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MakeFigures.Rmd
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MakeFigures.Rmd
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---
title: "Make Figures"
output: html_notebook
---
# Setup
Load libraries
```{r}
library(Seurat)
library(patchwork)
library(ggrepel)
gene_names = read.table("../../../10x-runs/ricardo/3324STDY7421492/outs/filtered_gene_bc_matrices/mm10/genes.tsv")
gene_names_human = read.csv("saved_data/gene_names_human_GRCh38_93.csv",
header = T)
```
Define palettes
```{r}
cl_allCells_pal = c("#d10c00","#e5acac","#ef8753","#217dff","#6db9ff","#599958","#7c7b7b","#c6c6c6")
sort_pal = c("#d10c00", "#e5acac", "#ffb432", "#f9d089", "#3264fc", "#91acff", "#28c2ff", "#a3e4ff", "#8851ff", "#b08cff")
de_colon_nlt_pal = c(sort_pal[1:2], cl_allCells_pal[4:5], "grey58")
de_skin_nlt_pal = c(sort_pal[3:4], cl_allCells_pal[4:5], "grey58")
cl_treg_pal = c("Treg NLT-like" = "#F27676", "eTreg" = "#a96bce", "cTreg" = "#217dff",
"Treg Stat1" = "#455680", "Treg NLT" = "#d10c00", "Treg suppressive" = "#f97800",
"Treg LT-like" = "#66a4ff", "Tmem NLT" = "#e5acac", "Stress/low qual" = "#c6c6c6")
cl_tmem_pal = c("Tmem Tfh" = "#a96bce", "Tmem LT" = "#217dff",
"Treg Stat1" = "#455680", "Treg suppressive" = "#f97800",
"Tmem LT-like" = "#66a4ff", "Tmem Th1" = RColorBrewer::brewer.pal(9, "Set1")[3],
"Tmem Th17" = RColorBrewer::brewer.pal(8, "Set2")[6] , "Tmem Th2" = RColorBrewer::brewer.pal(8, "Accent")[7],
"Tmem cycling" = "#ff0000",
"Tmem lymphoid resident" = "#455680", "Tmem lymphoid egressing" = "#F27676",
"Tmem Cxcl10" = "#dd0881",
"Treg NLT" = "#d10c00", "Tmem NLT" = "#e5acac", "Stress/low qual" = "#c6c6c6")
cl_treg_pal_alt = cl_treg_pal
names(cl_treg_pal_alt) = gsub(" ", "\n", names(cl_treg_pal_alt))
names(cl_treg_pal_alt)[5] = "Treg NLT"
col_cc = c("grey45", "#ff661a", "#ff0000")
col_mel_gene_groups = c(cl_allCells_pal[c(1,4)], "#ffad33", "#000000")
colours_mouse = RColorBrewer::brewer.pal(12, "Paired")[c(5,6, 1,2, 9,10)]
colours_mel = rev(RColorBrewer::brewer.pal(11, "RdYlBu")[c(1,11)])
colours_h_ind = c(RColorBrewer::brewer.pal(11, "RdYlBu")[c(9,10)], RColorBrewer::brewer.pal(11, "RdYlGn")[2:4])
colours_h_tc = c(RColorBrewer::brewer.pal(11, "PuOr")[8:10], RColorBrewer::brewer.pal(9, "Greens")[4], RColorBrewer::brewer.pal(9, "BuGn")[6], RColorBrewer::brewer.pal(9, "Greens")[9], RColorBrewer::brewer.pal(9, "YlOrRd")[c(3,4)], RColorBrewer::brewer.pal(12, "Paired")[6])
cols_m_h = c("Shared" = "#E0B814", "Human" = "#145BB3",
"Mouse" = "#28B54A", "None" = "#c6c6c6")
colours_clon = unique(c(RColorBrewer::brewer.pal(8, "Set1"), RColorBrewer::brewer.pal(8, "Dark2"),
RColorBrewer::brewer.pal(12, "Set3"), RColorBrewer::brewer.pal(8, "Set2"),
RColorBrewer::brewer.pal(8, "Accent")))
```
Define themes
```{r}
tsne_theme = theme(aspect.ratio = 1,
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
axis.title.x = element_text(size = 6, margin = margin(0,0,0,0)),
axis.title.y = element_text(size = 6, margin = margin(0,0,0,0)),
plot.background = element_blank(),
panel.background = element_blank(),
legend.key = element_blank(),
legend.key.size = unit(0.85, 'lines'),
legend.justification = "center",
legend.background = element_blank(),
legend.position = "right", legend.title.align = 0,
legend.title = element_text(face = "bold", size = 6),
legend.text = element_text(size = 5.5),
legend.box.spacing = unit(0.02, "lines"),
panel.border = element_rect(colour = "black",fill = "transparent",
linetype = "solid", size = 0.5))
de_theme = theme_bw()+
theme(aspect.ratio = 1,
legend.title = element_blank(),
legend.spacing = unit(0.04, "lines"),
legend.background = element_blank(),
plot.title = element_text(size = 11.5, face = "bold"),
legend.box.spacing = unit(0.02, "lines"),
legend.text = element_text(size = 6),
axis.text = element_text(colour = "black", size = 7),
axis.title = element_text(face = "bold", size = 8))
theme_bar = theme_classic()+
theme(aspect.ratio = 1/1.5,
axis.text.x = element_text(colour = "black", size = 5.5,
face = "bold", angle = 33,
vjust = 1, hjust = 1),
axis.text.y = element_text(colour = "black", size = 4.7),
axis.title.x = element_blank(),
axis.title.y = element_text(size = 5.5),
legend.key = element_blank(),
legend.key.size = unit(0.4, 'lines'),
legend.justification = "center",
legend.background = element_blank(),
legend.position = "right", legend.title.align = 0,
legend.title = element_text(face = "bold", size = 5.5),
legend.text = element_text(size = 5.5),
legend.box.spacing = unit(0.0025, "lines"),
plot.margin = unit(c(0,0.07,0,0.07),"lines"))
```
# Figure 1
Load data
```{r}
load("./saved_data/10X/all_data_cl.RData")
load("plots/10X/DE/de_lt_nlt_all_10X.RData")
load("plots/10X/DE/de_treg_tmem_all_10X.RData")
load("plots/SS2/DE/de_lt_nlt_all_SS2.RData")
load("plots/SS2/DE/de_treg_tmem_all_SS2.RData")
load("saved_data/SS2/sr_list_SS2.RData")
load("plots/SS2/DE/de_lt_nlt_all_SS2.RData")
