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---
title: "Figures for Tumor-Mediated Immunosuppression and Cytokine Spreading Affects the Relation Between EMT and PD-L1 Status"
author: "Carlijn Lems & Gerhard Burger"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
knitr::opts_chunk$set(tar_interactive = F)
```
# Packages
```{r, results='hide'}
packages <- c("rootSolve", "cowplot", "magick", "colorblindr", "tidyverse", "conicfit", "imager", "fs", "plotrix")
lapply(packages, require, character.only = TRUE)
```
# Globals
```{r}
theme_set(theme_cowplot())
text_size <- 15
label_size <- round(text_size*1.5)
okabe_ito_colors <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7", "#999999")
gradient_colors <- colorRampPalette(okabe_ito_colors[c(4,3)])(5)
```
# Utilities
```{r}
# determine bifurcation points
determine_BP <- function(df, xvar, yvar, res = "y") {
sf <- splinefun(df[[yvar]], df[[xvar]], method = "natural", ties = mean)
roots <- rootSolve::uniroot.all(sf, interval = c(min(df[[yvar]]), max(df[[yvar]])), deriv = 1)
if(res == "y") return(roots)
else if(res == "x") return(sf(roots))
else stop("invalid option for res")
}
# determine bifurcation points of 'island'
determine_BP_island <- function(df, xvar, yvar) {
xy = array(c(df[[xvar]],df[[yvar]]), dim=c(length(df[[yvar]]),2))
ellipDirect <- EllipseDirectFit(xy)
ellipDirectG <- AtoG(ellipDirect)$ParG
roots <- c(ellipDirectG[1]-ellipDirectG[4],ellipDirectG[1]+ellipDirectG[4])
return(roots)
}
bp_to_tibble <- function(bp) {
tibble(value = bp) %>%
mutate(name = paste0("BP", seq_along(bp))) %>%
pivot_wider()
}
# read copasi
read_copasi <- function(x) {
suppressWarnings(read_tsv(x, col_types = cols())) %>%
select(where(is.numeric))
}
# read morpheus
read_morpheus <- function(x) {
suppressWarnings(read_tsv(x, col_types = cols()))
}
```
# Figures
## 1. PD-L1-mediated IFNγ inhibition
### Schematic overview
```{r}
ifn_inh_pdl <- load.image("data/inkscape/ifn_inh_pdl_nf.png")
ifn_inh_pdl_image <- ggplot() + theme(line = element_blank())
ifn_inh_pdl_image <- ggdraw(ifn_inh_pdl_image) + draw_image(ifn_inh_pdl, x = -0.2, y = -0.1, width = 1.3, height = 1.3)
```
### Data
```{r}
twod_copasi <- read_copasi("data/copasi_data/bifs/ifn_inh_pdl_2d_bif.txt")
twod_copasi_nf <- read_copasi("data/copasi_data/bifs/ifn_inh_pdl_nf_2d_bif.txt")
g_I_cont <- read_copasi("data/copasi_data/bifs/ifn_inh_pdl_ifn_bif.txt")
g_I_cont_nf <- read_copasi("data/copasi_data/bifs/ifn_inh_pdl_nf_ifn_bif.txt")
```
### SNAIL1 bifurcations
```{r}
data <- twod_copasi %>% filter(`(R21 - production I).g_I` == 0.1)
data_nf <- twod_copasi_nf %>% filter(`(R21 - production I).g_I` == 0.1)
bp = determine_BP(data_nf, "# Values[S].InitialValue", "[mZ]")
data_nf = data_nf %>% mutate(interval = findInterval(`[mZ]`, bp))
# PD-L1
PM_smooth <- spline(data$`[PM]`, data$`# Values[S].InitialValue`, n = 10*length(data$`[PM]`))
PM_smooth <- tibble(`[PM]` = PM_smooth$x, `# Values[S].InitialValue` = PM_smooth$y)
bp = determine_BP(PM_smooth, "# Values[S].InitialValue", "[PM]")
PM_smooth <- PM_smooth %>% mutate(interval = findInterval(`[PM]`, bp))
data_PM <- tribble(
~experiment, ~data,
"no_nf", PM_smooth,
"nf", data_nf,
) %>%
rowwise() %>%
unnest(cols = data)
exp_order <- c("no_nf", "nf")
exp_labels <- c(expression(paste('No IFN', gamma, ' inhibition')),
expression(paste('Inhibition of IFN', gamma, ' by PD-L1'))
) %>% setNames(exp_order)
plot_data_PM <- data_PM %>% mutate(experiment = factor(experiment, levels = exp_order))
plot_data_PM <- plot_data_PM %>%
mutate(interval = factor(interval, levels = c("0", "1", "2", "3", "4", "5", "6"),
labels = c("E", "Unstable 1", "E/M", "Unstable 2", "M", "M", "M")))
xmin = 1.7
xmax = 2.3
plot_data_PM %>%
ggplot(aes(`# Values[S].InitialValue`/1e5, `[PM]`, group = interaction(experiment, interval), linetype = experiment, color = interval)) +
geom_line(size = 1.2) +
geom_vline(xintercept = 1.95, linetype = "dashed") +
scale_linetype_discrete(labels = exp_labels, name = NULL) +
scale_color_manual(values = gradient_colors, name = NULL) +
labs(x = expression(paste('SNAIL1 (10'^'5', ' molecules)')), y = 'PD-L1 membrane (molecules)') +
coord_cartesian(xlim = c(xmin,xmax), ylim = c(0, 3.2e5)) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0)) -> bif_S_PM
legend_bif_S_PM <- get_legend(bif_S_PM + guides(linetype = "none"))
# ZEB1 mRNA
mZ_smooth <- spline(data$`[mZ]`, data$`# Values[S].InitialValue`, n = 10*length(data$`[mZ]`))
mZ_smooth <- tibble(`[mZ]` = mZ_smooth$x, `# Values[S].InitialValue` = mZ_smooth$y)
bp = determine_BP(mZ_smooth, "# Values[S].InitialValue", "[mZ]")
mZ_smooth <- mZ_smooth %>% mutate(interval = findInterval(`[mZ]`, bp))
data_mZ <- tribble(
~experiment, ~data,
"no_nf", mZ_smooth,
"nf", data_nf,
) %>%
rowwise() %>%
unnest(cols = data)
plot_data_mZ <- data_mZ %>% mutate(experiment = factor(experiment, levels = exp_order))
