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cell_composition_epi.R
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cell_composition_epi.R
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library(ggplot2)
library(ggpubr)
library(ggsci)
library(ComplexHeatmap)
library(RColorBrewer)
library(circlize)
library(dplyr)
# load meta data
meta = readRDS("all.clean.meta.rds")
# Epithelial cells
# disease distribution
# Ro/e, heatmap plot
# Extended Data Figure 6a
if(T){
# select data
data.sub = meta %>%
filter(major_cluster %in% c("Epithelial")) %>%
filter(stage == "adult") %>%
filter(tissue %in% c("smallInt","largeInt"))
data.sub$minor_cluster = data.sub$minor_cluster %>% droplevels()
# count
N.o <- table(data.sub[["minor_cluster"]],data.sub[["disease"]])
N.o = N.o[,colSums(N.o) > 0]
N.o = N.o[!grepl("Fetal", N.o %>% rownames()),]
N.o = N.o[!grepl("Pediatric", N.o %>% rownames()),]
# order disease
N.o = N.o[,c("Healthy","UC_inflamed","UC_non_inflamed")]
# filter cluster
N.o = N.o[which(rowSums(N.o > 20) > 0) %>% names(),]
N.o %>% rownames()
# order cluster
subset_order = seu$minor_cluster %>% levels
N.o = N.o[subset_order[subset_order %in% rownames(N.o)],]
# calculate expected value
res.chisq <- chisq.test(N.o)
# calculate Ro/e
R.oe <- (res.chisq$observed)/(res.chisq$expected)
mark = R.oe
mark[R.oe == 0] = '-'
mark[R.oe > 0 & R.oe < 0.5] = '+/-'
mark[R.oe >= 0.5 & R.oe <= 1] = '+'
mark[R.oe > 1 & R.oe <= 2] = '++'
mark[R.oe > 2] = '+++'
cell_function = function(j, i, x, y, width, height, fill){
grid.text(mark[i, j], x, y, gp = gpar(fontsize = 10))
}
getPalette = colorRampPalette(brewer.pal(11, "Blues")[1:6])
R.oe.v2 = R.oe
R.oe.v2[R.oe.v2 > 3] = 3
pdf("scIBD.cell_type_compare.between_disease.epi.pdf", width = 3, height = 4)
Heatmap(R.oe.v2,
column_title = "",
cluster_rows = FALSE, cluster_columns = FALSE,
#row_split = c(rep("1-Myeloid", myeloid$V1[myeloid$V1 %in% rownames(R.oe.v2)] %>% length ),
# rep("2-CD4+ T", cd4t$V1[cd4t$V1 %in% rownames(R.oe.v2)] %>% length),
# rep("3-CD8+ T", cd8t$V1[cd8t$V1 %in% rownames(R.oe.v2)] %>% length),
# rep("4-ILC", ilc$V1[ilc$V1 %in% rownames(R.oe.v2)] %>% length),
# rep("5-B/Plasma",B_Plasma$V1[B_Plasma$V1 %in% rownames(R.oe.v2)] %>% length)),
cell_fun = cell_function,
col = getPalette(5),
heatmap_legend_param = list(
title = "Ro/e",
break_dist = 1,
#col_fun = colorRamp2(c(0, 1, 2, 4, max(R.oe.v2)), getPalette(5)),
#at = c(0, 1, 2, 4, max(R.oe.v2)),
col_fun = colorRamp2(c(0, 0.5, 1, 2, 4), getPalette(5)),
at = c(0, 1, 2, 3, 4),
labels = c('0','0.5','1','3','max')
#labels = c('0','0.5','1','4',sprintf("%.1f", max(R.oe.v2)))
)
)
dev.off()
}
# Extended Data Figure 6d
# DUOX2+ epi
if(T){
tmp = meta %>% filter(stage == "adult") %>%
filter(major_cluster == "Epithelial") %>%
filter(disease %in% c("Healthy","UC_inflamed")) %>%
filter(!(tissue.sub %in% c("blood","lymph node"))) %>%
group_by(sample, minor_cluster) %>%
summarise(count = n()) %>% reshape2::dcast(sample ~ minor_cluster)
tmp[is.na(tmp)] = 0
rownames(tmp) = tmp$sample
tmp$sample = NULL
table(rowSums(tmp) > 100)
tmp.filter = tmp[rowSums(tmp) > 100, ]
tmp.filter = tmp.filter/rowSums(tmp.filter)
#View(tmp.filter)
tmp.meta = meta[,c("sample","disease")] %>% unique
tmp.meta = tmp.meta[!is.na(tmp.meta$sample),]
tmp.filter$sample = rownames(tmp.filter)
tmp.filter = left_join(tmp.filter, tmp.meta, by = "sample", kep)
cluster = "DUOX2+ epithelial"
tmp.filter.sub = tmp.filter[,c(cluster,"disease")]
colnames(tmp.filter.sub) = c("pct", "group")
# summary sample size in each group
tmp.filter %>% group_by(disease) %>% count() # Healthy 69, UC_inflamed 34
my_comparisons = list( c("UC_inflamed","Healthy") )
ggplot(tmp.filter.sub, aes(x=group, y = pct, fill = group, color = group)) +
labs(x = "",y = "Percentage of cells", title = "") +
geom_boxplot( outlier.size = -1, linewidth =0.5) +
geom_point( position = position_jitter(width = 0.1), size = 1) +
