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210_cohort_plotting_composition.Rmd
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210_cohort_plotting_composition.Rmd
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---
<<<<<<< HEAD
title: "Cell type compositions"
subtitle: "MSK SPECTRUM"
author:
- "Florian Uhlitz"
- "Ignacio Vazquez-Garcia"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
bookdown::html_document2:
includes:
in_header: header.html
bookdown::gitbook:
includes:
in_header: header.html
=======
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
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
<<<<<<< HEAD
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
=======
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = F)
>>>>>>> 3b877a7c59812e1ad259a7239a3b4b8e355e7ff4
```
```{r chunk_210_010}
library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(openxlsx)
library(cowplot)
library(viridis)
# library(magick, lib.loc = "/home/uhlitzf/miniconda3/lib/R/library")
library(ggpubr)
```
# Figure 2 lower
```{r chunk_210_030}
## load global vars:
source("src/global_vars.R")
# scrna_meta_tbl
# clrs
# markers_v7
# markers_v7_super
# cell_type_super_lookup
names(clrs$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$cell_type))) %>%
mutate(sort_short_x = ifelse(sort_short == "U" & cell_type_super == "Immune",
"CD45+", ifelse(sort_short == "U" & cell_type_super == "Stromal",
"CD45-", sort_short))) %>%
left_join(consOV_tbl, by = "cell_id")
# seu_tbl_full <- seu_tbl_full %>%
# sample_n(10000)
```
## scRNA compositions {.tabset}
```{r chunk_210_040, fig.width=8, fig.height=4}
source("src/comp_plot.R")
# rank_by()
add_helper_columns <- . %>%
mutate(label_supersite = "Site",
label_therapy = "Rx",
label_mutsig = "Signature")
comp_tbl_sample <- seu_tbl_full %>%
filter(therapy == "pre-Rx", cell_type != "Other") %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature, cell_type) %>%
tally() %>%
ungroup() %>%
# Pad cell type combinations with zero counts in the data
complete(cell_type,
nesting(tumor_subsite, tumor_supersite, tumor_megasite,
patient_id_short, therapy, sort_short_x, consensus_signature),
fill = list(n = 0)) %>%
group_by(tumor_subsite, tumor_supersite, tumor_megasite, patient_id_short,
therapy, sort_short_x, consensus_signature) %>%
mutate(nrel = n/sum(n)*100) %>%
add_helper_columns %>%
mutate(tumor_supersite = ordered(tumor_supersite, levels = rev(names(clrs$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() %>%
ungroup() %>%
# Pad consensus type combinations with zero counts in the data
complete(consensusOV,
nesting(tumor_subsite, tumor_supersite, tumor_megasite,
patient_id_short, therapy, sort_short_x, consensus_signature),
fill = list(n = 0)) %>%
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
#
# g9 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Immunoreactive")
# g10 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Mesenchymal")
# g11 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Differentiated")
# g12 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
# consensusOV, "Proliferative")
#
# g13 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Immunoreactive")
# g14 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Mesenchymal")
# g15 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Differentiated")
# g16 <- default_comp_grid(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
# consensusOV, "Proliferative")
# pdf("_fig/002_cohort/002_comp_full.pdf", width = 3.5, height = 12)
# g1;g2;g3;g4;g5;g6;g7;g8;g9;g10;g11;g12;g13;g14;g15;g16
# dev.off()
```
### Cell type {.tabset}
#### Site {.tabset}
```{r chunk_210_050, fig.width=3.5, fig.height=12, results="asis"}
