlist = plist2[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_naive_site.pdf", comp_grid1, width = 3, height = 9)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_naive_site.png", comp_grid1, width = 3, height = 9)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_site.pdf", comp_grid2, width = 3, height = 9)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_site.png", comp_grid2, width = 3, height = 9)
plot_grid(comp_grid1, comp_grid2, nrow = 1)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_site_scrna.pdf", width = 6, height = 9)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_site_scrna.png", width = 6, height = 9)
```
### Mutsig comp scrna
```{r chunk_210_120, fig.width=3, fig.height=4.5}
# source("src/comp_plot.R")
#
# rank_by(comp_list$T.super, cell_type_naive, "T naive/mem",
# cluster_label_sub, super_type_sub = "T.super") %>%
# plot_comp_vector(sample_id_rank, patient_id_short, tumor_megasite, tumor_megasite, "Adnexa", "Other", cell_type_naive, "T naive/mem")
```
```{r chunk_210_125, fig.width=3, fig.height=4.5}
comp_grid1 <- plot_grid(plotlist = plist1[-c(4,5)], ncol = 1, align = "v", rel_heights = c(0.3, 0.3, 0.4))
comp_grid2 <- plot_grid(plotlist = plist2[-c(4,5)], ncol = 1, align = "v", rel_heights = c(0.3, 0.3, 0.4))
comp_grid1
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_naive_mutsig.pdf", comp_grid1, width = 3, height = 4.5)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_naive_mutsig.png", comp_grid1, width = 3, height = 4.5)
comp_grid2
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_mutsig.pdf", comp_grid2, width = 3, height = 4.5)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_mutsig.png", comp_grid2, width = 3, height = 4.5)
```
### Site box+vector scrna
```{r chunk_210_130, fig.width=3, fig.height=6.3}
comp_grid1 <- plot_grid(plotlist = plist1[c(4,5)], ncol = 1, align = "v", rel_heights = c(0.24, 0.76))
comp_grid2 <- plot_grid(plotlist = plist2[c(4,5)], ncol = 1, align = "v", rel_heights = c(0.24, 0.76))
comp_grid1
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_naive_vectors.pdf", comp_grid1, width = 3, height = 6.3)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_naive_vectors.png", comp_grid1, width = 3, height = 6.3)
comp_grid2
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_vectors.pdf", comp_grid2, width = 3, height = 6.3)
ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_vectors.png", comp_grid2, width = 3, height = 6.3)
```
### Site comp mpif
```{r chunk_210_140, fig.width=3, fig.height=9}
# plist1 <- default_comp_grid_list(
# filter(mpif_cell_state_cd8_n_slide, sort_short_x == "mpIF"),
# cell_type_pd1n, "CD8+PD1-", cell_phenotype_mpif, nmax = 50000)
#
# comp_grid1 <- plot_grid(plotlist = plist1[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
#
# comp_grid1
#
# plist2 <- default_comp_grid_list(
# filter(mpif_cell_state_cd8_n_slide, sort_short_x == "mpIF"),
# cell_type_pd1p, "CD8+PD1+", cell_phenotype_mpif, nmax = 50000)
#
# comp_grid2 <- plot_grid(plotlist = plist2[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
#
# comp_grid2
#
# plist3 <- default_comp_grid_list(
# filter(mpif_cell_state_cd8_n_slide, sort_short_x == "mpIF"),
# cell_type_toxn, "CD8+TOX-", cell_phenotype_mpif, nmax = 50000)
#
# comp_grid3 <- plot_grid(plotlist = plist3[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
#
# comp_grid3
#
# plist4 <- default_comp_grid_list(