load("plots/SS2/DE/de_treg_tmem_all_SS2.RData")
```
## Make plots
t-SNE with all 10X
```{r, fig.width=5, fig.height=2.75}
plot_df = cbind(all_data_cl@dr$tsne@cell.embeddings, all_data_cl@meta.data)
plot_df$isT = ifelse(plot_df$cl_ct, "Treg",
ifelse(grepl("Tmem", plot_df$cl_ct), "Tmem", "Other"))
plot_df$cell_type_sort_mod = as.character(plot_df$cell_type_sort)
plot_df$cell_type_sort_mod[plot_df$cl_ct %in% c("NKT", "Undefined", "MitoStress")] = "Contamination"
plot_df$cell_type_sort_mod = gsub("_", " ", plot_df$cell_type_sort_mod)
plot_df$cell_type_sort_mod = factor(plot_df$cell_type_sort_mod, levels = c("Treg colon","Tmem colon",
"Treg skin","Tmem skin",
"Treg mLN","Tmem mLN",
"Treg bLN","Tmem bLN",
"Treg spleen","Tmem spleen",
"Contamination"))
plot_df = plot_df[sample(1:nrow(plot_df), nrow(plot_df), replace = F),]
tsne_1b = ggplot(plot_df)+
geom_point(mapping = aes(x = tSNE_1, y = tSNE_2, colour = cell_type_sort_mod),
size = 0.2, shape = 19)+
scale_colour_manual(values = c(sort_pal, "grey70"))+
guides(colour = guide_legend(title = "Cell Type and Tissue", ncol = 2,byrow = T,
title.position = "top",
override.aes = list(size = 1.1)))+
tsne_theme
plot_df$cl_allCells = as.character(plot_df$cl_allCells)
plot_df$cl_allCells = gsub("_", " ", plot_df$cl_allCells)
plot_df$cl_allCells = gsub("MitoStress", "Stress/Mt", plot_df$cl_allCells)
plot_df$cl_allCells = factor(plot_df$cl_allCells, levels = c("Colon Treg", "Colon Tmem",
"Skin", "LT Treg", "LT Tmem",
"NKT", "Stress/Mt", "Undefined"))
tsne_supsort = ggplot(plot_df)+
geom_point(mapping = aes(x = tSNE_1, y = tSNE_2, colour = cl_allCells),
size = 0.2, shape = 19)+
scale_colour_manual(values = cl_allCells_pal)+
guides(colour = guide_legend(title = "Clusters", title.position = "top",
override.aes = list(size = 1.1)))+
tsne_theme
tsne_fig1_10X = list(tsne_1b = tsne_1b, tsne_supsort = tsne_supsort)
save(tsne_fig1_10X, file = "FINAL_PLOTS/plot_repository/tsne_fig1_10X.RData")
```
tSNE without contaminant cells (re-run it here)
```{r}
cells = rownames(all_data_cl@meta.data)[!all_data_cl@meta.data$cl_allCells %in% c("NKT","Undefined","MitoStress")]
sub_alldata = SubsetData(all_data_cl, cells.use = cells)
sub_alldata <- NormalizeData(object = sub_alldata, normalization.method = "LogNormalize",
scale.factor = 10000, display.progress = F)
sub_alldata <- Seurat::ScaleData(object = sub_alldata, model.use = "negbinom",
do.center = T, do.scale = F, use.umi = T)
sub_alldata = Seurat::RunPCA(sub_alldata, pcs.compute = 50, pcs.print = NULL,
genes.print = 0, pc.genes = rownames(sub_alldata@data))
PCElbowPlot(object = sub_alldata, num.pc = 50)
sub_alldata = Seurat::RunTSNE(sub_alldata, dims.use = 1:30, seed.use = 1)
plot_df = cbind(sub_alldata@dr$tsne@cell.embeddings, sub_alldata@meta.data)
plot_df$isT = ifelse(plot_df$cl_ct, "Treg",
ifelse(grepl("Tmem", plot_df$cl_ct), "Tmem", "Other"))
plot_df$cell_type_sort = as.character(plot_df$cell_type_sort)
plot_df$cell_type_sort = gsub("_", " ", plot_df$cell_type_sort)
plot_df$cell_type_sort = factor(plot_df$cell_type_sort, levels = c("Treg colon",
"Tmem colon",
"Treg skin",
"Tmem skin",
"Treg mLN",
"Tmem mLN",
"Treg bLN",
"Tmem bLN",
"Treg spleen",
"Tmem spleen"))
plot_df = plot_df[sample(1:nrow(plot_df), nrow(plot_df), replace = F),]
tsne_1b_nocont = ggplot(plot_df)+
geom_point(mapping = aes(x = tSNE_1, y = tSNE_2, colour = cell_type_sort),
size = 0.2, shape = 19)+
scale_colour_manual(values = c(sort_pal))+
guides(colour = guide_legend(title = "Cell Type and Tissue", ncol = 1, byrow = T,
title.position = "top",
override.aes = list(size = 1.1)))+
tsne_theme
save(tsne_1b_nocont, file = "FINAL_PLOTS/plot_repository/tsne_1b_nocont.RData")
```
tSNE SS2
```{r}
ss2_list_sup = list()
for(n in c("mouse_colon", "mouse_skin")){
plot_df = cbind(sr_list[[n]]@dr$tsne@cell.embeddings, sr_list[[n]]@meta.data)
plot_df$tissue_cell = gsub(".", " ", as.character(plot_df$tissue_cell), fixed = T)
plot_df$tissue_cell = factor(plot_df$tissue_cell, levels = c("colon Tmem", "colon Treg",
"skin Tmem", "skin Treg",
"LN Tmem", "LN Treg",
"spleen Tmem", "spleen Treg"))
ss2_list_sup[[n]] = ggplot(plot_df)+
geom_point(mapping = aes(x = tSNE_1, y = tSNE_2, colour = tissue_cell),
size = 0.25, shape = 19)+
scale_colour_manual(values = colours_mouse)+
guides(colour = guide_legend(title = "Cell Type and Tissue", ncol = 1, byrow = T,
title.position = "top",
override.aes = list(size = 1.1)))+
tsne_theme
}
save(ss2_list_sup, file = "FINAL_PLOTS/plot_repository/ss2_list_sup.RData")
```
DE LT-NLT and Treg-Tmem - 10X
```{r, fig.width=3.75, fig.height=3.7}
# FUNCTIONS
processDEresults = function(tiss, ct, thr = 0.25){
tiss$pct_diff = tiss$pct.2-tiss$pct.1
tiss$is_de = tiss$p_val_adj<=0.05 & abs(tiss$avg_logFC)>=thr
ct$pct_diff = ct$pct.2-ct$pct.1
ct$is_de = ct$p_val_adj<=0.05 & abs(ct$avg_logFC)>=thr
res = merge(tiss[,c(2,4,8,9)], ct[,c(2,4,8,9)], by = 1)
colnames(res) = c("Gene", "logFC_NLT.vs.LT", "pDiff_NLT.vs.LT", "isDE_NLT.vs.LT",