cell_types_immune <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Immune"]), "\\.", " ")
cell_types_stromal <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Stromal"]), "\\.", " ") %>%
str_replace_all("Ovarian", "Ov")
for(i in 1:length(cell_types_immune)){
cat('##### ', cell_types_immune[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, cell_types_immune[i], cell_type, mutsig_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, cell_types_immune, tumor_supersite)))
cat(' \n \n')
}
for(i in 1:length(cell_types_stromal)){
cat('##### ', cell_types_stromal[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45-"),
cell_type, cell_types_stromal[i], cell_type, mutsig_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_tbl_sample, sort_short_x == "CD45-"),
cell_type, cell_types_stromal, tumor_supersite)))
cat(' \n \n')
}
```
#### Signature {.tabset}
```{r chunk_210_060, fig.width=3.5, fig.height=12, results="asis"}
cell_types_immune <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Immune"]), "\\.", " ")
cell_types_stromal <- str_replace_all(names(cell_type_super_lookup[cell_type_super_lookup=="Stromal"]), "\\.", " ") %>%
str_replace_all("Ovarian", "Ov")
sois <- c("HRD-Dup", "HRD-Del", "TD", "FBI")
for(i in 1:length(cell_types_immune)){
cat('##### ', cell_types_immune[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45+"),
cell_type, cell_types_immune[i], cell_type, site_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_tbl_sample, sort_short_x == "CD45+",
consensus_signature %in% sois),
cell_type, cell_types_immune, consensus_signature)))
cat(' \n \n')
}
for(i in 1:length(cell_types_stromal)){
cat('##### ', cell_types_stromal[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_sample, sort_short_x == "CD45-"),
cell_type, cell_types_stromal[i], cell_type, site_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_tbl_sample, sort_short_x == "CD45-",
consensus_signature %in% sois),
cell_type, cell_types_stromal, consensus_signature)))
cat(' \n \n')
}
```
### TCGA {.tabset}
#### Site {.tabset}
```{r chunk_210_070, fig.width=3.5, fig.height=12, results="asis"}
tcga_subtypes <- as.character(unique(consOV_tbl$consensusOV))
for(i in 1:length(tcga_subtypes)){
cat('##### ', tcga_subtypes[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_consOV, sort_short_x == "CD45+"),
consensusOV, tcga_subtypes[i], consensusOV, mutsig_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_tbl_consOV, sort_short_x == "CD45+"),
consensusOV, tcga_subtypes, tumor_supersite)))
cat(' \n \n')
}
for(i in 1:length(tcga_subtypes)){
cat('##### ', tcga_subtypes[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_consOV, sort_short_x == "CD45-"),
consensusOV, tcga_subtypes[i], consensusOV, mutsig_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_tbl_consOV, sort_short_x == "CD45-"),
consensusOV, tcga_subtypes, tumor_supersite)))
cat(' \n \n')
}
```
#### Signature {.tabset}
```{r chunk_210_080, fig.width=3.5, fig.height=12, results="asis"}
tcga_subtypes <- as.character(unique(consOV_tbl$consensusOV))
for(i in 1:length(tcga_subtypes)){
cat('##### ', tcga_subtypes[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_consOV, sort_short_x == "CD45+"),
consensusOV, tcga_subtypes[i], consensusOV, site_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_tbl_consOV, sort_short_x == "CD45+",
consensus_signature %in% sois),
consensusOV, tcga_subtypes, consensus_signature)))
cat(' \n \n')
}
for(i in 1:length(tcga_subtypes)){
cat('##### ', tcga_subtypes[i],' \n')
plist <- default_comp_grid_list(
filter(comp_tbl_consOV, sort_short_x == "CD45-"),
consensusOV, tcga_subtypes[i], consensusOV, site_box = F)
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_tbl_consOV, sort_short_x == "CD45-",