scale_fill_manual(values = alpha(paletteer::paletteer_d("ggsci::category10_d3")[c(1,2)], 0.6) ) +
scale_color_manual(values = paletteer::paletteer_d("ggsci::category10_d3")[c(1,2)] ) +
scale_x_discrete(labels = c("HC","UC")) +
theme_classic2() +
theme(
legend.position = "none",
#axis.text.x = element_text(family = "ArialMT", size = 16, color = "black", angle = 45, hjust = 1),
axis.text.x = element_text(family = "ArialMT", size = 16, color = "black"),
axis.text.y = element_text(family = "ArialMT", size = 16, color = 'black'),
axis.title.y = element_text(family = "ArialMT",size = 16, color = 'black'),
axis.title.x = element_text(family = "ArialMT",size = 16, color = 'black'),
legend.text = element_text(family = "ArialMT",size = 16, color ='black'),
legend.title = element_text(family = "ArialMT",size = 16, color ='black'),
strip.text = element_text(family = "ArialMT",face = "italic", size = 16, color ='black'),
strip.background = element_blank(),
plot.title = element_text(family = "ArialMT",size = 18, color ='black', hjust = 0.5)
) + stat_compare_means(comparisons = my_comparisons,
method = "t.test", label = "p.signif",
method.args = list(alternative = "greater"))
ggsave("DUOX2_epi.pct.boxplot.pdf", width = 1.8, height = 3)
pval = t.test(tmp.filter.sub[tmp.filter.sub$group == "Healthy",]$pct,
tmp.filter.sub[tmp.filter.sub$group == "UC_inflamed",]$pct)
pval = pval$p.value/2
pval
}
# M-like cells
if(T){
tmp = meta %>% filter(stage == "adult") %>%
filter(major_cluster == "Epithelial") %>%
filter(disease %in% c("Healthy","UC_inflamed")) %>%
filter(!(tissue.sub %in% c("blood","lymph node"))) %>%
group_by(sample, minor_cluster) %>%
summarise(count = n()) %>% reshape2::dcast(sample ~ minor_cluster)
tmp[is.na(tmp)] = 0
rownames(tmp) = tmp$sample
tmp$sample = NULL
table(rowSums(tmp) > 100)
tmp.filter = tmp[rowSums(tmp) > 100, ]
tmp.filter = tmp.filter/rowSums(tmp.filter)
#View(tmp.filter)
tmp.meta = meta[,c("sample","disease")] %>% unique
tmp.meta = tmp.meta[!is.na(tmp.meta$sample),]
tmp.filter$sample = rownames(tmp.filter)
tmp.filter = left_join(tmp.filter, tmp.meta, by = "sample", kep)
cluster = "M-like cell"
tmp.filter.sub = tmp.filter[,c(cluster,"disease")]
colnames(tmp.filter.sub) = c("pct", "group")
my_comparisons = list( c("UC_inflamed","Healthy") )
ggplot(tmp.filter.sub, aes(x=group, y = pct, fill = group, color = group)) +
labs(x = "",y = "Percentage of cells", title = "") +
geom_boxplot( outlier.size = -1, linewidth =0.5) +
geom_point( position = position_jitter(width = 0.1), size = 1) +
scale_fill_manual(values = alpha(paletteer::paletteer_d("ggsci::category10_d3")[c(1,2)], 0.6) ) +
scale_color_manual(values = paletteer::paletteer_d("ggsci::category10_d3")[c(1,2)] ) +
scale_x_discrete(labels = c("HC","UC")) +
theme_classic2() +
theme(
legend.position = "none",
#axis.text.x = element_text(family = "ArialMT", size = 16, color = "black", angle = 45, hjust = 1),
axis.text.x = element_text(family = "ArialMT", size = 16, color = "black"),
axis.text.y = element_text(family = "ArialMT", size = 16, color = 'black'),
axis.title.y = element_text(family = "ArialMT",size = 16, color = 'black'),
axis.title.x = element_text(family = "ArialMT",size = 16, color = 'black'),
legend.text = element_text(family = "ArialMT",size = 16, color ='black'),
legend.title = element_text(family = "ArialMT",size = 16, color ='black'),
strip.text = element_text(family = "ArialMT",face = "italic", size = 16, color ='black'),
strip.background = element_blank(),
plot.title = element_text(family = "ArialMT",size = 18, color ='black', hjust = 0.5)
) + stat_compare_means(comparisons = my_comparisons,
method = "t.test", label = "p.signif",
method.args = list(alternative = "greater"))
#ggsave("M_cell.pct.boxplot.pdf", width = 1.8, height = 3)
pval = t.test(tmp.filter.sub[tmp.filter.sub$group == "Healthy",]$pct,
tmp.filter.sub[tmp.filter.sub$group == "UC_inflamed",]$pct)
pval = pval$p.value/2
pval
}