"logFC_Treg.vs.Tmem", "pDiff_Treg.vs.Tmem", "isDE_Treg.vs.Tmem")
res$Gene = as.character(res$Gene)
res$col = ifelse(res$logFC_NLT.vs.LT>thr &
res$logFC_Treg.vs.Tmem>thr,
"NLT Treg",
ifelse(res$logFC_NLT.vs.LT<(-thr) &
res$logFC_Treg.vs.Tmem>thr,
"LT Treg",
ifelse(res$logFC_NLT.vs.LT>thr &
res$logFC_Treg.vs.Tmem<(-thr),
"NLT Tmem",
ifelse(res$logFC_NLT.vs.LT<(-thr) &
res$logFC_Treg.vs.Tmem<(-thr),
"LT Tmem", "Other"))))
res$col[!res$isDE_NLT.vs.LT | !res$isDE_Treg.vs.Tmem] = "Other"
res$col = factor(res$col, levels = c("NLT Treg","NLT Tmem","LT Treg",
"LT Tmem","Other"))
res$shap = ifelse((res$logFC_NLT.vs.LT<thr & res$logFC_NLT.vs.LT>(-thr)) |
!res$isDE_NLT.vs.LT, "DE cell type",
ifelse((res$logFC_Treg.vs.Tmem<thr & res$logFC_Treg.vs.Tmem>(-thr)) |
!res$isDE_Treg.vs.Tmem, "DE tissue",
"DE both"))
return(res)
}
plotDE = function(de_tab, col_pal, title_plt, text_df,
xlim_plt=NULL, ylim_plt=NULL){
de_tab = de_tab[order(de_tab$col, decreasing = T),]
plt = ggplot(de_tab, aes(x = logFC_NLT.vs.LT, y = logFC_Treg.vs.Tmem,
colour = col, shape = shap))+
geom_point(size = 0.8)+
ggrepel::geom_text_repel(data = text_df, segment.colour = "black",
colour = "black", min.segment.length = 0,
fontface = "bold", size = 2.35, alpha = 1,
mapping = aes(x = logFC_NLT.vs.LT,
y = logFC_Treg.vs.Tmem,
label = Gene))+
scale_colour_manual(values = col_pal)+
scale_shape_manual(values = c(19, 4, 1))+
guides(colour = guide_legend(override.aes = list(size = 1.1)),
shape = guide_legend(override.aes = list(size = 1.1)))+
labs(x = "Average logFC NLT vs LT", y = "Average logFC Treg vs Tmem",
colour = "", shape = "", title = title_plt)+
de_theme
if(!is.null(xlim_plt) & !is.null(ylim_plt)){
plt = plt + coord_cartesian(xlim = xlim_plt, ylim = ylim_plt)
}
return(plt)
}
# COLON
colon_all = processDEresults(de_lt_nlt_all$colon_mLN_spleen,
de_treg_tmem_all$colon_mLN_spleen, thr = 0.25)
genes_top = c("Il10", "Rora", "Ramp3", "Tcf7", "Sell", "Ikzf2","Cxcr6","Bcl2",
"Gzmb","Slamf6", "Gpr83", "Nrp1","Ccr2","Tnfaip3","Tnfrsf9","Tnfrsf4",
"Itga4", "Cd83","Vps37b")
text_df = colon_all[colon_all$Gene %in% genes_top,]
colon_de = plotDE(colon_all, de_colon_nlt_pal, "Colon vs LT", text_df,
xlim = c(-2.1,3.1), ylim = c(-2,1.7))
# SKIN
skin_all = processDEresults(de_lt_nlt_all$skin_bLN_spleen,
de_treg_tmem_all$skin_bLN_spleen)
genes_top = c("Il10", "Rora", "Ramp3", "Tcf7", "Sell","Ikzf2", "Cxcr6","Bcl2","Pim1",
"Gzmb", "Pfkp", "Slamf6", "Gpr83", "Nrp1","Ccr2","Tnfaip3","Odc1",
"Tnfrsf9","Tnfrsf4","Itga4", "Cd83", "Vps37b", "Id2", "Il4ra", "Ly6c1")
text_df = skin_all[skin_all$Gene %in% genes_top,]
skin_de = plotDE(skin_all, de_skin_nlt_pal, "Skin vs LT", text_df,
xlim = c(-2.1,3.1), ylim = c(-2,1.7))
de_fig1 = list(colon_de = colon_de, skin_de = skin_de)
save(de_fig1, file = "FINAL_PLOTS/plot_repository/de_fig1.RData")
de_summary_10x = list(colon_all = colon_all, skin_all = skin_all)
save(de_summary_10x, file = "plots/10X/DE/de_summary_10x.RData")
```
DE LT-NLT and Treg-Tmem - SS2
```{r, fig.width=3.75, fig.height=3.7}
# COLON
colon_all_ss2 = processDEresults(de_lt_nlt_all_ss2$mouse_colon,
de_treg_tmem_all_ss2$mouse_colon)
genes_top = c("Il10", "Rora", "Ramp3", "Tcf7", "Sell", "Ikzf2","Cxcr6","Bcl2",
"Gzmb","Slamf6", "Gpr83", "Nrp1","Ccr2","Tnfaip3","Tnfrsf9","Tnfrsf4",
"Itga4", "Cd83","Vps37b")
text_df = colon_all_ss2[colon_all_ss2$Gene %in% genes_top,]
colon_de_ss2 = plotDE(colon_all_ss2, de_colon_nlt_pal, "Colon vs LT", text_df,
xlim = c(-2.1,3.2), ylim = c(-1.5, 2.5))
# SKIN
skin_all_ss2 = processDEresults(de_lt_nlt_all_ss2$mouse_skin,
de_treg_tmem_all_ss2$mouse_skin)
genes_top = c("Il10", "Rora", "Ramp3", "Tcf7", "Sell", "Ikzf2", "Cxcr6","Bcl2","Gzmb", "Pfkp",
"Slamf6", "Gpr83", "Nrp1", "Ccr2","Tnfaip3","Tnfrsf9","Tnfrsf4","Itga4",
"Cd83", "Vps37b", "Id2", "Il4ra", "Ly6c1", "Pim1", "Odc1")
text_df = skin_all_ss2[skin_all_ss2$Gene %in% genes_top,]
skin_de_ss2 = plotDE(skin_all_ss2, de_skin_nlt_pal, "Skin vs LT", text_df,
xlim = c(-2, 3.7), ylim = c(-2.2, 2))
de_supfig1_SS2 = list(colon_de_ss2 = colon_de_ss2, skin_de_ss2 = skin_de_ss2)
save(de_supfig1_SS2, file = "FINAL_PLOTS/plot_repository/de_supfig1_SS2.RData")
```
Confusion Matrix
```{r, fig.width=4, fig.height=3.5}
plot_df = cbind(all_data_cl@dr$tsne@cell.embeddings, all_data_cl@meta.data)
plot_df$cl_allCells = as.character(plot_df$cl_allCells)
plot_df$cl_allCells = gsub("_", " ", plot_df$cl_allCells)
plot_df$cl_allCells = gsub("MitoStress", "Stress/Mt", plot_df$cl_allCells)
plot_df$cl_allCells = factor(plot_df$cl_allCells, levels = c("Colon Treg", "Colon Tmem",
"Skin", "LT Treg", "LT Tmem",
"NKT", "Stress/Mt", "Undefined"))
plot_df$cell_type_sort = as.character(plot_df$cell_type_sort)
plot_df$cell_type_sort = gsub("_", " ", plot_df$cell_type_sort)
plot_df$cell_type_sort = factor(plot_df$cell_type_sort, levels = c("Treg colon","Tmem colon",
"Treg skin","Tmem skin",
"Treg mLN","Tmem mLN",
"Treg bLN","Tmem bLN",
"Treg spleen","Tmem spleen"))
df_cm = data.frame(table(plot_df$cell_type_sort, plot_df$cl_allCells))