# filter(mpif_cell_state_cd8_n_slide, sort_short_x == "mpIF"),
# cell_type_toxp, "CD8+TOX+", cell_phenotype_mpif, nmax = 50000)
#
# comp_grid4 <- plot_grid(plotlist = plist4[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
#
# comp_grid4
#
# plist5 <- default_comp_grid_list(
# filter(mpif_cell_state_cd8_n_slide, sort_short_x == "mpIF"),
# cell_phenotype_mpif, "CD8+PD1+TOX+", cell_phenotype_mpif, nmax = 50000)
#
# comp_grid5 <- plot_grid(plotlist = plist5[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
#
# comp_grid5
#
# plist6 <- default_comp_grid_list(
# filter(mpif_cell_state_cd8_n_slide_compartment, sort_short_x == "Tumor"),
# cell_phenotype_mpif, "CD8+PD1+TOX+", cell_phenotype_mpif, nmax = 50000)
#
# comp_grid6 <- plot_grid(plotlist = plist6[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
#
# comp_grid6
#
# plist7 <- default_comp_grid_list(
# filter(mpif_cell_state_cd8_n_slide_compartment, sort_short_x == "Stroma"),
# cell_phenotype_mpif, "CD8+PD1+TOX+", cell_phenotype_mpif, nmax = 50000)
#
# comp_grid7 <- plot_grid(plotlist = plist7[-3], ncol = 1, align = "v", rel_heights = c(0.15, 0.15, 0.2, 0.5))
```
```{r chunk_210_150, fig.width=6, fig.height=9}
# plot_grid(comp_grid6, comp_grid7, nrow = 1)
#
# ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_site_mpif.pdf", width = 6, height = 9)
# ggsave("figures/215_composition_plots_subset/215_comp_plot_T.super_dysfunc_site_mpif.png", width = 6, height = 9)
```
## mpIF x scRNA correlation
```{r chunk_210_190, fig.width=6, fig.height=3}
# seu_cohort <- read_rds("/work/shah/isabl_data_lake/analyses/68/75/6875/RNASCP/outs/integrated_seurat_final.rds")
#
# scrna_markers <- as_tibble(cbind(cell_id = colnames(seu_cohort), FetchData(seu_cohort, c("cell_type", "sample", "CD68", "PDCD1", "CD274", "TOX", "CD8A", "CD8B", "KRT8", "KRT19")))) %>%
# mutate(CD68_state = ifelse(CD68 > 0, "CD68+", "CD68-"),
# CD8_state = ifelse(CD8A > 0 | CD8B > 0, "CD8+", "CD8-"),
# panCK_state = ifelse(KRT8 > 0 | KRT19 > 0, "panCK+", "panCK-"),
# TOX_state = ifelse(TOX > 0, "TOX+", "TOX-"),
# PD1_state = ifelse(PDCD1 > 0, "PD1+", "PD1-"),
# PDL1_state = ifelse(CD274 > 0, "PDL1+", "PDL1-"))
#
# write_tsv(scrna_markers, "/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v7/scrna_mpif_marker_expression.tsv")
# scrna_markers <- read_tsv("/work/shah/users/uhlitzf/data/SPECTRUM/freeze/v7/scrna_mpif_marker_expression.tsv") %>%
# ## remove double positives
# filter(!(CD68_state == "CD68+" & CD8_state == "CD8+"),
# !(CD68_state == "CD68+" & panCK_state == "panCK+"),
# !(CD8_state == "CD8+" & panCK_state == "panCK+")) %>%
# mutate(cell_type_sc = ifelse(CD68_state == "CD68+", "CD68+", ifelse(CD8_state == "CD8+", "CD8+", ifelse(panCK_state == "panCK+", "panCK+", "Other"))),
# cell_state = case_when(
# cell_type_sc == "CD8+" ~ paste0(CD8_state, PD1_state, TOX_state),
# cell_type_sc == "CD68+" ~ paste0(CD68_state, PDL1_state),
# cell_type_sc == "panCK+" ~ paste0(panCK_state, PDL1_state),
# cell_type_sc == "Unknown" ~ "Unknown",
# cell_type_sc == "Other" ~ "Other"
# ))
#
# markers_pos_frac_scrna_celltype <- seu_tbl_full %>%
# # select(cell_id, sample) %>%
# select(cell_id, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# left_join(select(scrna_markers, cell_id, cell_type_sc, cell_state), by = "cell_id") %>%
# na.omit() %>%
# group_by(sample, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# tally %>%
