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Figure3.R
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#---- Figure 3 ----#
# Library and data loading
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
require(Seurat)
require(ggpubr)
sample_info = data.frame(fread('obj/clinical_info_09.csv'))
load('/raid1/cyq/tumor_t_obj.rda')
load('/raid1/cyq/t_obj.rda')
lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
## UMAP
ggplot() +
geom_point(aes(x=UMAP1, y=UMAP2, color=annotation), size=.01, alpha=1) +
scale_color_manual(values = col_yy, name='T sub-types') +
# geom_text(aes(x=m1, y=m2, label=anno_brief), data = text_anno, size=5) +
theme_pubr() +
theme(plot.title = element_text(size=22),
strip.text = element_text(size = 18),
strip.background =element_rect(fill='white', color = 'black', size = .1),
axis.text.x = element_text(size=18, angle = 0, hjust = .5, vjust=0),
axis.text.y = element_text(size=15),
axis.title = element_text(size = 18),
axis.line = element_line(size = .8),
legend.position = 'right',
legend.key.size = unit(1.4,'line'),
legend.text = element_text(size = 15),
legend.title = element_text(size = 15, face = 'bold'),
plot.margin = margin(b=0, l=10, t=10, r=10)) +
guides(colour = guide_legend(override.aes = list(size=5, alpha = 1)))
ggplot(aes(x=UMAP1, y=UMAP2)) +
facet_grid(cols=vars(factor(Stage, levels=c('Pre','On','Post'))),
rows=vars(Response)) +
stat_density_2d(aes(fill = stat(piece)),
geom = "polygon",
n = 30,
bins = 10,
contour = T,
contour_var = "ndensity", adjust = 1/4
) +
scale_fill_viridis(option = "A") +
theme_pubr() +
theme(plot.title = element_text(size=22),
strip.text = element_text(size = 18),
strip.background =element_rect(fill=NA, color = NA, size = .1),
axis.text = element_blank(),
axis.title = element_text(size = 18),
axis.line = element_line(size = .8),
legend.position = 'right',
legend.key.size = unit(1.4,'line'),
legend.text = element_text(size = 15),
legend.title = element_text(size = 15, face = 'bold'),
plot.margin = margin(b=0, l=10, t=10, r=10))
## Proportion
global_df = [email protected] %>%
group_by(Tissue, Patient, Stage) %>% summarise(n1 = n())
sub_df = [email protected] %>%
group_by(Tissue, annotation, Patient, Stage) %>%
summarise(n = n())
sub_freq_df =
mutate(sample_stage = paste(Patient, Stage, Tissue, sep = '-')) %>%
tidyr::expand(sample_stage,annotation) %>%
separate(sample_stage, into = c('Patient','Stage','Tissue'), sep = '-') %>%
left_join(sub_df, by = c('Tissue', 'Patient', 'Stage','annotation')) %>%
left_join(global_df, by = c('Tissue', 'Patient', 'Stage')) %>%
replace_na(list(n=0, n1=0)) %>%
mutate(freq = n/n1) %>%
left_join(sample_info, by = 'Patient')
sub_freq_df$Stage = factor(sub_freq_df$Stage, levels = c('Pre','On','Post'))
# head(sub_freq_df)
# dim(sub_freq_df)
### Scatter plot
cls = 'c23_CD8_Tex_LAYN'
input = sub_freq_df %>%
filter(Tissue == 'Tumor') %>%
select(-c(n1,n)) %>% distinct() %>%
pivot_wider(names_from = Stage, values_from = freq) %>%
replace_na(list(On=0, Post=0, Pre=0)) %>%
filter(annotation %in% cls)
lm = lm(Pre ~ SIZE_CHANGE, data = input)
print(paste0('R^2 = ',format(summary(lm)$r.squared, digits = 2)))
print(paste0('p.value = ',format(lmp(lm), digits = 2)))
input %>%
ggplot(aes(x = Pre, y = SIZE_CHANGE)) +
geom_smooth(method='lm', col='black', lwd=.5) +
geom_point(aes(col = Response, shape = MSI), size = 2, stroke=2) +
scale_color_manual(values = c('#5778A2','#DCB44B','#8A281E')) +
scale_shape_manual(values = c(16,17,1,4),name='MSI') +
theme_pubr() + labs(title = cls) +
geom_text(x = .05, y = 1.1, label = paste0('R = ',format(sqrt(summary(lm)$r.squared), digits = 2))) +
geom_text(x = .045, y = 1.05, label = paste0('P = ',format(lmp(lm), digits = 2))) +
geom_text(aes(x=Pre, y=SIZE_CHANGE-0.08, label=Patient), data=input) +
theme(legend.position = 'right',
legend.key.size = unit(1.4,'line'),
legend.text = element_text(size = 13),
legend.title = element_text(size = 15, face = 'bold'),
axis.title = element_text(size=15),
plot.margin = margin(r = 10, t=10)) +
xlab('Frequency at baseline') +
ylab('Change of tumor size (%)')
### Line chart
input = sub_freq_df %>%
filter(Tissue == 'Tumor') %>%
filter(annotation==cls)
input %>%