plot_data_mZ <- plot_data_mZ %>%
mutate(interval = factor(interval, levels = c("0", "1", "2", "3", "4", "5", "6"),
labels = c("E", "Unstable 1", "E/M", "Unstable 2", "M", "M", "M")))
plot_data_mZ %>%
ggplot(aes(`# Values[S].InitialValue`/1e5, `[mZ]`, group = interaction(experiment, interval), linetype = experiment, color = interval)) +
geom_line(size = 1.2) +
geom_vline(xintercept = 1.95, linetype = "dashed") +
scale_linetype_discrete(labels = exp_labels, name = NULL) +
scale_color_manual(values = gradient_colors, name = NULL) +
labs(x = expression(paste('SNAIL1 (10'^'5', ' molecules)')), y = 'ZEB1 mRNA (molecules)') +
coord_cartesian(xlim = c(xmin,xmax)) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0)) +
expand_limits(y = 0) -> bif_S_mZ
legend_bif_S_mZ <- get_legend(bif_S_mZ + guides(linetype = "none"))
```
### IFNγ bifurcation
```{r}
data <- tribble(
~experiment, ~data,
"no_nf", g_I_cont,
"nf", g_I_cont_nf,
) %>%
rowwise() %>%
mutate(
bp = list(determine_BP(data, "# (R21 - production I).g_I", "[mZ]")),
data = list(data %>% mutate(interval = findInterval(`[mZ]`, bp)))
) %>%
unnest(cols = data)
unstable_1_data <- data %>% filter((interval == 1 | interval == 2 | interval == 3) & experiment == "no_nf")
unstable_1_smooth <- spline(unstable_1_data$`# (R21 - production I).g_I`, unstable_1_data$`[PM]`, n = 10*length(unstable_1_data$`# (R21 - production I).g_I`))
unstable_1_smooth <- tibble(`# (R21 - production I).g_I` = unstable_1_smooth$x, `[PM]` = unstable_1_smooth$y, experiment = "no_nf", interval = 1)
data <- bind_rows(data, unstable_1_smooth)
plot_data <- data %>% mutate(experiment = factor(experiment, levels = exp_order))
plot_data <- plot_data %>%
mutate(interval = factor(interval, levels = c("0", "1", "2", "3", "4", "5", "6"),
labels = c("E", "Unstable 1", "Unstable 1", "Unstable 1", "E/M", "Unstable 2", "M")))
plot_data %>%
ggplot(aes(`# (R21 - production I).g_I`, `[PM]`, group = interaction(experiment, interval), linetype = experiment, color = interval)) +
geom_line(size = 1.2) +
scale_linetype_discrete(labels = exp_labels, name = NULL) +
scale_color_manual(values = gradient_colors, name = NULL) +
labs(x = expression(paste('Basal IFN', gamma, ' prod. rate (nM hr'^'-1', ')')), y = 'PD-L1 membrane (molecules)') +
coord_cartesian(xlim = c(0, 0.5), ylim = c(0, 3.2e5)) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) -> bif_I_PM
legend_bif_I_PM <- get_legend(bif_I_PM + guides(linetype = "none"))
```
### 2D bifurcation
```{r}
twod_copasi %>%
drop_na() %>%
nest_by(`(R21 - production I).g_I`) %>%
mutate(
bps = list(determine_BP(data, "# Values[S].InitialValue", "[mZ]", res = "x"))
) %>%
filter(length(bps) <= 4) %>% # sometimes too many bps are detected
summarize(bp_to_tibble(bps)) %>%
rename(IFN = `(R21 - production I).g_I`) -> sum2
BP1_sp <- spline(sum2$IFN, sum2$BP1, xmax = 0.2, n = 10*length(sum2$IFN))
BP2_sp <- spline(sum2$IFN, sum2$BP2, xmax = 0.2, n = 10*length(sum2$IFN))
BP3_sp <- spline(sum2$IFN, sum2$BP3, xmax = 0.2, n = 10*length(sum2$IFN))
BP4_sp <- spline(sum2$IFN, sum2$BP4, xmax = 0.2, n = 10*length(sum2$IFN))
BP_smooth <- tibble(IFN = BP1_sp$x, BP1 = BP1_sp$y, BP2 = BP2_sp$y, BP3 = BP3_sp$y, BP4 = BP4_sp$y)
twod_copasi_nf %>%
drop_na() %>%
nest_by(`(R21 - production I).g_I`) %>%
mutate(
bps = list(determine_BP(data, "# Values[S].InitialValue", "[mZ]", res = "x"))
) %>%
filter(length(bps) <= 4) %>%
summarize(bp_to_tibble(bps)) %>%
rename(IFN = `(R21 - production I).g_I`) -> sum2
BP1_sp <- spline(sum2$IFN, sum2$BP1, xmax = 0.2, n = 10*length(sum2$IFN))
BP2_sp <- spline(sum2$IFN, sum2$BP2, xmax = 0.2, n = 10*length(sum2$IFN))
BP3_sp <- spline(sum2$IFN, sum2$BP3, xmax = 0.2, n = 10*length(sum2$IFN))
BP4_sp <- spline(sum2$IFN, sum2$BP4, xmax = 0.2, n = 10*length(sum2$IFN))
BP_smooth_nf <- tibble(IFN = BP1_sp$x, BP1 = BP1_sp$y, BP2 = BP2_sp$y, BP3 = BP3_sp$y, BP4 = BP4_sp$y)
plot_data <- tribble(
~experiment, ~data,
"no_nf", BP_smooth,
"nf", BP_smooth_nf,
) %>%
unnest(cols = data) %>%
mutate(experiment = factor(experiment, levels = exp_order, labels = exp_labels))
plot_data %>%
ggplot() +
geom_ribbon(aes(y = IFN, xmin = xmin, xmax = xmax, fill = '{E}')) +
geom_ribbon(aes(y = IFN, xmin = BP4/1e5, xmax = xmax, fill = '{E, M}')) +
geom_ribbon(aes(y = IFN, xmin = BP2/1e5, xmax = xmax, fill = '{E, E/M, M}')) +
geom_ribbon(aes(y = IFN, xmin = BP1/1e5, xmax = xmax, fill = '{E/M, M}')) +
geom_ribbon(aes(y = IFN, xmin = BP3/1e5, xmax = xmax, fill = '{M}')) +
geom_vline(xintercept = 1.95, linetype = "dashed") +
geom_hline(yintercept = 0.1, linetype = "dashed") +
coord_cartesian(ylim = c(0,0.2)) +
labs(x = expression(paste('SNAIL1 (10'^'5', ' molecules)')), y = expression(paste('Basal IFN', gamma, ' prod. rate (nM hr'^'-1', ')')), fill = NULL) +
scale_fill_OkabeIto(order = c(6,2,7,5,1), limits = c('{E}','{E, M}', '{E, E/M, M}', '{E/M, M}', '{M}')) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0), breaks = seq(0, 0.2, by =0.05)) +