# group_by(sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# mutate(nrel = n/sum(n)*100) %>%
# select(sample, cell_type_sc, n, nrel, everything()) %>%
# ungroup
#
# markers_pos_frac_scrna_cellstate <- seu_tbl_full %>%
# # select(cell_id, sample) %>%
# select(cell_id, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# left_join(select(scrna_markers, cell_id, cell_type_sc, cell_state), by = "cell_id") %>%
# na.omit() %>%
# group_by(sample, cell_state, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# tally %>%
# group_by(sample, cell_type_sc, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# mutate(nrel = n/sum(n)*100) %>%
# select(sample, cell_type_sc, cell_state, n, nrel, everything()) %>%
# ungroup
#
# markers_pos_frac_scrna_gene <- seu_tbl_full %>%
# # select(cell_id, sample) %>%
# select(cell_id, sample, cell_type, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# left_join(select(scrna_markers, -sample, -cell_type), by = "cell_id") %>%
# na.omit() %>%
# select(-contains("state")) %>%
# mutate(cell_type = str_replace_all(cell_type, "\\.", " ")) %>%
# gather(gene, value, -cell_id, -sample, -cell_type, -tumor_supersite,
# -consensus_signature, -patient_id_short, -sort_short_x, -cell_type_sc) %>%
# mutate(gene_state = value > 0) %>%
# group_by(gene, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x) %>%
# mutate(n = sum(gene_state),
# nrel = n/length(n)*100) %>%
# ungroup %>%
# distinct(gene, sample, tumor_supersite, consensus_signature, patient_id_short, sort_short_x, n, nrel) %>%
# select(sample, gene, n, nrel, everything())
```
### CD45+ cell type
```{r chunk_210_200, fig.width=16, fig.height=8}
# markers_pos_scrna_mpif <- mpif_cell_state_n_slide_compartment %>%
# select(sample = sample_id, cell_type_sc = cell_phenotype_short_mpif,
# compartment = sort_short_x, n_mpif = n, nrel_mpif = nrel) %>%
# mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45P_")) %>%
# group_by(sample, cell_type_sc, compartment) %>%
# summarise(n_mpif = sum(n_mpif), nrel_mpif = sum(nrel_mpif)) %>%
# ungroup() %>%
# left_join(markers_pos_frac_scrna_celltype, by = c("sample", "cell_type_sc")) %>%
# filter(sort_short_x == "CD45+",
# cell_type_sc %in% c("CD8+", "CD68+"),
# compartment %in% c("Stroma", "Tumor"))
#
# common_layers <- list(
# facet_wrap(cell_type_sc~compartment, scales = "free"),
# stat_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "spearman", color = "black"),
# labs(x = "Fraction in scRNA (CD45+)",
# y = "Fraction in mpIF"),
# # coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
# theme(aspect.ratio = 1)
# )
#
# p1 <- ggplot(markers_pos_scrna_mpif) +
# geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
# scale_color_manual(values = clrs$tumor_supersite) +
# common_layers
#
# p2 <- ggplot(markers_pos_scrna_mpif) +
# geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
# scale_color_manual(values = clrs$consensus_signature) +
# common_layers
#
# plot_grid(p1, p2, ncol = 2)
```
### CD45+ cell state
```{r chunk_210_220, fig.width=12, fig.height=12}