consensus_signature %in% sois),
consensusOV, tcga_subtypes, consensus_signature)))
cat(' \n \n')
}
```
### Cell state {.tabset}
```{r chunk_210_090, fig.width=3, fig.height=12}
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)
# foo <- comp_list$T.super %>%
# mutate(sample_id = paste(tumor_subsite, patient_id_short, therapy, sort_short_x)) %>%
# mutate(Treg = str_detect(cluster_label_sub, "CD4.T.reg")) %>%
# select(sample, sample_id, cluster_label_sub, n, nrel, nsum, Treg) %>%
# group_by(Treg, sample, sample_id) %>%
# mutate(n_Treg = ifelse(Treg, sum(n), NA), nrel_Treg_T = ifelse(Treg, sum(nrel), NA)) %>%
# na.omit %>%
# arrange(desc(nrel_Treg_T)) %>%
# mutate(nrel = signif(nrel, 2), nrel_Treg_T = signif(nrel_Treg_T, 2)) %>%
# ungroup %>%
# distinct(sample, sample_id, n_Treg, nrel_Treg_T) %>%
# left_join(comp_tbl_sample %>% select(n_cd45p = n, sample_id), by = "sample_id") %>%
# group_by(sample, sample_id, n_Treg, nrel_Treg_T) %>%
# summarise(n_cd45p = sum(n_cd45p)) %>%
# mutate(nrel_Treg_cd45p = signif(n_Treg/n_cd45p*100, 2)) %>%
# ungroup %>%
# arrange(-nrel_Treg_cd45p) %>%
# select(-sample_id) %>%
# mutate(sample_mpif = str_remove_all(sample, "_CD45P_")) %>%
# left_join(select(mpif_meta_tbl, sample_mpif = sample_id, has_mpif = batch), by = "sample_mpif") %>%
# mutate(has_mpif = ifelse(is.na(has_mpif), F, T)) %>%
# select(-sample_mpif)
#
# ggplot(foo, aes(nrel_Treg_T, nrel_Treg_cd45p, color = has_mpif)) +
# geom_point() +
# theme(aspect.ratio = 1)
#
# write_tsv(foo, "treg_fractions.tsv")
#
# mpif_meta_tbl$sample_id
#
# foo %>%
# filter(has_mpif == T) %>%
# filter(str_detect(sample, "ADNEXA|OVARY|OMENTUM")) %>%
# write_tsv("treg_fractions_sub.tsv")
```
#### T/NK cells {.tabset}
```{r chunk_210_100, fig.width=3.5, fig.height=10, results="asis"}
T.super.clusters <- sort(unique(comp_list$T.super$cluster_label_sub))
for(i in 1:length(T.super.clusters)){
cat('##### ', T.super.clusters[i],' \n')
plist <- default_comp_grid_list(
filter(comp_list$T.super, sort_short_x == "CD45+"),
cluster_label_sub, T.super.clusters[i], cluster_label_sub,
super_type_sub = "T.super")
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.14, 0.14, 0.14, 0.14, 0.44))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$T.super, sort_short_x == "CD45+"),
cluster_label_sub, T.super.clusters, tumor_supersite,
super_type_sub = "T.super")))
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$T.super, sort_short_x == "CD45+"),
cluster_label_sub, T.super.clusters, consensus_signature,
super_type_sub = "T.super")))
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$T.super, sort_short_x == "CD45+",
consensus_signature %in% sois),
cluster_label_sub, T.super.clusters, consensus_signature,
super_type_sub = "T.super")))
cat(' \n \n')
}
```
#### Myeloid cells {.tabset}
```{r chunk_210_110, fig.width=3.5, fig.height=10, results="asis"}
Myeloid.super.clusters <- sort(unique(comp_list$Myeloid.super$cluster_label))
for(i in 1:length(Myeloid.super.clusters)){
cat('##### ', Myeloid.super.clusters[i],' \n')
plist <- default_comp_grid_list(
filter(comp_list$Myeloid.super, sort_short_x == "CD45+"),
cluster_label, Myeloid.super.clusters[i], cluster_label,
super_type = "Myeloid.super")
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.14, 0.14, 0.14, 0.14, 0.44))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Myeloid.super, sort_short_x == "CD45+"),
cluster_label, Myeloid.super.clusters, tumor_supersite,
super_type = "Myeloid.super")))
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Myeloid.super, sort_short_x == "CD45+"),
cluster_label, Myeloid.super.clusters, consensus_signature,
super_type = "Myeloid.super")))
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Myeloid.super, sort_short_x == "CD45+",