facet_grid(~experiment, labeller = label_parsed) -> twod_bif_nf
```
### Combined
```{r}
A <- plot_grid(ifn_inh_pdl_image,
bif_S_PM + theme_cowplot(text_size) + guides(color = "none") + theme(legend.position = "top", legend.justification = "center"),
legend_bif_S_PM,
NULL,
rel_widths = c(1.0, 0.78, 0.0, 0.22), labels = c("A", "B", "", ""), label_size = label_size, ncol = 4, align = "vh", axis = "tlbr", scale = 0.9)
B <- plot_grid(bif_I_PM + theme_cowplot(text_size) + guides(color = "none") + theme(legend.position = "top", legend.justification = "center"),
legend_bif_I_PM,
NULL,
bif_S_mZ + theme_cowplot(text_size) + guides(color = "none") + theme(legend.position = "top", legend.justification = "center"),
legend_bif_S_mZ,
NULL,
rel_widths = c(0.78, 0.0, 0.22, 0.78, 0.0, 0.22), labels = c("C", "", "", "D", "", ""), label_size = label_size, ncol = 6, align = "vh", axis = "tlbr", scale = 0.9)
C <- plot_grid(twod_bif_nf + theme_cowplot(text_size) + theme(strip.placement = 'outside', strip.background = element_blank(), legend.position="right", panel.spacing = unit(4, "lines")),
labels = "E", label_size = label_size, ncol = 1, align = "vh", axis = "tlbr", scale = 0.9)
figure1 <- plot_grid(A,
B,
C,
ncol = 1, align = "vh", axis = "tlbr", rel_heights = c(1, 1, 0.8))
ggsave("output/figure1.pdf", width = 12, height = 15.75)
```
## 2. TGFβ-mediated IFNγ inhibition
### Schematic overview
```{r}
full_model_ti <- load.image("data/inkscape/full_model_ti.png")
full_model_ti_image <- ggplot() + theme(line = element_blank())
full_model_ti_image <- ggdraw(full_model_ti_image) + draw_image(full_model_ti, x = -0.2, y = -0.1, width = 1.3, height = 1.3)
```
### Data
```{r}
twod_copasi_ext <- read_copasi("data/copasi_data/bifs/full_model_2d_bif.txt") %>%
rename(IFN = `(R21 - production I).g_I`,
TGF = `# (R23 - production T).gT`)
twod_copasi_ext_ti <- bind_rows(
read_copasi("data/copasi_data/bifs/full_model_ti_2d_bif_i.txt"),
read_copasi("data/copasi_data/bifs/full_model_ti_2d_bif_ii.txt"),
read_copasi("data/copasi_data/bifs/full_model_ti_2d_bif_iii.txt")
) %>%
rename(IFN = `(R21 - production I).g_I`,
TGF = `# (R23 - production T).gT`)
```
### TGFβ bifurcations
```{r}
data_0_06 <- twod_copasi_ext %>% filter(IFN == 0.06)
data_0_11 <- twod_copasi_ext %>% filter(IFN == 0.11)
data_0_16 <- twod_copasi_ext %>% filter(IFN == 0.16)
data_ti_0_06 <- twod_copasi_ext_ti %>% filter(IFN == 0.06)
bp = determine_BP(data_ti_0_06, "TGF", "[mZ]")
data_ti_0_06 = data_ti_0_06 %>% mutate(interval = findInterval(`[mZ]`, bp))
data_ti_0_11 <- twod_copasi_ext_ti %>% filter(IFN == 0.11)
bp = determine_BP(data_ti_0_11, "TGF", "[mZ]")
data_ti_0_11 = data_ti_0_11 %>% mutate(interval = findInterval(`[mZ]`, bp))
data_ti_0_16 <- twod_copasi_ext_ti %>% filter(IFN == 0.16)
bp = determine_BP(data_ti_0_16, "TGF", "[mZ]")
data_ti_0_16 = data_ti_0_16 %>% mutate(interval = findInterval(`[mZ]`, bp))
# PD-L1
PM_smooth_0_06 <- spline(data_0_06$`[PM]`, data_0_06$TGF, n = 10*length(data_0_06$`[PM]`))
PM_smooth_0_06 <- tibble(`[PM]` = PM_smooth_0_06$x, TGF = PM_smooth_0_06$y)
bp = determine_BP(PM_smooth_0_06, "TGF", "[PM]")
PM_smooth_0_06 <- PM_smooth_0_06 %>% mutate(interval = findInterval(`[PM]`, bp))
PM_smooth_0_11 <- spline(data_0_11$`[PM]`, data_0_11$TGF, n = 10*length(data_0_11$`[PM]`))
PM_smooth_0_11 <- tibble(`[PM]` = PM_smooth_0_11$x, TGF = PM_smooth_0_11$y)
bp = determine_BP(PM_smooth_0_11, "TGF", "[PM]")
PM_smooth_0_11 <- PM_smooth_0_11 %>% mutate(interval = findInterval(`[PM]`, bp))
PM_smooth_0_16 <- spline(data_0_16$`[PM]`, data_0_16$TGF, n = 10*length(data_0_16$`[PM]`))
PM_smooth_0_16 <- tibble(`[PM]` = PM_smooth_0_16$x, TGF = PM_smooth_0_16$y)
bp = determine_BP(PM_smooth_0_16, "TGF", "[PM]")
PM_smooth_0_16 <- PM_smooth_0_16 %>% mutate(interval = findInterval(`[PM]`, bp))
data_PM <- tribble(
~experiment, ~concentration, ~data,
"no_ti", "ifn_0_06", PM_smooth_0_06,
"ti", "ifn_0_06", data_ti_0_06,
"no_ti", "ifn_0_11", PM_smooth_0_11,
"ti", "ifn_0_11", data_ti_0_11,
"no_ti", "ifn_0_16", PM_smooth_0_16,
"ti", "ifn_0_16", data_ti_0_16,
) %>%
rowwise() %>%
unnest(cols = data)
exp_order <- c("no_ti", "ti")
exp_labels <- c(expression(paste('No IFN', gamma, ' inhibition')),
expression(paste('Inhibition of IFN', gamma, ' by TGF', beta))
) %>% setNames(exp_order)
conc_order <- c("ifn_0_06", "ifn_0_11", "ifn_0_16")
conc_labels <- c(expression(paste('Low IFN', gamma, ' production')),
expression(paste('Intermediate IFN', gamma, ' production')),
expression(paste('High IFN', gamma, ' production'))
) %>% setNames(conc_order)
xmin = 0
xmax = 0.25
plot_data_PM <- data_PM %>% mutate(experiment = factor(experiment, levels = exp_order),
concentration = factor(concentration, levels = conc_order, labels = conc_labels)) %>%
mutate(interval = factor(interval, levels = c("0", "1", "2", "3", "4", "5", "6"),