# markers_pos_scrna_mpif_state <- mpif_cell_state_n_slide_compartment %>%
# group_by(sample_id, sort_short_x, cell_phenotype_short_mpif) %>%
# mutate(nrel_state = n/sum(n)*100) %>%
# select(sample = sample_id, cell_type_sc = cell_phenotype_short_mpif,
# compartment = sort_short_x,
# n_mpif = n, nrel_mpif = nrel_state, cell_state = cell_phenotype_mpif) %>%
# mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45P_")) %>%
# left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
# filter(sort_short_x == "CD45+",
# cell_state %in% c("CD8+TOX-PD1-", "CD8+TOX+PD1+", "CD68+PDL1-", "CD68+PDL1+"),
# compartment %in% c("Stroma", "Tumor"))
#
# common_layers <- list(
# facet_wrap(cell_state~compartment, scales = "free", ncol = 4),
# geom_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "spearman", color = "black"),
# labs(x = "Fraction in scRNA (CD45+)",
# y = "Fraction in mpIF"),
# # coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
# theme(aspect.ratio = 1)
# )
#
# p1 <- ggplot(markers_pos_scrna_mpif_state) +
# geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
# scale_color_manual(values = clrs$tumor_supersite) +
# common_layers
#
# p2 <- ggplot(markers_pos_scrna_mpif_state) +
# geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
# scale_color_manual(values = clrs$consensus_signature) +
# common_layers
#
# plot_grid(p1, p2, ncol = 1)
```
### CD45- cell type
```{r chunk_210_230, fig.width=16, fig.height=8}
# markers_pos_scrna_mpif_cd45n <- mpif_cell_state_n_slide_compartment %>%
# select(sample = sample_id, cell_type_sc = cell_phenotype_short_mpif,
# compartment = sort_short_x, n_mpif = n, nrel_mpif = nrel) %>%
# mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45N_")) %>%
# group_by(sample, cell_type_sc, compartment) %>%
# summarise(n_mpif = sum(n_mpif), nrel_mpif = sum(nrel_mpif)) %>%
# ungroup() %>%
# left_join(markers_pos_frac_scrna_celltype, by = c("sample", "cell_type_sc")) %>%
# filter(sort_short_x == "CD45-",
# cell_type_sc %in% c("panCK+"),
# compartment %in% c("Stroma", "Tumor"))
#
# common_layers <- list(
# facet_wrap(cell_type_sc~compartment, scales = "free"),
# stat_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "pearson", color = "black"),
# labs(x = "Fraction in scRNA (CD45-)",
# y = "Fraction in mpIF"),
# # coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
# theme(aspect.ratio = 1)
# )
#
# p1 <- ggplot(markers_pos_scrna_mpif_cd45n) +
# geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
# scale_color_manual(values = clrs$tumor_supersite) +
# common_layers
#
# p2 <- ggplot(markers_pos_scrna_mpif_cd45n) +
# geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
# scale_color_manual(values = clrs$consensus_signature) +
# common_layers
#
# plot_grid(p1, p2)
```
### CD45- cell state
```{r chunk_210_240, fig.width=12, fig.height=6}
# markers_pos_scrna_mpif_state_cd45n <- mpif_cell_state_n_slide_compartment %>%
# group_by(sample_id, sort_short_x, cell_phenotype_short_mpif) %>%
# mutate(nrel_state = n/sum(n)*100) %>%
# select(sample = sample_id, cell_type_sc = cell_phenotype_short_mpif,
# compartment = sort_short_x, n_mpif = n,
# nrel_mpif = nrel_state, cell_state = cell_phenotype_mpif) %>%
# mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45N_")) %>%
# left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
# filter(sort_short_x == "CD45-",
# cell_state %in% c("panCK+PDL1-", "panCK+PDL1+"),
# compartment %in% c("Stroma", "Tumor"))
#
# common_layers <- list(
# facet_wrap(cell_state~compartment, scales = "free", ncol = 4),
# geom_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "pearson", color = "black"),
# labs(x = "Fraction in scRNA (CD45-)",
# y = "Fraction in mpIF"),
# # coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
# theme(aspect.ratio = 1)
# )
#
# p1 <- ggplot(markers_pos_scrna_mpif_state_cd45n) +
# geom_point(aes(nrel, nrel_mpif, color = tumor_supersite)) +
# scale_color_manual(values = clrs$tumor_supersite) +
# common_layers
#
# p2 <- ggplot(markers_pos_scrna_mpif_state_cd45n) +
# geom_point(aes(nrel, nrel_mpif, color = consensus_signature)) +
# scale_color_manual(values = clrs$consensus_signature) +
# common_layers
#
# plot_grid(p1, p2, ncol = 1)
```