colnames(df_cm) = c("Sorted", "Clustered", "n")
df_cm$Sorted = factor(df_cm$Sorted, levels = rev(levels(df_cm$Sorted)))
for(i in unique(df_cm$Sorted)){
df_cm$frac[df_cm$Sorted==i] = df_cm$n[df_cm$Sorted==i]/sum(df_cm$n[df_cm$Sorted==i])*100
}
df_cm$white = ifelse(df_cm$frac>75, "white", "black")
conf_mat = ggplot(df_cm, aes(x = Clustered, y = Sorted, fill = frac, label = n))+
geom_tile()+ geom_text(mapping = aes(colour = white), size = 1.6, show.legend = F)+
scale_x_discrete(position = "top")+
scale_fill_gradientn(colors = RColorBrewer::brewer.pal(n = 9,
name = "PuBuGn")[-c(9,8)])+
guides(fill = guide_colourbar(title = "% of Sorted", barwidth = unit("0.4", "lines")))+
scale_colour_manual(values = c("black", "grey95"))+
theme_classic()+
theme(axis.text = element_text(colour = "black", size = 5.8),
axis.text.x.top = element_text(angle = 33, hjust = 0, vjust = 0,
colour = "black"),
axis.title = element_text(face = "bold", size = 6.2),
legend.title = element_text(size = 6.2),
legend.text = element_text(size = 5),
axis.ticks = element_blank(),
axis.line = element_blank(),
legend.background = element_blank(),
legend.box.spacing = unit(0.02, "lines"),
aspect.ratio = 1/1.15)
save(conf_mat, file = "FINAL_PLOTS/plot_repository/conf_mat_supp.RData")
```
## Assemble Figures
Figure 1
```{r, fig.width=7.2, fig.height=5.8}
load("FINAL_PLOTS/plot_repository/tsne_fig1_10X.RData")
load("FINAL_PLOTS/plot_repository/de_fig1.RData")
load("FINAL_PLOTS/plot_repository/tsne_1b_nocont.RData")
fig_top = cowplot::plot_grid(NULL, tsne_1b_nocont,
ncol = 2, rel_widths = c(1.05,1), labels = c("A","B"))
fig_bot = cowplot::plot_grid(NULL, de_fig1$colon_de, NULL, de_fig1$skin_de, NULL,
ncol = 5, rel_widths = c(0.04, 1, 0.05, 1, 0.04))
fig1 = cowplot::plot_grid(fig_top, fig_bot, nrow = 2, labels = c("","C"))
ggsave("FINAL_PLOTS/Figures/Figure01.pdf", fig1,
device = "pdf", height = 5.8, width = 7.3, paper = "a4", useDingbats = F)
```
Supplementary Figure 1 (add sorting later)
```{r, fig.width=7.6, fig.height=3}
load("FINAL_PLOTS/plot_repository/qc_10X_all.RData")
load("FINAL_PLOTS/plot_repository/SS2_qc_violin_list.RData")
load("FINAL_PLOTS/plot_repository/tsne_fig1_10X.RData")
load("FINAL_PLOTS/plot_repository/conf_mat_supp.RData")
fig_mid = plot_grid(tsne_fig1_10X$tsne_supsort, conf_mat, ncol = 2, labels = c("C", "D"))
fig_bot = plot_grid(qc_violin_list$mouse_colon, qc_violin_list$mouse_skin, ncol = 2, labels = c("E", "F"))
fig_scrna = plot_grid(gene_umi_hex_sample, fig_mid, fig_bot, nrow = 3, labels = "B", rel_heights = c(1, 0.62, 1))
ggsave("FINAL_PLOTS/Figures/SupplementaryFigure01.pdf", fig_scrna,
device = "pdf", height = 7.6, width = 7.6, paper = "a4", useDingbats = F)
```
Supplementary Figure 2
```{r}
load("FINAL_PLOTS/plot_repository/ss2_list_sup.RData")
load("FINAL_PLOTS/plot_repository/de_supfig1_SS2.RData")
fig_top = cowplot::plot_grid(ss2_list_sup$mouse_colon, ss2_list_sup$mouse_skin,
ncol = 2, labels = c("A","B"))
fig_bot = cowplot::plot_grid(NULL, de_supfig1_SS2$colon_de_ss2, NULL, de_supfig1_SS2$skin_de_ss2, NULL,
ncol = 5, rel_widths = c(0.04, 1, 0.05, 1, 0.04))
fig1 = cowplot::plot_grid(fig_top, fig_bot, nrow = 2, labels = c("","C"))
ggsave("FINAL_PLOTS/Figures/SupplementaryFigure02.pdf", fig1,
device = "pdf", height = 5.8, width = 7.2, paper = "a4", useDingbats = F)
```
# Figure 2
Load data
```{r}
load("saved_data/10X/10X_sub_data_list.RData")
load("plots/10X/DE/de_list_subpop.RData")
load("plots/10X/DE/de_summary_10x.RData")
load("saved_data/10X/all_data_cl.RData")
load("plots/10X/DE/pairwise_subcl_tiss_list.RData")
load("plots/SS2/DE/de_colon_skin_ss2.RData")
load("saved_data/SS2/colon_skin_data.RData")
probs = read.csv("plots/10X/LR/probs_test_data_TregSkin.csv", header= T, row.names = 1)
labs = read.csv("plots/10X/LR/preds_test_data_TregSkin.csv", header= T, row.names = 1)
labs_meta_10x = read.csv("plots/10X/LR/preds_metadata_Treg_10X_noStress.csv", header= T, row.names = 1)
labs_meta_ss2 = read.csv("plots/10X/LR/preds_metadata_Treg_SS2_allTreg10X_noStress.csv", header= T, row.names = 1)
```
## Make plots
tSNE plots for each tissue (Treg) - no stress cells
```{r, fig.width=5.5, fig.height=4.15}
sub_tsne_list = list()
for(n in names(sub_data_noStress_list)[c(1,3,5,7,11)]){
plot_df = cbind(sub_data_noStress_list[[n]]@dr$tsne@cell.embeddings, sub_data_noStress_list[[n]]@meta.data)
plot_df = merge(plot_df, all_data_cl@meta.data[,c("cl_annot", "tissue_sp")], by = 0)
plot_df$cl_annot = factor(plot_df$cl_annot)
levels(plot_df$cl_annot) = list("Treg suppressive" = "Treg_suppressive", "Treg NLT" = "Treg_NLT",
"Treg LT-like" = "Treg_LTlike", "Treg NLT-like" = "Treg_NLTlike",
"eTreg" = "Treg_effector", "Treg Stat1" = "Treg_Stat1",
"cTreg" = "Treg_lymphoid", "Tmem NLT" = "Tmem_NLT")
if(n=="Skin") n = "Skin (Treg and Tmem)"
plot_df = plot_df[sample(1:nrow(plot_df), nrow(plot_df), replace = F),]
sub_tsne_list[[n]] = ggplot(plot_df)+