labels = c("E", "Unstable 1", "E/M", "Unstable 2", "M", "M", "M")))
plot_data_PM %>%
ggplot(aes(TGF, `[PM]`, group = interaction(experiment, interval), linetype = experiment, color = interval)) +
geom_line(size = 1.2) +
scale_linetype_discrete(labels = exp_labels, name = NULL) +
scale_color_manual(values = gradient_colors, name = NULL) +
labs(x = expression(paste('TGF', beta, ' (nM)')), y = 'PD-L1 membrane (molecules)') +
coord_cartesian(xlim = c(xmin,xmax), ylim = c(0, 3.2e5)) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0)) +
facet_grid(~concentration, labeller = label_parsed) -> bif_T_PM
legend_bif_T_PM <- get_legend(bif_T_PM + guides(linetype = "none"))
# ZEB1 mRNA
mZ_smooth_0_11 <- spline(data_0_11$`[mZ]`, data_0_11$TGF, n = 10*length(data_0_11$`[mZ]`))
mZ_smooth_0_11 <- tibble(`[mZ]` = mZ_smooth_0_11$x, TGF = mZ_smooth_0_11$y)
bp = determine_BP(mZ_smooth_0_11, "TGF", "[mZ]")
mZ_smooth_0_11 <- mZ_smooth_0_11 %>% mutate(interval = findInterval(`[mZ]`, bp))
data_mZ <- tribble(
~experiment, ~concentration, ~data,
"no_ti", "ifn_0_11", mZ_smooth_0_11,
"ti", "ifn_0_11", data_ti_0_11,
) %>%
rowwise() %>%
unnest(cols = data)
plot_data_mZ <- data_mZ %>% mutate(experiment = factor(experiment, levels = exp_order),
concentration = factor(concentration, levels = conc_order)) %>%
mutate(interval = factor(interval, levels = c("0", "1", "2", "3", "4", "5", "6"),
labels = c("E", "Unstable 1", "E/M", "Unstable 2", "M", "M", "M")))
plot_data_mZ %>%
ggplot(aes(TGF, `[mZ]`, group = interaction(experiment, interval), linetype = experiment, color = interval)) +
geom_line(size = 1.2) +
scale_linetype_discrete(labels = exp_labels, name = NULL) +
scale_color_manual(values = gradient_colors, name = NULL) +
labs(x = expression(paste('TGF', beta, ' (nM)')), y = 'ZEB mRNA (molecules)') +
coord_cartesian(xlim = c(xmin,xmax)) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0)) -> bif_T_mZ
legend_bif_T_mZ <- get_legend(bif_T_mZ + guides(linetype = "none"))
```
### 2D bifurcation
#### Without IFNγ inhibition
```{r}
twod_copasi_ext %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP(data, "TGF", "[mZ]", res = "x"))
) %>%
filter(length(bps) <= 4) %>%
summarize(bp_to_tibble(bps)) -> sum2
BP1_sp <- spline(sum2$IFN, sum2$BP1, xmax = 0.4, n = 10*length(sum2$IFN))
BP2_sp <- spline(sum2$IFN, sum2$BP2, xmax = 0.4, n = 10*length(sum2$IFN))
BP3_sp <- spline(sum2$IFN, sum2$BP3, xmax = 0.4, n = 10*length(sum2$IFN))
BP4_sp <- spline(sum2$IFN, sum2$BP4, xmax = 0.4, n = 10*length(sum2$IFN))
BP_smooth <- tibble(IFN = BP1_sp$x, BP1 = BP1_sp$y, BP2 = BP2_sp$y, BP3 = BP3_sp$y, BP4 = BP4_sp$y)
plot_data <- tribble(
~experiment, ~data,
"no_ti", BP_smooth,
) %>%
unnest(cols = data)
plot_data %>% ggplot() +
geom_ribbon(aes(y = IFN, xmin = xmin, xmax = xmax, fill = '{E}')) +
geom_ribbon(aes(y = IFN, xmin = BP4, xmax = xmax, fill = '{E, M}')) +
geom_ribbon(aes(y = IFN, xmin = BP2, xmax = xmax, fill = '{E, E/M, M}')) +
geom_ribbon(aes(y = IFN, xmin = BP1, xmax = xmax, fill = '{E/M, M}')) +
geom_ribbon(aes(y = IFN, xmin = BP3, xmax = xmax, fill = '{M}')) +
geom_hline(yintercept = 0.06, linetype = "dashed") +
geom_hline(yintercept = 0.11, linetype = "dashed") +
geom_hline(yintercept = 0.16, linetype = "dashed") +
coord_cartesian(xlim = c(0,0.25), ylim = c(0,0.2)) +
labs(x = expression(paste('TGF', beta, ' (nM)')), y = expression(paste('Basal IFN', gamma, ' prod. rate (nM hr'^'-1', ')')), fill = NULL) +
scale_fill_OkabeIto(order = c(6,2,7,5,1), limits = c('{E}','{E, M}', '{E, E/M, M}', '{E/M, M}', '{M}')) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0), breaks = seq(0, 0.2, by = 0.05)) +
ggtitle(expression(paste('No IFN', gamma, ' inhibition'))) -> twod_bif_ext
plot_grid(twod_bif_ext + theme_cowplot(text_size) + theme(axis.title.x=element_text(hjust=1.34), plot.margin = unit(c(0, 0, 0, 0), "cm"), plot.title = element_text(hjust = 0.5, size = 0.875*text_size), strip.placement = 'outside', strip.background = element_blank(), legend.position="None", panel.spacing = unit(4, "lines")),
labels = "", label_size = label_size, ncol = 1, align = "vh", axis = "tlbr") -> twod_bif_ext
```
#### With IFNγ inhibition
Calculate BP1 (separates {E, E/M, M} from {E/M, M})
```{r}
T_cutoffs <- c(0.097, 0.111)
I_cutoffs <- c(0.0826, 0.0824, 0.0815)
mZ_cutoff <- 200
# Top section
twod_copasi_ext_ti %>%
filter(IFN != I_cutoffs[1] & IFN >= I_cutoffs[2]) %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP(data, "TGF", "[mZ]", res = "x"))
) -> nested
BP1_top <- tibble('gI' = unlist(nested$IFN), 'BP' = sapply(nested$bps,"[[",1))
# Section left of island
twod_copasi_ext_ti %>%
filter(IFN < I_cutoffs[2] & IFN >= I_cutoffs[3] & TGF < T_cutoffs[1]) %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP(data, "TGF", "[mZ]", res = "x"))
) -> nested
BP1_left_of_island <- tibble('gI' = unlist(nested$IFN), 'BP' = unlist(nested$bps)[c(TRUE, FALSE, FALSE, FALSE)])