consensus_signature %in% sois),
cluster_label, Myeloid.super.clusters, consensus_signature,
super_type = "Myeloid.super")))
cat(' \n \n')
}
```
#### Fibroblasts {.tabset}
```{r chunk_210_120, fig.width=3.5, fig.height=10, results="asis"}
Fibroblast.super.clusters <- sort(unique(comp_list$Fibroblast.super$cluster_label))
for(i in 1:length(Fibroblast.super.clusters)){
cat('#### ', Fibroblast.super.clusters[i],' \n')
plist <- default_comp_grid_list(
filter(comp_list$Fibroblast.super, sort_short_x == "CD45-"),
cluster_label, Fibroblast.super.clusters[i], cluster_label,
super_type = "Fibroblast.super")
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.14, 0.14, 0.14, 0.14, 0.44))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Fibroblast.super, sort_short_x == "CD45-"),
cluster_label, Fibroblast.super.clusters, tumor_supersite,
super_type = "Fibroblast.super")))
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Fibroblast.super, sort_short_x == "CD45-"),
cluster_label, Fibroblast.super.clusters, consensus_signature,
super_type = "Fibroblast.super")))
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Fibroblast.super, sort_short_x == "CD45-",
consensus_signature %in% sois),
cluster_label, Fibroblast.super.clusters, consensus_signature,
super_type = "Fibroblast.super")))
cat(' \n \n')
}
```
#### Cancer cells {.tabset}
```{r chunk_210_130, fig.width=3.5, fig.height=10, results="asis"}
Cancer.super.clusters <- sort(unique(comp_list$Ovarian.cancer.super$cluster_label))
for(i in 1:length(Cancer.super.clusters)){
cat('##### ', Cancer.super.clusters[i],' \n')
plist <- default_comp_grid_list(
filter(comp_list$Ovarian.cancer.super, sort_short_x == "CD45-"),
cluster_label, Cancer.super.clusters[i], cluster_label,
super_type = "Ovarian.cancer.super")
p <- plot_grid(plotlist = plist, ncol = 1, align = "v",
rel_heights = c(0.14, 0.14, 0.14, 0.14, 0.44))
print(p)
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Ovarian.cancer.super, sort_short_x == "CD45-"),
cluster_label, Cancer.super.clusters, tumor_supersite,
super_type = "Ovarian.cancer.super")))
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Ovarian.cancer.super, sort_short_x == "CD45-"),
cluster_label, Cancer.super.clusters, consensus_signature,
super_type = "Ovarian.cancer.super")))
cat(' \n \n')
print(knitr::kable(wilcoxon_tests(filter(comp_list$Ovarian.cancer.super, sort_short_x == "CD45-",
consensus_signature %in% sois),
cluster_label, Cancer.super.clusters, consensus_signature,
super_type = "Ovarian.cancer.super")))
cat(' \n \n')
}
```
## H&E composition {.tabset}
```{r chunk_210_250, fig.width=6, fig.height=3}
## meta data for H&E
source("src/global_vars.R")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "\\.", " ")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "Ovarian", "Ov")
hne_cell_type <- read_tsv("/work/shah/vazquezi/projects/spectrum/results/hne/v17/qupath/outputs/detection_summary/cohort/SPECTRUM.tsv")
hne_cell_type_expanded <- hne_cell_type %>%
dplyr::select(image_hid, compartment=Parent, cell_type=Class, count, proportion) %>%
# Pad cell type-compartment combinations with zero counts in the data
complete(image_hid, crossing(cell_type, compartment), fill = list(count = 0, proportion = 0)) %>%
# group_by(image_hid, compartment) %>%
# mutate(
# proportion = case_when(
# sum(count)==0 & cell_type == "Lymphocyte" ~ 0,
# sum(count)==0 & cell_type == "Other" ~ 1,
# TRUE ~ proportion
# ),
# count = case_when(
# sum(count)==0 & cell_type == "Other" ~ 1,
# TRUE ~ count
# )
# ) %>%
# ungroup %>%
left_join(hne_meta_tbl, by = c("image_hid"="image_id")) %>%
# filter(patient_id %in% hne_patients) %>%
mutate(sort_short_x = compartment) %>%
mutate(sample_id = paste(patient_id, tumor_site, therapy)) %>%