geom_point(mapping = aes(x = tSNE_1, y = tSNE_2, colour = cl_annot),
size = 0.1, shape = 19)+
scale_colour_manual(values = c(cl_treg_pal), drop = FALSE)+
guides(colour = guide_legend(title = "Subpopulation", byrow = T,
title.position = "top", ncol = 2,
override.aes = list(size = 1.6)))+
ggtitle(gsub("_", " ", n))+
tsne_theme+theme(legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
plot.title = element_text(size = 8.25, hjust = 0, margin = margin(0,0,2,0)),
legend.box.spacing = unit(1, "lines"))
}
save(sub_tsne_list, file = "FINAL_PLOTS/plot_repository/sub_tsne_list.RData")
```
tSNE plots for each tissue (Tmem) - no stress cells
```{r, fig.width=5.5, fig.height=4.15}
sub_tsne_list_Tmem = list()
for(n in names(sub_data_noStress_list)[c(2,4,6,8,11)]){
plot_df = cbind(sub_data_noStress_list[[n]]@dr$tsne@cell.embeddings, sub_data_noStress_list[[n]]@meta.data)
plot_df = merge(plot_df, all_data_cl@meta.data[,c("cl_annot", "tissue_sp")], by = 0)
plot_df$cl_annot = factor(plot_df$cl_annot)
levels(plot_df$cl_annot) = rev(list("Tmem LT" = "Tmem_lymphoid", "Tmem LT" = "Tmem_lymphoidResident",
"Tmem Tfh" = "Tmem_activated", "Tmem Th1" = "Tmem_lymphoidEgressing",
"Tmem LT-like" = "Tmem_LTlike", "Tmem Cxcl10" = "Tmem_Cxcl10",
"Tmem cycling" = "Tmem_cycling", "Tmem Th1" = "Tmem_Th1",
"Tmem Th17" = "Tmem_Th17", "Tmem Th2" = "Tmem_Th2"))
if(n=="Skin") n = "Skin (Treg and Tmem)"
plot_df = plot_df[sample(1:nrow(plot_df), nrow(plot_df), replace = F),]
sub_tsne_list_Tmem[[n]] = ggplot(plot_df)+
geom_point(mapping = aes(x = tSNE_1, y = tSNE_2, colour = cl_annot),
size = 0.1, shape = 19)+
scale_colour_manual(values = c(cl_tmem_pal), drop = FALSE)+
guides(colour = guide_legend(title = "Subpopulation", byrow = T,
title.position = "top", ncol = 1,
override.aes = list(size = 1.6)))+
ggtitle(gsub("_", " ", n))+
tsne_theme+theme(legend.title = element_text(size = 7),
legend.text = element_text(size = 6),
plot.title = element_text(size = 8.25, hjust = 0, margin = margin(0,0,2,0)),
legend.box.spacing = unit(1, "lines"))
}
save(sub_tsne_list_Tmem, file = "FINAL_PLOTS/plot_repository/sub_tsne_list_Tmem.RData")
```
Ratio of NLT/LT Tmem genes/pop
```{r, fig.height=1.8, fig.width=2.3}
colon_treg_genes = de_summary_10x$colon_all$Gene[de_summary_10x$colon_all$logFC_NLT.vs.LT>=.25 &
de_summary_10x$colon_all$isDE_NLT.vs.LT &
de_summary_10x$colon_all$logFC_Treg.vs.Tmem<.25]
skin_treg_genes = de_summary_10x$skin_all$Gene[de_summary_10x$skin_all$logFC_NLT.vs.LT>=.25 &
de_summary_10x$skin_all$isDE_NLT.vs.LT &
de_summary_10x$skin_all$logFC_Treg.vs.Tmem<.25]
nlt_treg_genes = union(skin_treg_genes, colon_treg_genes)
clt_treg_genes = de_summary_10x$colon_all$Gene[de_summary_10x$colon_all$logFC_NLT.vs.LT<=(-.25) &
de_summary_10x$colon_all$isDE_NLT.vs.LT &
de_summary_10x$colon_all$logFC_Treg.vs.Tmem<.25]
slt_treg_genes = de_summary_10x$skin_all$Gene[de_summary_10x$skin_all$logFC_NLT.vs.LT<=(-.25) &
de_summary_10x$skin_all$isDE_NLT.vs.LT &
de_summary_10x$skin_all$logFC_Treg.vs.Tmem<.25]
lt_treg_genes = union(slt_treg_genes, clt_treg_genes)
genes_df = data.frame()
for(n in names(de_list_subpop)[c(2,21,15,19)]){
cell_type_sort = strsplit(n, "res")[[1]][1]
cell_type_sort = substr(cell_type_sort, 1, nchar(cell_type_sort)-1)
sub_df = data.frame("cell_type_sort" = rep(cell_type_sort, length(unique(de_list_subpop[[n]]$cluster))),
"cluster" = unique(de_list_subpop[[n]]$cluster),
"n" = rep(0, length(unique(de_list_subpop[[n]]$cluster))), stringsAsFactors = F)
for(cl in unique(de_list_subpop[[n]]$cluster)){
de_cl = de_list_subpop[[n]][de_list_subpop[[n]]$cluster==cl,]
genes_cl = as.character(de_cl$V2[de_cl$avg_logFC>=0.25 & de_cl$p_val_adj<=0.05])
sub_df$n[sub_df$cluster==cl] = log2(length(intersect(genes_cl, nlt_treg_genes))/length(intersect(genes_cl, lt_treg_genes)))
}
genes_df = rbind(genes_df, sub_df)
}
genes_df$cell_type_sort = gsub("_", " ", genes_df$cell_type_sort)
genes_df$cell_type_sort = factor(genes_df$cell_type_sort, levels = c("Tmem spleen", "Tmem bLN", "Tmem mLN",
"Tmem colon"))
genes_df$cluster = as.character(genes_df$cluster)
temp = factor(paste0(genes_df$cell_type_sort, genes_df$cluster), levels = unique(paste0(genes_df$cell_type_sort, genes_df$cluster)))
levels(temp) = list("Tmem Tfh" = "Tmem spleen2", "Tmem LT" = "Tmem spleen0", "Tmem Th1" = "Tmem spleen1",
"Tmem Th17" = "Tmem bLN3", "Tmem Tfh" = "Tmem bLN2", "Tmem LT" = "Tmem bLN0",
"Tmem Th1" = "Tmem bLN1", "Tmem Th17" = "Tmem bLN3", "Stress/low qual" = "Tmem mLN3",
"Tmem Th1" = "Tmem mLN1", "Tmem LT" = "Tmem mLN2",
"Tmem Tfh" = "Tmem mLN0",
"Tmem Cxcl10" = "Tmem mLN4", "Tmem Th17" = "Tmem mLN5", "Tmem cycling" = "Tmem mLN6",
"Tmem LT-like" = "Tmem colon3", "Stress/low qual" = "Tmem colon2", "Tmem Th1" = "Tmem colon1",
"Tmem Th17" = "Tmem colon0","Stress/low qual" = "Tmem colon4", "Tmem Th2" = "Tmem colon5")
genes_df$cluster = temp
genes_df$cluster = factor(genes_df$cluster, levels = unique(genes_df$cluster)[c(3,1,8,7,5,6,2,9,4)])