# Island section
twod_copasi_ext_ti %>%
filter(IFN < I_cutoffs[2] & IFN >= I_cutoffs[3] & TGF > T_cutoffs[1] & `[mZ]` < mZ_cutoff) %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP_island(data, "TGF", "[mZ]"))
) -> nested
BP1_island_left <- tibble('gI' = unlist(nested$IFN), 'BP' = unlist(nested$bps)[c(TRUE, FALSE)])
BP1_island_right <- tibble('gI' = unlist(nested$IFN), 'BP' = unlist(nested$bps)[c(FALSE, TRUE)])
# Bottom section
twod_copasi_ext_ti %>%
filter(IFN < I_cutoffs[3] & TGF > T_cutoffs[2] & `[mZ]` < mZ_cutoff) %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP(data, "TGF", "[mZ]", res = "x"))
) -> nested
BP1_bottom <- tibble('gI' = unlist(nested$IFN), 'BP' = unlist(nested$bps))
# Combined
BP1 <- rbind(BP1_top, BP1_left_of_island, BP1_island_left, BP1_island_right, BP1_bottom)
```
Calculate BP3 (separates {E/M, M} from {M})
```{r}
T_cutoffs <- c(0.162, 0.2)
I_cutoffs <- c(0.1432, 0.1356, 0.141, 0.137)
mZ_cutoff <- 600
# Top section
twod_copasi_ext_ti %>%
filter(IFN >= I_cutoffs[1]) %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP(data, "TGF", "[mZ]", res = "x"))
) -> nested
BP3_top <- tibble('gI' = unlist(nested$IFN), 'BP' = unlist(nested$bps)[c(FALSE, FALSE, TRUE, FALSE)])
# Section left of island
twod_copasi_ext_ti %>%
filter(IFN < I_cutoffs[3] & IFN >= I_cutoffs[2] & TGF < T_cutoffs[1]) %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP(data, "TGF", "[mZ]", res = "x"))
) -> nested
BP3_left_of_island <- tibble('gI' = unlist(nested$IFN), 'BP' = sapply(sapply(nested$bps, rev),"[[",2))
# Island section
twod_copasi_ext_ti %>%
filter(IFN < I_cutoffs[1] & IFN >= I_cutoffs[4] & TGF > T_cutoffs[1] & `[mZ]` < mZ_cutoff) %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP_island(data, "TGF", "[mZ]"))
) -> nested
BP3_island_left <- tibble('gI' = unlist(nested$IFN), 'BP' = unlist(nested$bps)[c(TRUE, FALSE)])
BP3_island_right <- tibble('gI' = unlist(nested$IFN), 'BP' = unlist(nested$bps)[c(FALSE, TRUE)])
# Bottom section
twod_copasi_ext_ti %>%
filter(IFN < I_cutoffs[2] & TGF > T_cutoffs[2] & `[mZ]` < mZ_cutoff) %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP(data, "TGF", "[mZ]", res = "x"))
) -> nested
BP3_bottom <- tibble('gI' = unlist(nested$IFN), 'BP' = unlist(nested$bps))
# Combined
BP3 <- rbind(BP3_top, BP3_left_of_island, BP3_island_left, BP3_island_right, BP3_bottom)
```
Plot figure
```{r}
twod_copasi_ext_ti %>%
drop_na() %>%
nest_by(IFN) %>%
mutate(
bps = list(determine_BP(data, "TGF", "[mZ]", res = "x"))
) %>%
filter(length(bps) <= 4) %>%
summarize(bp_to_tibble(bps)) -> sum2
BP2_sp <- spline(sum2$IFN, sum2$BP2, xmax = 0.4, n = 10*length(sum2$IFN))
BP4_sp <- spline(sum2$IFN, sum2$BP4, xmax = 0.4, n = 10*length(sum2$IFN))
plot_data_ti <- tibble(IFN = BP2_sp$x, BP2 = BP2_sp$y, BP4 = BP4_sp$y)
ggplot() +
geom_ribbon(aes(y = plot_data_ti$IFN, xmin = xmin, xmax = xmax, fill = '{E}')) +
geom_ribbon(aes(y = plot_data_ti$IFN, xmin = plot_data_ti$BP4, xmax = xmax, fill = '{E, M}')) +
geom_ribbon(aes(y = plot_data_ti$IFN, xmin = plot_data_ti$BP2, xmax = xmax, fill = '{M}')) +
geom_ribbon(aes(y = plot_data_ti$IFN, xmin = plot_data_ti$BP2, xmax = min(BP3$BP + 0.0001), fill = '{E/M, M}')) +
geom_ribbon(aes(y = plot_data_ti$IFN, xmin = plot_data_ti$BP2, xmax = min(BP1$BP + 0.0001), fill = '{E, E/M, M}')) +
geom_area(aes(x = BP3$BP, y = BP3$gI, fill = '{E/M, M}')) +
geom_area(aes(x = BP1$BP, y = BP1$gI, fill = '{E, E/M, M}')) +
geom_ribbon(aes(y = plot_data_ti$IFN, xmin = max(BP3$BP - 0.001), xmax = xmax, fill = '{M}')) +
geom_ribbon(aes(y = plot_data_ti$IFN, xmin = max(BP1$BP - 0.001), xmax = min(BP3$BP), fill = '{E/M, M}')) +
geom_hline(yintercept = 0.06, linetype = "dashed") +
geom_hline(yintercept = 0.11, linetype = "dashed") +
geom_hline(yintercept = 0.16, linetype = "dashed") +
coord_cartesian(xlim = c(0,0.25), ylim = c(0,0.2)) +
labs(x = expression(paste('TGF', beta, ' (nM)')), y = "", fill = NULL) +
scale_fill_OkabeIto(order = c(6,2,7,5,1), limits = c('{E}','{E, M}', '{E, E/M, M}', '{E/M, M}', '{M}')) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0), breaks = seq(0, 0.2, by =0.05)) +
ggtitle(expression(paste('Inhibition of IFN', gamma, ' by TGF', beta))) -> twod_bif_ext_ti
plot_grid(twod_bif_ext_ti + theme_cowplot(text_size) + theme(plot.margin = unit(c(0, 0, 0, 0), "cm"), axis.line.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title.x = element_text(colour = "white"), plot.title = element_text(hjust = 0.5, size = (14/16)*text_size), strip.placement = 'outside', strip.background = element_blank(), legend.position="right", panel.spacing = unit(4, "lines")),
labels = "", label_size = label_size, ncol = 1, align = "vh", axis = "tlbr") -> twod_bif_ext_ti
plot_grid(twod_bif_ext,
twod_bif_ext_ti,
labels = "", label_size = label_size, ncol = 2, align = "vh", axis = "tlbr", scale = 0.9, rel_widths = c(1, 1.2)) -> twod_bif_ext_full