## Main text figure
```{r chunk_210_280, fig.width=12, fig.height=6}
# plist1 <- default_comp_grid_list(
# filter(comp_tbl_sample, sort_short_x == "CD45-"),
# cell_type, "Ov cancer cell", cell_type)
# plist1$empty <- ggdraw()
#
# plist2 <- default_comp_grid_list(
# filter(comp_tbl_sample, sort_short_x == "CD45+"),
# cell_type, "T cell", cell_type)
# plist2$empty <- ggdraw()
#
# plist3 <- default_comp_grid_list(
# filter(hne_cell_type_n_slide_compartment, sort_short_x == "Tumor"),
# cell_type_hne, "Lymphocyte", cell_type_hne, nmax = 2000000)
# plist3$empty <- ggdraw()
#
# plist4 <- default_comp_grid_list(
# filter(hne_cell_type_n_slide_compartment, sort_short_x == "Stroma"),
# cell_type_hne, "Lymphocyte", cell_type_hne, nmax = 2000000)
# plist4$empty <- ggdraw()
#
# plist5 <- default_comp_grid_list(
# filter(mpif_cell_type_n_slide_compartment, sort_short_x == "Tumor"),
# cell_phenotype_short_mpif, "CD8+", cell_phenotype_short_mpif, nmax = 250000)
# plist5$empty <- ggdraw()
#
# plist6 <- default_comp_grid_list(
# filter(mpif_cell_type_n_slide_compartment, sort_short_x == "Stroma"),
# cell_phenotype_short_mpif, "CD8+", cell_phenotype_short_mpif, nmax = 250000)
# plist6$empty <- ggdraw()
#
# ## cell type grid w/o mut sig
# pcomp1 <- plot_grid(plotlist = plist1[-3], ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.15, 0.59, 0))
#
# pcomp2 <- plot_grid(plotlist = plist2[-3], ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.15, 0.59, 0))
#
# pcomp3 <- plot_grid(plotlist = plist3[-3], ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.15, 0.59, 0))
#
# pcomp4 <- plot_grid(plotlist = plist4[-3], ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.15, 0.59, 0))
#
# pcomp5 <- plot_grid(plotlist = plist5[-3], ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.15, 0.59, 0))
#
# pcomp6 <- plot_grid(plotlist = plist6[-3], ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.15, 0.59, 0))
#
# gcomp1 <- plot_grid(pcomp1, pcomp2, ggdraw(), pcomp3, pcomp4, ggdraw(), pcomp5, pcomp6,
# ncol = 8, rel_widths = c(0.245, 0.245, 0.02, 0.245, 0.245, 0.02, 0.245, 0.245))
#
# ## cell type grid with mut sig
# pcomp1 <- plot_grid(plotlist = plist1, ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.1, 0.15, 0.59, 0))
#
# pcomp2 <- plot_grid(plotlist = plist2, ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.1, 0.15, 0.59, 0))
#
# pcomp3 <- plot_grid(plotlist = plist3, ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.1, 0.15, 0.59, 0))
#
# pcomp4 <- plot_grid(plotlist = plist4, ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.1, 0.15, 0.59, 0))
#
# pcomp5 <- plot_grid(plotlist = plist5, ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.1, 0.15, 0.59, 0))
#
# pcomp6 <- plot_grid(plotlist = plist6, ncol = 1, align = "v",
# rel_heights = c(0.13, 0.13, 0.1, 0.15, 0.59, 0))
#
# gcomp2 <- plot_grid(pcomp1, pcomp2, ggdraw(), pcomp3, pcomp4, ggdraw(), pcomp5, pcomp6,
# ncol = 8, rel_widths = c(0.245, 0.245, 0.02, 0.245, 0.245, 0.02, 0.245, 0.245))
#
#
# # mpif_state_legend <- get_legend(plot_comp_bar(