genes_df = genes_df[!grepl("Stress", genes_df$cluster),] # remove Stress cells
de_rat_bar_Tmem = ggplot(genes_df, aes(x = cell_type_sort, fill = cluster, y = n))+
geom_bar(stat = "identity", position = position_dodge2(preserve = "single"))+
scale_fill_manual(values = cl_tmem_pal)+
labs(y = "log2(NLT/LT)", fill = "Subpopulations")+
theme_bar+
theme(plot.margin = unit(c(0.6,0.27,0.2,0.27),"lines"))
save(de_rat_bar_Tmem, file = "FINAL_PLOTS/plot_repository/de_rat_bar_Tmem.RData")
```
Proportions barplot - Tmem
```{r, fig.height=1.8, fig.width=2.3}
plot_df = all_data_cl@meta.data[,c("cl_annot", "tissue_sp")]
plot_df = plot_df[!grepl("stress", plot_df$cl_annot),] # remove Stress cells
plot_df = table(plot_df[plot_df$cl_annot %in% unique(plot_df$cl_annot)[grepl("Tmem", unique(plot_df$cl_annot))],])
plot_df = data.frame(t(t(plot_df)/colSums(plot_df))*100)
plot_df = plot_df[plot_df$tissue_sp!="skin" & plot_df$cl_annot!="Tmem_NLT",]
levels(plot_df$cl_annot) = rev(list("Tmem LT" = "Tmem_lymphoid", "Tmem LT" = "Tmem_lymphoidResident",
"Tmem Tfh" = "Tmem_activated", "Tmem Th1" = "Tmem_lymphoidEgressing",
"Tmem LT-like" = "Tmem_LTlike", "Stress/low qual" = "Tmem_stress",
"Tmem Cxcl10" = "Tmem_Cxcl10", "Tmem cycling" = "Tmem_cycling",
"Tmem Th1" = "Tmem_Th1","Tmem Th2" = "Tmem_Th2", "Tmem Th17" = "Tmem_Th17"))
plot_df$tissue_sp = factor(paste("Tmem", plot_df$tissue_sp),
levels = c("Tmem spleen", "Tmem bLN", "Tmem mLN", "Tmem colon"))
prop_bars_Tmem = ggplot(plot_df, aes(x = tissue_sp, fill = cl_annot, y = Freq))+
geom_bar(stat = "identity")+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(values = cl_tmem_pal) +
guides(fill = guide_legend(reverse=T))+
labs(y = "%", fill = "Subpopulations")+
theme_bar+
theme(plot.margin = unit(c(0.6,0.27,0.2,0.27),"lines"))
save(prop_bars_Tmem, file = "FINAL_PLOTS/plot_repository/prop_bars_Tmem.RData")
```
Proportions barplot - Treg
```{r, fig.height=1.8, fig.width=2.3}
plot_df = all_data_cl@meta.data[,c("cl_annot", "tissue_sp")]
plot_df = plot_df[!grepl("stress", plot_df$cl_annot),] # remove Stress cells
plot_df = table(plot_df[plot_df$cl_annot %in% unique(plot_df$cl_annot)[grepl("Treg", unique(plot_df$cl_annot))],])
plot_df = data.frame(t(t(plot_df)/colSums(plot_df))*100)
levels(plot_df$cl_annot) = list("Treg suppressive" = "Treg_suppressive", "Treg NLT" = "Treg_NLT",
"Treg LT-like" = "Treg_LTlike", "Treg NLT-like" = "Treg_NLTlike",
"eTreg" = "Treg_effector","Treg Stat1" = "Treg_Stat1",
"cTreg" = "Treg_lymphoid", "Tmem NLT" = "Tmem_NLT",
"Stress/low qual" = "lowQuality","Stress/low qual" = "Treg_stress")
plot_df = plot_df[plot_df$tissue_sp!="skin",]
plot_df$tissue_sp = factor(paste("Treg", plot_df$tissue_sp),
levels = c("Treg spleen", "Treg bLN", "Treg mLN", "Treg colon"))
prop_bars = ggplot(plot_df, aes(x = tissue_sp, fill = cl_annot, y = Freq))+
geom_bar(stat = "identity")+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(values = cl_treg_pal)+
labs(y = "%", fill = "Subpopulations")+
theme_bar
save(prop_bars, file = "FINAL_PLOTS/plot_repository/prop_bars.RData")
```
Ratio of NLT/LT Treg genes/pop - Treg
```{r, fig.height=1.8, fig.width=2.3}
colon_treg_genes = de_summary_10x$colon_all$Gene[de_summary_10x$colon_all$logFC_NLT.vs.LT>=.25 &
de_summary_10x$colon_all$isDE_NLT.vs.LT &
de_summary_10x$colon_all$logFC_Treg.vs.Tmem>(-.25)]
skin_treg_genes = de_summary_10x$skin_all$Gene[de_summary_10x$skin_all$logFC_NLT.vs.LT>=.25 &
de_summary_10x$skin_all$isDE_NLT.vs.LT &
de_summary_10x$skin_all$logFC_Treg.vs.Tmem>(-.25)]
nlt_treg_genes = union(skin_treg_genes, colon_treg_genes)
clt_treg_genes = de_summary_10x$colon_all$Gene[de_summary_10x$colon_all$logFC_NLT.vs.LT<=(-.25) &
de_summary_10x$colon_all$isDE_NLT.vs.LT &
de_summary_10x$colon_all$logFC_Treg.vs.Tmem>(-.25)]
slt_treg_genes = de_summary_10x$skin_all$Gene[de_summary_10x$skin_all$logFC_NLT.vs.LT<=(-.25) &
de_summary_10x$skin_all$isDE_NLT.vs.LT &
de_summary_10x$skin_all$logFC_Treg.vs.Tmem>(-.25)]
lt_treg_genes = union(slt_treg_genes, clt_treg_genes)
genes_df = data.frame()
for(n in names(de_list_subpop)[c(1,4,11,17, 20)]){
cell_type_sort = strsplit(n, "res")[[1]][1]
cell_type_sort = substr(cell_type_sort, 1, nchar(cell_type_sort)-1)
sub_df = data.frame("cell_type_sort" = rep(cell_type_sort, length(unique(de_list_subpop[[n]]$cluster))),
"cluster" = unique(de_list_subpop[[n]]$cluster),
"n" = rep(0, length(unique(de_list_subpop[[n]]$cluster))), stringsAsFactors = F)
for(cl in unique(de_list_subpop[[n]]$cluster)){
de_cl = de_list_subpop[[n]][de_list_subpop[[n]]$cluster==cl,]
genes_cl = as.character(de_cl$V2[de_cl$avg_logFC>=0.25 & de_cl$p_val_adj<=0.05])
sub_df$n[sub_df$cluster==cl] = log2(length(intersect(genes_cl, nlt_treg_genes))/length(intersect(genes_cl, lt_treg_genes)))
}
genes_df = rbind(genes_df, sub_df)
}
genes_df = genes_df[-c(15,17),]
genes_df$cell_type_sort = gsub("_", " ", genes_df$cell_type_sort)
genes_df$cell_type_sort = gsub("Skin", "Treg skin", genes_df$cell_type_sort)