```
### Combined
```{r}
A <- plot_grid(full_model_ti_image,
bif_T_mZ + guides(color = "none") + theme_cowplot(text_size) + theme(legend.position="top", legend.justification = "center"),
legend_bif_T_mZ,
NULL,
rel_widths = c(1.0, 0.78, 0.0, 0.22), labels = c("A", "C", "", ""), label_size = label_size, ncol = 4, align = "vh", axis = "tlbr", scale = 0.9)
B <- plot_grid(bif_T_PM + guides(color = "none") + theme_cowplot(text_size) + theme(strip.placement = 'outside', strip.background = element_blank(), legend.position="top", legend.justification = "center", panel.spacing = unit(4, "lines")),
legend_bif_T_PM,
NULL,
rel_widths = c(0.89, 0.0, 0.11), labels = c("B", "", ""), label_size = label_size, ncol = 3, align = "vh", axis = "tlbr", scale = 0.9)
C <- plot_grid(twod_bif_ext_full,
scale = 0.93, labels = "D", label_size = label_size)
figure2 <- plot_grid(A,
B,
C,
ncol = 1, align = "vh", axis = "tlbr", rel_heights = c(1, 0.9, 0.8))
ggsave("output/figure2.pdf", width = 12, height = 15.1875)
```
## 3. Long-range IFNγ spreading
### Simulation image
```{r}
long <- load.image("data/inkscape/long_image.png")
long_image <- ggplot() + theme(line = element_blank())
long_image <- ggdraw(long_image) + draw_image(long, x = -0.02, y = -0.02, width = 1.05, height = 1.05)
```
### Data
```{r}
morpheus_base_path <- path("data/morpheus_data/")
data_long <- tribble(
~experiment, ~condition, ~replicate, ~file,
"hom", "no_im", "1", "long_tgf_uni_246/logger.csv",
"hom", "no_im", "2", "long_tgf_uni_247/logger.csv",
"hom", "no_im", "3", "long_tgf_uni_248/logger.csv",
"hom", "no_im", "4", "long_tgf_uni_249/logger.csv",
"hom", "no_im", "5", "long_tgf_uni_250/logger.csv",
"hom", "pd_im", "1", "long_tgf_uni_251/logger.csv",
"hom", "pd_im", "2", "long_tgf_uni_252/logger.csv",
"hom", "pd_im", "3", "long_tgf_uni_253/logger.csv",
"hom", "pd_im", "4", "long_tgf_uni_254/logger.csv",
"hom", "pd_im", "5", "long_tgf_uni_255/logger.csv",
"hom", "tgf_im", "1", "long_tgf_uni_256/logger.csv",
"hom", "tgf_im", "2", "long_tgf_uni_257/logger.csv",
"hom", "tgf_im", "3", "long_tgf_uni_258/logger.csv",
"hom", "tgf_im", "4", "long_tgf_uni_259/logger.csv",
"hom", "tgf_im", "5", "long_tgf_uni_260/logger.csv",
"het", "no_im", "1", "long_tgf_uni_288/logger.csv",
"het", "no_im", "2", "long_tgf_uni_289/logger.csv",
"het", "no_im", "3", "long_tgf_uni_290/logger.csv",
"het", "no_im", "4", "long_tgf_uni_291/logger.csv",
"het", "no_im", "5", "long_tgf_uni_292/logger.csv",
"het", "pd_im", "1", "long_tgf_uni_286/logger.csv",
"het", "pd_im", "2", "long_tgf_uni_293/logger.csv",
"het", "pd_im", "3", "long_tgf_uni_294/logger.csv",
"het", "pd_im", "4", "long_tgf_uni_295/logger.csv",
"het", "pd_im", "5", "long_tgf_uni_296/logger.csv",
"het", "tgf_im", "1", "long_tgf_uni_297/logger.csv",
"het", "tgf_im", "2", "long_tgf_uni_298/logger.csv",
"het", "tgf_im", "3", "long_tgf_uni_299/logger.csv",
"het", "tgf_im", "4", "long_tgf_uni_300/logger.csv",
"het", "tgf_im", "5", "long_tgf_uni_301/logger.csv",
) %>%
rowwise() %>%
mutate(data = list(read_morpheus(path(morpheus_base_path, file)))) %>%
select(-file) %>%
unnest(data) %>%
mutate(cell.type = replace(cell.type, cell.type == "1", "E"),
cell.type = replace(cell.type, cell.type == "2", "E/M"),
cell.type = replace(cell.type, cell.type == "3", "M"),
cell.type = replace(cell.type, cell.type == "4", "T"),
) %>%
filter(time > 2100)
exp_order <- c("hom", "het")
exp_labels <- c('Homogeneous \n (SD = 0)',
'Heterogeneous \n (SD = 0.01)'
) %>% setNames(exp_order)
con_order <- c("no_im", "pd_im", "tgf_im")
con_labels <- c(expression(paste('No IFN', gamma, ' inhibition')),
expression(paste('Inh. of IFN', gamma, ' by PD-L1')),
expression(paste('Inh. of IFN', gamma, ' by TGF', beta))
) %>% setNames(con_order)
data_long <- data_long %>% mutate(experiment = factor(experiment, levels = exp_order, labels = exp_labels),
condition = factor(condition, levels = con_order))
```
### IFNγ production
```{r}
plot_data_hom <- data_long %>%
filter(cell.type == 'T') %>%
group_by(experiment, condition) %>%
summarize(
median_gI = median(gI)
)
data_long %>%
filter(cell.type == 'T' & experiment == 'Heterogeneous \n (SD = 0.01)' & condition == 'pd_im') %>%
ggplot() +
geom_violin(aes(condition, gI, fill = 'Heterogeneous \n (SD = 0.01)')) +
scale_x_discrete(labels = con_labels) +
scale_y_continuous(expand = expansion(mult = c(0,0.05))) +
expand_limits(y=0) +
scale_fill_manual(values = c("#00BFC4")) +
scale_color_manual(values = c( "#F8766D")) +
guides(color = guide_legend(reverse = TRUE)) +
labs(x = '', y = expression(paste('T cell IFN', gamma, ' prod. rate (molecules min'^'-1', ')'))) +
geom_boxplot(aes(condition, gI, group = interaction(condition,experiment)), width = 0.2, position = position_dodge(width=0.9)) +
geom_point(aes(1, plot_data_hom[2,3][[1]], color = 'Homogeneous \n (SD = 0)'), size = 3) -> long_I_prod