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",
# tumor_supersite != "Unknown") %>%
# # Remove low-quality slides
# filter(qc_status == "Pass") %>%
# # Keep adjacent H&E slides
# filter(is_adjacent) %>%
# Keep included files
filter(tme_inclusion_status == "Yes") %>%
# Keep tumor and stroma compartments
filter(compartment %in% c("Tumor", "Stroma")) %>%
# Remove cell type-compartment combinations if no tumor or stroma detected
group_by(image_hid) %>%
mutate(tumor_stroma_match = ifelse(n_distinct(compartment) > 1, TRUE, FALSE)) %>%
# filter(n_distinct(compartment) > 1) %>%
ungroup()
# Remove cell type-compartment combinations if no tumor or stroma detected
# group_by(image_hid, compartment) %>%
# filter(if(!all(n == 0)) TRUE else FALSE) %>%
# ungroup()
# # Remove cell type-compartment combinations if no tumor or stroma detected
# group_by(image_hid, compartment) %>%
# mutate(n = ifelse(all(n == 0) & cell_type_hne == "Lymphocyte", 0, n),
# n = ifelse(all(n == 0) & cell_type_hne == "Other", 1, n)) %>%
# ungroup()
## 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_site, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, cell_type_hne) %>%
mutate(n = sum(count)) %>%
group_by(patient_id_short, tumor_supersite, tumor_site, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature) %>%
# mutate(nrel = n/sum(n)*100) %>%
# mutate(nrel = ifelse(n > 0, n/sum(n)*100, 0)) %>%
mutate(
nrel = case_when(
sum(n)==0 & cell_type == "Lymphocyte" ~ 0,
sum(n)==0 & cell_type == "Other" ~ 100,
TRUE ~ nrel
),
) %>%
# mutate(nrel = ifelse(n == 0 & sort_short_x == "Tumor", 0, nrel),
# nrel = ifelse(n == 0 & sort_short_x == "Stroma", 100, nrel)) %>%
# mutate(nrel = ifelse(n > 0, n/sum(n)*100, ifelse(cell_type_hne == "Lymphocyte", 0, 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_site, 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_site, tumor_subsite, tumor_megasite, therapy, sample_id, consensus_signature, sort_short_x) %>%
# mutate(nrel = n/sum(n)*100) %>%
# mutate(nrel = ifelse(n > 0, n/sum(n)*100, 0)) %>%
mutate(
nrel = case_when(
sum(n)==0 & cell_type == "Lymphocyte" ~ 0,
sum(n)==0 & cell_type == "Other" ~ 100,
TRUE ~ nrel
),
) %>%
# mutate(nrel = ifelse(n == 0 & sort_short_x == "Tumor", 0, nrel),
# nrel = ifelse(n == 0 & sort_short_x == "Stroma", 100, nrel)) %>%
# mutate(nrel = ifelse(n > 0, n/sum(n)*100, ifelse(sort_short_x == "Lymphocyte", 0, 100))) %>%
ungroup() %>%
add_helper_columns
```
### Cell type {.tabset}
#### Site {.tabset}
```{r chunk_210_260, fig.width=7, fig.height=12, results="asis"}
cell_types_hne <- c("Lymphocyte", "Other")[1]
for(i in 1:length(cell_types_hne)){
cat('#### ', cell_types_hne[i],' \n')
plist_t <- default_comp_grid_list(
filter(hne_cell_type_n_slide_compartment, sort_short_x == "Tumor"),
cell_type_hne, cell_types_hne[i], cell_type_hne, mutsig_box = F,
nmax = 250000)
plist_s <- default_comp_grid_list(
filter(hne_cell_type_n_slide_compartment, sort_short_x == "Stroma"),
cell_type_hne, cell_types_hne[i], cell_type_hne, mutsig_box = F,
nmax = 250000)
pt <- plot_grid(plotlist = plist_t, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
ps <- plot_grid(plotlist = plist_s, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
p <- plot_grid(pt, ps, ncol = 2)
print(p)
cat(' \n \n')
}
```
#### Signature {.tabset}
```{r chunk_210_270, fig.width=7, fig.height=12, results="asis"}
cell_types_hne <- c("Lymphocyte", "Other")[1]
for(i in 1:length(cell_types_hne)){
cat('#### ', cell_types_hne[i],' \n')
plist_t <- default_comp_grid_list(
filter(hne_cell_type_n_slide_compartment, sort_short_x == "Tumor"),
cell_type_hne, cell_types_hne[i], cell_type_hne, site_box = F,
nmax = 250000)
plist_s <- default_comp_grid_list(