genes_df$cell_type_sort = factor(genes_df$cell_type_sort, levels = c("Treg spleen", "Treg bLN", "Treg mLN",
"Treg colon", "Treg skin"))
genes_df$cluster = as.character(genes_df$cluster)
temp = factor(paste0(genes_df$cell_type_sort, genes_df$cluster), levels = unique(paste0(genes_df$cell_type_sort, genes_df$cluster)))
levels(temp) = c("Treg spleen2" = "Treg NLT-like", "Treg spleen0" = "cTreg", "Treg spleen1" = "eTreg",
"Treg bLN3" = "eTreg", "Treg bLN2" = "Treg NLT-like", "Treg bLN0" = "cTreg", "Treg bLN1" = "Treg Stat1",
"Treg mLN0" = "cTreg", "Treg mLN1" = "eTreg", "Treg mLN2" = "Treg NLT-like",
"Treg colon2" = "Treg LT-like", "Treg colon3" = "Stress/low qual", "Treg colon1" = "Treg NLT",
"Treg colon0" = "Treg suppressive", "Treg skin2" = "Treg NLT")
genes_df$cluster = temp
genes_df = genes_df[c(2,3,1,6,7,4,5,8:15),]
genes_df$cluster = factor(genes_df$cluster, levels = unique(genes_df$cluster)[c(1,4,2,3,5:8)])
genes_df = genes_df[!grepl("Stress", genes_df$cluster),] # remove Stress cells
de_rat_bar = ggplot(genes_df, aes(x = cell_type_sort, fill = cluster, y = n))+
geom_bar(stat = "identity", position = position_dodge2(preserve = "single"))+
scale_fill_manual(values = cl_treg_pal, breaks = rev(c("Stress/low qual", "cTreg", "Treg Stat1", "eTreg",
"Treg NLT-like", "Treg LT-like", "Treg NLT",
"Treg suppressive")))+
labs(y = "log2(NLT/LT)", fill = "Subpopulations")+
theme_bar
save(de_rat_bar, file = "FINAL_PLOTS/plot_repository/de_rat_bar.RData")
```
Plot probabilities
```{r, fig.height=3, fig.width=3}
skin_Treg = rownames(all_data_cl@meta.data)[all_data_cl@meta.data$cl_tiss=="Treg_NLT.in.skin"]
plot_df = merge(probs, labs, by = 0)
plot_df[,1] = skin_Treg[as.numeric(plot_df$Row.names)+1]
hc = hclust(dist(plot_df[,2:5]), method = "ward.D2")
plot_df[,1] = factor(plot_df[,1], levels = plot_df[,1][hc$order])
colnames(plot_df) = gsub("_", " ", colnames(plot_df))
colnames(plot_df)[colnames(plot_df)=="Treg LTlike"] = "Treg LT-like"
colnames(plot_df)[colnames(plot_df)=="Treg stress"] = "Stress/low qual"
plot_df$X0 = gsub("_", " ", plot_df$X0)
plot_df$X0[plot_df$X0=="Treg LTlike"] = "Treg LT-like"
plot_df$X0[plot_df$X0=="Treg stress"] = "Stress/low qual"
plot_df$X0 = factor(plot_df$X0, levels = unique(plot_df$X0)[c(2,1,4,3)])
plot_df1 = reshape2::melt(plot_df[,1:5])
plot_df1$variable = factor(plot_df1$variable, levels = levels(plot_df$X0))
vals_heat = ggplot(plot_df1, aes(x = variable, y = Row.names, fill = value))+
geom_tile()+
labs(x = "Colon Treg Class", y = "Skin Treg Cells", fill = "Assignment\nProbability")+
scale_fill_viridis_c(limits = c(0,1), guide = guide_colourbar(barwidth = 0.4, barheight = 4))+
scale_x_discrete(expand = c(0,0))+
theme_classic()+
theme(axis.text.y = element_blank(),
axis.line = element_blank(),
axis.ticks.y = element_blank(),
legend.title = element_text(size = 5.5, face = "bold"),
legend.text = element_text(size = 4.75),
axis.title = element_text(size = 5.5, face = "bold"),
axis.text.x = element_text(size = 5.5, angle = 90, colour = "black",
hjust = 1, vjust = 0.5),
legend.background = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm"),
legend.margin = margin(0,0,0,0),
legend.box.margin = margin(0,0,0,0),
aspect.ratio = 2/1)
class_heat = ggplot(plot_df[,c(1,6)], aes(x = "Attributed\nLabel", y = Row.names, fill = X0))+
geom_tile()+
labs(x = " ", fill = "Predicted\nLabel")+
scale_x_discrete(expand = c(0,0))+
scale_fill_manual(values = cl_treg_pal)+
theme_classic()+
theme(axis.text = element_blank(),
axis.title.y = element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
legend.title = element_text(size = 5.5, face = "bold"),
legend.text = element_text(size = 4.75),
axis.title = element_text(size = 5.5, face = "bold"),
legend.background = element_blank(),
legend.key.size = unit(0.3, "lines"),
legend.margin = margin(0,0,0,0),
legend.box.margin = margin(0,0,0,0),
plot.margin = unit(c(0, 0, 0, 0), "cm"))
leg1 = get_legend(vals_heat)
leg2 = get_legend(class_heat)
legends_heat = plot_grid(NULL, leg1, NULL, leg2, nrow = 4, rel_heights = c(0.1, 1, 0.1, 1), align = "hv")
lr_probs_fig = (vals_heat+theme(legend.position = "none") + class_heat + theme(legend.position = "none") + plot_spacer() + legends_heat) + plot_layout(widths = c(0.7, 0.1, 0.01, 0.4), ncol = 4)
save(lr_probs_fig, file = "FINAL_PLOTS/plot_repository/lr_probs_fig.RData")
```
Plot labels confusion matrix - 10X
```{r, fig.height=3, fig.width=2.2}
tab_10x = table(labs_meta_10x[,c("cl_tiss", "labs")])
plot_df = reshape2::melt(tab_10x)
plot_df$perc = reshape2::melt(tab_10x/rowSums(tab_10x)*100)$value
plot_df$cluster = unlist(lapply(strsplit(as.character(plot_df$cl_tiss), ".in."), function(x) x[1]))
plot_df$tissue = unlist(lapply(strsplit(as.character(plot_df$cl_tiss), ".in."), function(x) x[2]))
plot_df$tissue = factor(plot_df$tissue, levels = rev(c("spleen", "bLN", "mLN", "skin", "colon")))
plot_df$labs = factor(plot_df$labs)
levels(plot_df$labs) = list("Treg\nsuppressive" = "Treg_suppressive", "Treg NLT" = "Treg_NLT",