```
### IFNγ concentration
```{r}
data_long %>%
filter(cell.type == 'E') %>%
ggplot() +
geom_violin(aes(condition, IFN_nM, fill = experiment)) +
scale_y_continuous(expand = expansion(mult = c(0,0.05))) +
scale_x_discrete(labels = con_labels) +
expand_limits(y = 0) +
labs(x = '', y = expression(paste('[IFN', gamma, '] sensed by epith. tumor cells (nM)'))) +
geom_boxplot(aes(condition, IFN_nM, group = interaction(condition,experiment)), width = 0.2, position = position_dodge(width=0.9)) -> long_I_conc
```
### Combined
```{r}
plot_grid(long_image,
labels = "AUTO", label_size = label_size, ncol = 1, align = "vh", axis = "tlbr", scale = 0.9) -> A
plot_grid(long_I_prod + theme_cowplot(text_size) + theme(strip.placement = 'outside', strip.background = element_blank(), legend.position="top", legend.justification = "right", legend.title = element_blank()),
long_I_conc + theme_cowplot(text_size) + theme(strip.placement = 'outside', strip.background = element_blank(), legend.position="top", legend.justification = "right", legend.title = element_blank()),
labels = c("B", "C"), label_size = label_size, ncol = 2, align = "vh", axis = "tlbr", scale = 0.9, rel_widths = c(1, 2)) -> B
plot_grid(A,
B,
ncol = 1, align = "vh", axis = "tlbr", rel_heights = c(0.8, 1.5)) -> figure3
ggsave("output/figure3.pdf", width = 12, height = 9.2)
```
## 4. Short-range IFNγ spreading
### Simulation image
```{r}
short <- load.image("data/inkscape/short_image.png")
short_image <- ggplot() + theme(line = element_blank())
short_image <- ggdraw(short_image) + draw_image(short, x = -0.15, y = -0.1, width = 1.2, height = 1.2)
```
### Data
```{r}
data_short <- tribble(
~experiment, ~condition, ~replicate, ~file,
"no_gr", "no_im", "1", "short_tgf_uni_170/logger.csv",
"no_gr", "no_im", "2", "short_tgf_uni_171/logger.csv",
"no_gr", "no_im", "3", "short_tgf_uni_172/logger.csv",
"no_gr", "no_im", "4", "short_tgf_uni_173/logger.csv",
"no_gr", "no_im", "5", "short_tgf_uni_174/logger.csv",
"no_gr", "no_im", "6", "short_tgf_uni_175/logger.csv",
"no_gr", "no_im", "7", "short_tgf_uni_176/logger.csv",
"no_gr", "no_im", "8", "short_tgf_uni_177/logger.csv",
"no_gr", "no_im", "9", "short_tgf_uni_178/logger.csv",
"no_gr", "no_im", "10", "short_tgf_uni_179/logger.csv",
"no_gr", "pd_im", "1", "short_tgf_uni_180/logger.csv",
"no_gr", "pd_im", "2", "short_tgf_uni_181/logger.csv",
"no_gr", "pd_im", "3", "short_tgf_uni_182/logger.csv",
"no_gr", "pd_im", "4", "short_tgf_uni_183/logger.csv",
"no_gr", "pd_im", "5", "short_tgf_uni_184/logger.csv",
"no_gr", "pd_im", "6", "short_tgf_uni_185/logger.csv",
"no_gr", "pd_im", "7", "short_tgf_uni_186/logger.csv",
"no_gr", "pd_im", "8", "short_tgf_uni_187/logger.csv",
"no_gr", "pd_im", "9", "short_tgf_uni_188/logger.csv",
"no_gr", "pd_im", "10", "short_tgf_uni_189/logger.csv",
"no_gr", "tgf_im", "1", "short_tgf_uni_190/logger.csv",
"no_gr", "tgf_im", "2", "short_tgf_uni_191/logger.csv",
"no_gr", "tgf_im", "3", "short_tgf_uni_192/logger.csv",
"no_gr", "tgf_im", "4", "short_tgf_uni_193/logger.csv",
"no_gr", "tgf_im", "5", "short_tgf_uni_194/logger.csv",
"no_gr", "tgf_im", "6", "short_tgf_uni_195/logger.csv",
"no_gr", "tgf_im", "7", "short_tgf_uni_196/logger.csv",
"no_gr", "tgf_im", "8", "short_tgf_uni_197/logger.csv",
"no_gr", "tgf_im", "9", "short_tgf_uni_198/logger.csv",
"no_gr", "tgf_im", "10", "short_tgf_uni_199/logger.csv",
"gr", "no_im", "1", "short_tgf_grad_200/logger.csv",
"gr", "no_im", "2", "short_tgf_grad_201/logger.csv",
"gr", "no_im", "3", "short_tgf_grad_202/logger.csv",
"gr", "no_im", "4", "short_tgf_grad_203/logger.csv",
"gr", "no_im", "5", "short_tgf_grad_204/logger.csv",
"gr", "no_im", "6", "short_tgf_grad_205/logger.csv",
"gr", "no_im", "7", "short_tgf_grad_206/logger.csv",
"gr", "no_im", "8", "short_tgf_grad_207/logger.csv",
"gr", "no_im", "9", "short_tgf_grad_208/logger.csv",
"gr", "no_im", "10", "short_tgf_grad_209/logger.csv",
"gr", "pd_im", "1", "short_tgf_grad_210/logger.csv",
"gr", "pd_im", "2", "short_tgf_grad_211/logger.csv",
"gr", "pd_im", "3", "short_tgf_grad_212/logger.csv",
"gr", "pd_im", "4", "short_tgf_grad_213/logger.csv",
"gr", "pd_im", "5", "short_tgf_grad_214/logger.csv",
"gr", "pd_im", "6", "short_tgf_grad_215/logger.csv",
"gr", "pd_im", "7", "short_tgf_grad_216/logger.csv",
"gr", "pd_im", "8", "short_tgf_grad_217/logger.csv",
"gr", "pd_im", "9", "short_tgf_grad_218/logger.csv",
"gr", "pd_im", "10", "short_tgf_grad_219/logger.csv",
"gr", "tgf_im", "1", "short_tgf_grad_220/logger.csv",
"gr", "tgf_im", "2", "short_tgf_grad_221/logger.csv",
"gr", "tgf_im", "3", "short_tgf_grad_222/logger.csv",
"gr", "tgf_im", "4", "short_tgf_grad_223/logger.csv",
"gr", "tgf_im", "5", "short_tgf_grad_224/logger.csv",
"gr", "tgf_im", "6", "short_tgf_grad_225/logger.csv",
"gr", "tgf_im", "7", "short_tgf_grad_226/logger.csv",
"gr", "tgf_im", "8", "short_tgf_grad_227/logger.csv",