# # rank_by(filter(mpif_cell_state_n_slide_compartment, sort_short_x == "Tumor"),
# # cell_type_mpif, "CD8.T.cell", cell_phenotype_mpif),
# # sample_id_lvl, nrel, cell_phenotype_mpif, facet = "sort_short_x") +
# # scale_color_manual(values = c(Adnexa = "#ff0000", Other = "#56B4E9"), labels = c("Enriched", "Depleted", "")) +
# # labs(fill = "mpIF cell state") +
# # guides(fill = guide_legend(ncol = 2)))
#
# mpif_cell_type_legend <- get_legend(plot_comp_bar(
# rank_by(filter(mpif_cell_type_n_slide_compartment, sort_short_x == "Tumor"),
# cell_phenotype_short_mpif, "CD8+", cell_phenotype_short_mpif),
# sample_id, nrel, cell_phenotype_short_mpif, facet = "sort_short_x") +
# scale_color_manual(values = c(Adnexa = "#ff0000", Other = "#56B4E9"), labels = c("Enriched", "Depleted", "")) +
# labs(fill = "mpIF cell type") +
# guides(fill = guide_legend(ncol = 2)))
#
# hne_cell_type_legend <- get_legend(plot_comp_bar(
# rank_by(filter(hne_cell_type_n_slide_compartment, sort_short_x == "Tumor"),
# cell_type_hne, "Lymphocyte", cell_type_hne),
# sample_id_lvl, nrel, cell_type_hne, facet = "sort_short_x") +
# labs(fill = "H&E cell type") +
# guides(fill = guide_legend(ncol = 1)))
#
# vec_legend_helper <- plot_comp_vector(
# rank_by(filter(comp_tbl_sample, sort_short_x == "CD45+"),
# cell_type, "T cell", cell_type),
# sample_id_rank, patient_id_short,
# tumor_megasite, tumor_megasite, "Adnexa",
# cell_type, "T cell") +
# scale_color_manual(values = c(Adnexa = "#ff0000", Other = "#56B4E9"), labels = c("Enriched", "Depleted", "")) +
# labs(color = "Non-adnexal\ninfiltration")
#
# vec_legend_shape <- get_legend(vec_legend_helper + guides(color = F))
# vec_legend_color <- get_legend(vec_legend_helper + guides(shape = F))
```
```{r chunk_210_290, fig.width=14, fig.height=8}
# markers_pos_scrna_mpif_state_cd45p <- mpif_cell_state_n_slide_compartment %>%
# group_by(sample_id, sort_short_x, cell_phenotype_short_mpif, cell_phenotype_mpif) %>%
# mutate(nrel_state = n/sum(n)*100) %>%
# dplyr::select(sample = sample_id, cell_type_sc = cell_phenotype_short_mpif,
# compartment = sort_short_x,
# n_mpif = n, nrel_mpif = nrel_state, cell_state = cell_phenotype_mpif) %>%
# mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45P_")) %>%
# left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
# filter(sort_short_x == "CD45+",
# cell_state %in% c("CD8+PD1+TOX+", "CD68+PDL1+"),
# compartment %in% c("Stroma", "Tumor"))
#
# markers_pos_scrna_mpif_state_cd45n <- mpif_cell_state_n_slide_compartment %>%
# group_by(sample_id, sort_short_x, cell_phenotype_short_mpif, cell_phenotype_mpif) %>%
# mutate(nrel_state = n/sum(n)*100) %>%
# dplyr::select(sample = sample_id, cell_type_sc = cell_phenotype_short_mpif,
# compartment = sort_short_x, n_mpif = n,
# nrel_mpif = nrel_state, cell_state = cell_phenotype_short_mpif) %>%
# mutate(sample = str_replace_all(sample, "_S1", "_S1_CD45N_")) %>%
# left_join(markers_pos_frac_scrna_cellstate, by = c("sample", "cell_type_sc", "cell_state")) %>%
# filter(sort_short_x == "CD45-",
# cell_state %in% c("panCK+PDL1+"),
# compartment %in% c("Stroma", "Tumor"))
#
# markers_pos_scrna_mpif_state <- bind_rows(markers_pos_scrna_mpif_state_cd45p,
# markers_pos_scrna_mpif_state_cd45n) %>%
# mutate(compartment = ordered(compartment, levels = c("Tumor", "Stroma"))) %>%
# mutate(cell_state = case_when(
# cell_state == "panCK+PDL1+" ~ "panCK+PDL1+\npanCK+",
# cell_state == "CD8+PD1+TOX+" ~ "CD8+PD1+TOX+\nCD8+",
# cell_state == "CD68+PDL1+" ~ "CD68+PDL1+\nCD68+"
# ))
#
# common_layers <- list(
# facet_grid(cell_state~compartment, scales = "free"),
# geom_smooth(aes(nrel, nrel_mpif), method = "lm", color = "black"),
# stat_cor(aes(nrel, nrel_mpif), method = "spearman", color = "black"),
# labs(x = "Fraction in scRNA [%]",
# y = "Fraction in mpIF [%]"),
# # coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
# theme(aspect.ratio = 1, plot.margin = margin(0, 0, 0, 0)),
# guides(color = F)
# )
#
# cor_plot_list <- list()
#
# cor_plot_list$p1 <- ggplot(filter(markers_pos_scrna_mpif_state,
# cell_state %in% c("panCK+PDL1+\npanCK+"))) +
# geom_point(aes(nrel, nrel_mpif, color = cell_type_sc)) +
# scale_color_manual(values = clrs$cell_type) +
# common_layers +
# theme(axis.title.x = element_blank(),
# axis.title.y = element_blank(),
# strip.text.x = element_blank())
#
# cor_plot_list$p2 <- ggplot(filter(markers_pos_scrna_mpif_state,
# cell_state %in% c("CD68+PDL1+\nCD68+"))) +
# geom_point(aes(nrel, nrel_mpif, color = cell_type_sc)) +
# scale_color_manual(values = clrs$cell_type) +
# common_layers +
# theme(axis.title.x = element_blank(),
# strip.text.x = element_blank())
#
# cor_plot_list$p3 <- ggplot(filter(markers_pos_scrna_mpif_state,
# cell_state %in% c("CD8+PD1+TOX+\nCD8+"))) +
# geom_point(aes(nrel, nrel_mpif, color = cell_type_sc)) +
# scale_color_manual(values = clrs$cell_type) +
# common_layers +
# theme(axis.title.y = element_blank(),
# strip.text.x = element_blank())
#
# cor_plot_grid <- plot_grid(plotlist = cor_plot_list, ncol = 1, align = "hv")
```
```{r chunk_210_300, fig.width=17, fig.height=10}
# # comp_grid_full <- ggdraw() +
# # draw_plot(gcomp1, x = 0, y = 0, width = 1, height = 1) +
# # draw_grob(vec_legend_shape, x = 0.2, y = -0.065, vjust = 0.5, hjust = 0) +
# # draw_grob(vec_legend_color, x = 0.4, y = -0.055, vjust = 0.5, hjust = 0) +
# # draw_grob(mpif_cell_type_legend, x = 0.6, y = -0.065, vjust = 0.5, hjust = 0) +
# # draw_grob(hne_cell_type_legend, x = 0.8, y = -0.065, vjust = 0.5, hjust = 0)
# # # draw_plot(cor_plot_grid, x = 0.68, y = 0.21, width = 0.35, height = 0.75) +
# # # draw_label("Tumor", x = 0.815, y = 0.98) +
# # # draw_label("Stroma", x = 0.91, y = 0.98)
#
# comp_grid_full <- plot_grid(gcomp1,
# plot_grid(ggdraw(), vec_legend_shape, vec_legend_color, hne_cell_type_legend, mpif_cell_type_legend, ggdraw(),
# ncol = 6, rel_widths = c(0.15, 0.15, 0.15, 0.15, 0.15, 0.15)),
# ncol = 1, rel_heights = c(1, 0.15))
# # draw_plot(cor_plot_grid, x = 0.68, y = 0.21, width = 0.35, height = 0.75) +
# # draw_label("Tumor", x = 0.815, y = 0.98) +
# # draw_label("Stroma", x = 0.91, y = 0.98)
#
# comp_grid_full
#
# ggsave(filename = "figures/210_cohort_cell_type_composition/210_comp_grid_site.pdf",
# comp_grid_full, width = 17, height = 10)
#
# ggsave(filename = "figures/210_cohort_cell_type_composition/210_comp_grid_site.png",
# comp_grid_full, width = 17, height = 10)
```
[figures/210_cohort_cell_type_composition/210_comp_grid_site.pdf](figures/210_cohort_cell_type_composition/210_comp_grid_site.pdf")
[figures/210_cohort_cell_type_composition/210_comp_grid_site.png](figures/210_cohort_cell_type_composition/210_comp_grid_site.png)
```{r chunk_210_310, fig.width=17, fig.height=11}
# comp_grid_full2 <- plot_grid(gcomp2,
# plot_grid(ggdraw(), vec_legend_shape, vec_legend_color, hne_cell_type_legend, mpif_cell_type_legend, ggdraw(),