ggplot(aes(x=Stage, y=freq, color=Response, shape=MSI, group=Patient)) +
geom_point(aes(shape=MSI), size=2, stroke=2) +
geom_line(aes(linewidth=MSI), alpha=.5) +
scale_color_manual(values = get_palette('nejm', 3)[c(2,3,1)], name='Response') +
scale_shape_manual(values = c(16,17,1,4), name='MSI state') +
scale_linewidth_discrete(range = c(.5,1.5), name='MSI state') +
xlab('Sampling stage') + ylab(paste0('Proportion')) +
facet_wrap(~Response) +
theme_pubr() +
theme(strip.text.x = element_text(size = 18),
strip.background =element_rect(fill=NA, color = NA, size = 1),
axis.text.x = element_text(size=15, angle = 0, hjust = .5),
axis.text.y = element_text(size=15),
axis.title = element_text(size = 18),
axis.line = element_line(size = .8),
legend.position = 'right',
legend.key.size = unit(1.4,'line'),
legend.text = element_text(size = 13),
legend.title = element_text(size = 15, face = 'bold'),
plot.margin = margin(b = 10, t=10, l=10, r=10))
## Expansion
meta_pre = [email protected] %>% filter(Tissue=='Tumor', Stage=='Pre')
in.dat = meta_pre[,c('cell_name', 'clone.id', 'clone.status', 'Patient', 'annotation', 'Tissue')]
colnames(in.dat) = c('Cell_Name', 'clone.id', 'clone.status', 'patient', 'majorCluster', 'loc')
out_pre = Startrac.run(in.dat, proj="CRC",verbose=F) %>% suppressWarnings()
expa_pre = [email protected] %>% filter(aid != 'CRC') %>% select('aid','majorCluster','expa') %>%
rename(Patient=aid, annotation=majorCluster, exp_pre=expa) %>%
mutate_all(~replace(., is.nan(.), 0))
expa_df = expa_pre %>% full_join(expa_on, by=c('Patient','annotation')) %>%
full_join(expa_post, by=c('Patient','annotation')) %>%
left_join(sample_info, by='Patient')
# head(expa_df)
### scatter plot
input = expa_df %>% rename(Pre=exp_pre, On=exp_on, Post=exp_post) %>%
filter(annotation == 'c23_CD8_Tex_LAYN')
lm = lm(Pre ~ SIZE_CHANGE, data = input)
# print(paste0('R^2 = ',format(summary(lm)$r.squared, digits = 2)))
# print(paste0('p.value = ',format(lmp(lm), digits = 2)))
input %>%
ggplot(aes(x = Pre, y = SIZE_CHANGE)) +
geom_smooth(method='lm', col='#525252') +
geom_point(aes(col = Response, shape = MSI), size = 2, stroke = 2) +
scale_color_manual(values = c('#5778A2','#DCB44B','#8A281E')) +
scale_shape_manual(values = c(16,17,1,4), name='MSI') +
geom_text(x = .032, y = 1.3, label = paste0('R = ',format(sqrt(summary(lm)$r.squared), digits = 2))) +
geom_text(x = .036, y = 1.2, label = paste0('P = ',format(lmp(lm), digits = 2))) +
geom_text(aes(x=Pre, y=SIZE_CHANGE-0.08, label=Patient), data=input) +
theme_pubr() +
theme(legend.position = 'right',
legend.key.size = unit(1.4,'line'),
legend.text = element_text(size = 13),
legend.title = element_text(size = 15, face = 'bold'),
axis.title = element_text(size=15),
plot.margin = margin(l = 10, b=10, t=10)) +
labs(title = paste0('c23_CD8_Tex_LAYN at baseline')) +
ylab('Tumor Regression') +
xlab('STARTRAC-expa')
### line chart
input=expa_df %>%
rename(Pre=exp_pre, On=exp_on, Post=exp_post) %>%
pivot_longer(cols = c('Pre','On','Post'),
names_to = "Stage",
values_to = "exp",
values_drop_na = TRUE) %>%
filter(annotation=='c23_CD8_Tex_LAYN')
input %>%
ggplot(aes(x=factor(Stage, levels=c('Pre','On','Post')), y=exp,
group=Patient, col=Response)) +
geom_point(aes(shape=MSI), size=2, stroke=2) +
geom_line(aes(linewidth=MSI), alpha=.5) +
scale_color_manual(values = get_palette('nejm', 3)[c(2,3,1)], name='Response') +
scale_shape_manual(values = c(16,17,1,4), name='MSI state') +
scale_linewidth_discrete(range = c(.5,1.5), name='MSI state') +
xlab('') + ylab(paste0('STARTRAC-expa')) +
facet_wrap(~Response) +
theme_pubr() +
theme(strip.text.x = element_text(size = 18),
strip.background =element_rect(fill=NA, color = NA, size = 1),
axis.text.x = element_text(size=15, angle = 0, hjust = .5),
axis.text.y = element_text(size=15),
axis.title = element_text(size = 18),
axis.line = element_line(size = .8),
legend.position = 'right',
legend.key.size = unit(1.4,'line'),
legend.text = element_text(size = 13),
legend.title = element_text(size = 15, face = 'bold'),
plot.margin = margin(b = 10, t=10, l=10, r=10)) +
guides(colour = guide_legend(override.aes = list(size=3, alpha = 1),ncol=1),
shape=guide_legend(ncol=1))