"gr", "tgf_im", "9", "short_tgf_grad_228/logger.csv",
"gr", "tgf_im", "10", "short_tgf_grad_229/logger.csv",
) %>%
rowwise() %>%
mutate(data = list(read_morpheus(path(morpheus_base_path, file)))) %>%
select(-file) %>%
unnest(data) %>%
mutate(cell.type = replace(cell.type, cell.type == "1", "E"),
cell.type = replace(cell.type, cell.type == "2", "E/M"),
cell.type = replace(cell.type, cell.type == "3", "M"),
cell.type = replace(cell.type, cell.type == "4", "T"),
)
exp_order <- c("no_gr", "gr")
exp_labels <- c(expression(paste('TGF', beta, ' uniform (0.1 nM)')),
expression(paste('TGF', beta, ' gradient'))
) %>% setNames(exp_order)
con_order <- c("no_im", "pd_im", "tgf_im")
con_labels <- c(expression(paste('No IFN', gamma, ' inhibition')),
expression(paste('Inh. of IFN', gamma, ' by PD-L1')),
expression(paste('Inh. of IFN', gamma, ' by TGF', beta))
) %>% setNames(con_order)
data_short <- data_short %>% mutate(experiment = factor(experiment, levels = exp_order, labels = exp_labels),
condition = factor(condition, levels = con_order))
```
### Time course PD-L1/ZEB1
```{r}
plot_data <- data_short %>%
filter(cell.type != 'T') %>%
group_by(experiment, condition, time, replicate) %>%
summarize(mean_PM = mean(PM),
sd_PM = sd(PM),
se_PM = std.error(PM),
mean_Z = mean(Z),
sd_Z = sd(Z),
se_Z = std.error(Z)) %>%
mutate(mean_mean_PM = mean(mean_PM),
se_mean_PM = std.error(mean_PM),
PM_top = mean_mean_PM + se_mean_PM,
PM_lower = mean_mean_PM - se_mean_PM,
mean_mean_Z = mean(mean_Z),
se_mean_Z = std.error(mean_Z),
Z_top = mean_mean_Z + se_mean_Z,
Z_lower = mean_mean_Z - se_mean_Z) %>%
filter(time>=10)
plot_data %>% ggplot() +
geom_ribbon(aes(x = time, ymin = PM_lower, max = PM_top, fill = condition, group = condition)) +
geom_line(aes(time, mean_mean_PM, group = condition, color = condition), size = 1) +
scale_color_OkabeIto(order = c(2,1,3), labels = con_labels, name = NULL) +
scale_fill_OkabeIto(order = c(2,1,3), darken = -0.8, labels = con_labels, name = NULL) +
labs(x = 'Time (min)', y = 'PD-L1 membrane (molecules)') +
coord_cartesian(xlim = c(0,130)) +
scale_x_continuous(expand = c(0,0), breaks = c(10, 40, 70, 100, 130)) +
scale_y_continuous(expand = c(0,0)) +
theme(legend.position = "top") +
facet_wrap(~experiment, labeller = label_parsed) -> short_PM
plot_data %>% ggplot() +
geom_ribbon(aes(x = time, ymin = Z_lower, max = Z_top, fill = condition, group = condition)) +
geom_line(aes(time, mean_mean_Z, group = condition, color = condition), size = 1) +
scale_color_OkabeIto(order = c(2,1,3), labels = con_labels, name = NULL) +
scale_fill_OkabeIto(order = c(2,1,3), darken = -0.8, labels = con_labels, name = NULL) +
labs(x = 'Time (min)', y = 'ZEB1 (molecules)') +
coord_cartesian(xlim = c(0,130)) +
scale_x_continuous(expand = c(0,0), breaks = c(10, 40, 70, 100, 130)) +
scale_y_continuous(expand = c(0,0)) +
theme(legend.position = "top") +
facet_wrap(~experiment, labeller = label_parsed) -> short_Z
plot_data %>% ggplot() +
geom_ribbon(aes(x = mean_mean_Z, ymin = PM_lower, max = PM_top, fill = condition, group = condition)) +
geom_line(aes(mean_mean_Z, mean_mean_PM, group = condition, color = condition), size = 1) +
scale_color_OkabeIto(order = c(2,1,3), labels = con_labels, name = NULL) +
scale_fill_OkabeIto(order = c(2,1,3), darken = -0.8, labels = con_labels, name = NULL) +
labs(x = 'ZEB1 (molecules)', y = 'PD-L1 membrane (molecules)') +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0)) +
theme(legend.position = "top") +
facet_wrap(~experiment, labeller = label_parsed) -> short_PM_Z
```
### Time course EMT phenotypes
```{r}
data_short <- data_short %>% mutate(condition = factor(condition, levels = con_order, labels = con_labels))
plot_data_emt <- data_short %>%
filter(cell.type != 'T') %>%
group_by(experiment, condition, replicate, time, cell.type) %>%
tally() %>%
group_by(experiment, condition, time, cell.type) %>%
mutate(mean_n = mean(n),
se_n = std.error(n),
n_top = mean_n + se_n,
n_lower = mean_n - se_n) %>%
filter(time>=10)
plot_data_emt %>%
ggplot() +
geom_ribbon(aes(x = time, ymin = n_lower, max = n_top, fill = cell.type), alpha = 0.2) +
geom_line(aes(time, mean_n, color = cell.type), size = 1) +
facet_grid(rows = vars(condition), cols = vars(experiment), labeller = label_parsed) +
scale_x_continuous(expand = c(0,0), breaks = c(10, 40, 70, 100, 130)) +
scale_y_continuous(expand = expansion(mult = c(0,0.05))) +
expand_limits(y = 0) +
labs(color = "phenotype", fill = "phenotype") +
labs(x = 'Time (min)', y = 'number of cells') -> short_emt
```
### Combined
```{r}
plot_grid(short_image,
short_PM_Z + theme_cowplot(text_size) + theme(strip.placement = 'outside', strip.background = element_blank(),legend.position="top", legend.justification = "right"),
labels = c("A", "D"), label_size = label_size, ncol = 1, align = "vh", axis = "tlbr", scale = 0.9, rel_heights = c(3, 1)) -> A