filter(hne_cell_type_n_slide_compartment, sort_short_x == "Stroma"),
cell_type_hne, cell_types_hne[i], cell_type_hne, site_box = F,
nmax = 250000)
pt <- plot_grid(plotlist = plist_t, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
ps <- plot_grid(plotlist = plist_s, ncol = 1, align = "v",
rel_heights = c(0.13, 0.13, 0.13, 0.61))
p <- plot_grid(pt, ps, ncol = 2)
print(p)
cat(' \n \n')
}
```
#### Tumor vs stroma
```{r, fig.width=10, fig.height=3.5}
hne_cell_type_n_slide_tumor_stroma <- hne_cell_type_n_slide_compartment %>%
filter(cell_type == "Lymphocyte") %>%
pivot_wider(id_cols = c("sample_id", "patient_id_short", "tumor_supersite", "consensus_signature", "cell_type"),
names_from = "compartment",
values_from = "nrel")
common_layers <- list(
# facet_wrap(cell_type~., scales = "free", ncol = 2),
geom_smooth(aes(Tumor, Stroma), method = "lm", color = "black"),
stat_cor(aes(Tumor, Stroma), method = "spearman", color = "black", label.y = 90),
labs(x = "% lymphocytes (tumor)",
y = "% lymphocytes (stroma)"),
coord_cartesian(ylim = c(0, 100), xlim = c(0, 100)),
theme(aspect.ratio = 1)
)
p1 <- ggplot(hne_cell_type_n_slide_tumor_stroma) +
geom_point(aes(Tumor, Stroma, color = patient_id_short)) +
scale_color_manual(values = clrs$patient_id_short) +
common_layers
p2 <- ggplot(hne_cell_type_n_slide_tumor_stroma) +
geom_point(aes(Tumor, Stroma, color = tumor_supersite)) +
scale_color_manual(values = clrs$tumor_supersite, drop = TRUE) +
labs(color = "Site") +
common_layers
p3 <- ggplot(hne_cell_type_n_slide_tumor_stroma) +
geom_point(aes(Tumor, Stroma, color = consensus_signature)) +
scale_color_manual(values = clrs$consensus_signature, drop = TRUE) +
labs(color = "Mutational\nsignature") +
common_layers
plot_grid(p2, p3, nrow = 1, align = "h")
```
## mpIF composition {.tabset}
```{r chunk_210_140, fig.width=6, fig.height=3}
## meta data for mpIF
source("src/global_vars.R")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "\\.", " ")
names(clrs$cell_type) <- str_replace_all(names(clrs$cell_type), "Ovarian", "Ov")
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/uhlitzf/data/SPECTRUM/mpIF/cell_type_manual_expanded_v10.tsv")
mpif_cell_state_expanded <- read_tsv("/work/shah/vazquezi/projects/spectrum/results/mpif/v13/cell-type/outputs/phenotype_summary_merged/SPECTRUM/phenotypes_expanded.tsv") %>%
# dplyr::mutate(Image = str_replace_all(Image, "SPECTRUM-OV-???-", "SPECTRUM-OV-???-")) %>%
# gsub('([0-9]+) .*', '\\1', c('c123123123 0320.1'))
# dplyr::mutate(Image = gsub("(\\d1{3}-){1}", "_", Image))
# dplyr::mutate(image_id = str_replace_all(Image, "-BO", "_BO"),
# image_id = str_replace_all(image_id, "-IO", "_IO"),
# image_id = str_replace_all(image_id, "-LA", "_LA"),
# image_id = str_replace_all(image_id, "-RA", "_RA"),
# image_id = str_replace_all(image_id, "-PP", "_PP"),
# image_id = str_replace_all(image_id, "-LUQ", "_LUQ"),
# image_id = str_replace_all(image_id, "-RUQ", "_RUQ")) %>%
# Image = str_replace_all(Image, "SPECTRUM_OV_", "SPECTRUM-OV-")) %>%
# dplyr::mutate(Image = gsub("(\\d1{3}){0,3}-", "_", Image))
# mutate(Image = str_extract(Image, '(0-9)[-]]+'))
# dplyr::mutate(Image = gsub("SPECTRUM-OV-(\\d1{3}){1}", "_", Image))
# dplyr::mutate(Image = gsub("(\\d1{3}){1}", "_", Image))
# gsub('(^.*\\(sec\\)).*', '\\1', '(sec): 0.xxx')
# tidyr::separate(Image, c("project","study","patient","site","markers_1","markers_2","markers_3","fov"), sep = "_", remove = FALSE) %>%
# tidyr::separate(image_id, c("patient_id","site","markers",NA,"fov"), sep = "_", remove = FALSE) %>%
# dplyr::mutate(pici_id = paste(patient_id, site)) %>%
# dplyr::mutate(patient_id = paste(project, study, patient, sep = "-"),
# pici_id = paste(patient_id, site),
# markers = paste(markers_1, markers_2, markers_3, sep = "_")) %>%