"Treg\nLT-like" = "Treg_LTlike", "Stress/low qual" = "lowQuality",
"Stress\n/low qual" = "Treg_stress")
plot_df$cluster = factor(plot_df$cluster)
levels(plot_df$cluster) = rev(list("Treg\nsuppressive" = "Treg_suppressive", "Treg\nNLT" = "Treg_NLT",
"Treg\nLT-like" = "Treg_LTlike", "Treg\nNLT-like" = "Treg_NLTlike",
"eTreg" = "Treg_effector", "Treg\nStat1" = "Treg_Stat1",
"cTreg" = "Treg_lymphoid", "Stress\n/low qual" = "lowQuality",
"Stress\n/low qual" = "Treg_stress"))
plot_df$white = ifelse(plot_df$perc>70, "white", "black")
labs_heat_10x = ggplot(plot_df[plot_df$tissue!="colon",],
aes(x = labs, y = cluster, fill = perc, label = round(perc, 1)))+
facet_grid(tissue~., scales = "free_y", switch = "y", space = "free")+
geom_tile()+geom_text(mapping = aes(colour = white), size = 1.7, show.legend = F)+
labs(x = "Assignments", y = "Subpopulation by Tissue", fill = "% of\nSubpop.")+
guides(fill = guide_colorbar(barheight = 0.45, barwidth = 3.6, direction = "horizontal",
title.position = "left"))+
scale_fill_gradientn(colors = RColorBrewer::brewer.pal(n = 9,
name = "PuBuGn")[-c(9,8)])+
scale_colour_manual(values = c("black", "grey95"))+
scale_x_discrete(position = "top")+
theme_classic()+
theme(axis.text.y = element_text(size = 5.5, colour = "black"),
axis.line = element_blank(),
axis.title = element_text(size = 5.5, face = "bold"),
axis.text.x = element_text(size = 5.5, angle = 90, colour = "black",
hjust = 0, vjust = 0.5),
strip.text = element_text(size = 6, colour = "black", face = "bold"),
strip.background = element_rect(size = 0.15, linetype = "solid"),
panel.background = element_blank(),
plot.margin = unit(c(0, 0, 0.75, 0), "cm"),
legend.title = element_text(size = 5.5, face = "bold"),
legend.position = c(0.1, -0.07),
legend.text = element_text(size = 4.75),
legend.background = element_blank(),
legend.margin = margin(0,0,0,0),
legend.box.margin = margin(0,0,0,0),
legend.box.spacing = unit(0.05, "lines"),
legend.title.align = 0)
save(labs_heat_10x, file = "FINAL_PLOTS/plot_repository/labs_heat_10x.RData")
```
Plot labels confusion matrix - SS2
```{r, fig.height=2, fig.width=2.5}
sub_labs = labs_meta_ss2[grep("-state", labs_meta_ss2$t_c_cond),]
tab_ss2 = table(sub_labs[,c("tissue_exp", "labs")])
tab_ss2 = tab_ss2[rowSums(tab_ss2)>0,]
plot_df = reshape2::melt(tab_ss2)
plot_df$perc = reshape2::melt(tab_ss2/rowSums(tab_ss2)*100)$value
plot_df$cluster = unlist(lapply(strsplit(as.character(plot_df$cl_tiss), ".in."), function(x) x[1]))
plot_df$tissue = unlist(lapply(strsplit(as.character(plot_df$cl_tiss), ".in."), function(x) x[2]))
plot_df$tissue_exp = factor(plot_df$tissue_exp)
levels(plot_df$tissue_exp) = rev(list("colon" = "colon", "skin" = "skin",
"mLN" = "mLN", "bLN" = "ln",
"spleen" = "spleen"))
plot_df$labs = factor(plot_df$labs)
levels(plot_df$labs) = list("Treg\nsuppressive" = "Treg_suppressive", "Treg NLT" = "Treg_NLT",
"Treg LT-like" = "Treg_LTlike", "Treg NLT-like" = "Treg_NLTlike",
"eTreg" = "Treg_effector", "Treg Stat1" = "Treg_Stat1",
"cTreg" = "Treg_lymphoid", "Stress/low qual" = "lowQuality",
"Stress/low qual" = "Treg_stress")
plot_df$white = ifelse(plot_df$perc>55, "white", "black")
labs_heat_ss2 = ggplot(plot_df, aes(x = labs, y = tissue_exp, fill = perc, label = round(perc, 1)))+
geom_tile()+geom_text(mapping = aes(colour = white), size = 1.75, show.legend = F)+
labs(x = "Assignments", y = "Treg sorted from", fill = "% of\nTreg")+
guides(fill = guide_colorbar(barwidth = 0.5))+
scale_fill_gradientn(colors = RColorBrewer::brewer.pal(n = 9,
name = "PuBuGn")[-c(9,8)])+
scale_colour_manual(values = c("black", "grey95"))+
scale_x_discrete(position = "top")+
theme_classic()+
theme(axis.text.y = element_text(size = 5.5, colour = "black"),
axis.line = element_blank(),
axis.title = element_text(size = 5.5, face = "bold"),
axis.text.x = element_text(size = 5.5, angle = 90, colour = "black",
hjust = 0, vjust = 0.5),
strip.text = element_text(size = 6, colour = "black", face = "bold"),
strip.background = element_rect(size = 0.3, linetype = "solid"),
panel.background = element_blank(),
legend.title = element_text(size = 5.5, face = "bold"),
legend.text = element_text(size = 4.75),
legend.background = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm"),
legend.margin = margin(0,0,0,0),
legend.box.margin = margin(15,3,0,0))
save(labs_heat_ss2, file = "FINAL_PLOTS/plot_repository/labs_heat_ss2.RData")
```
DE NLT vs suppressive
```{r, fig.height=1.8, fig.width=1.8}
dat = SubsetData(all_data_cl, cells.use = rownames(all_data_cl@meta.data)[all_data_cl@meta.data$cell_type_sort=="Treg_colon"])
genes_mean_cl = apply(dat@data, 1, function(x) tapply(x, dat@meta.data$cl_annot, mean))
genes_mean_cl = merge(t(genes_mean_cl), gene_names, by.x = 0, by.y = 1)
genes_per_cl = t(apply(dat@data, 1, function(x) tapply(x, dat@meta.data$cl_annot, function(y) sum(y>0)/length(y))))
colnames(genes_per_cl) = paste0("per_", colnames(genes_per_cl))
genes_mean_cl = merge(genes_mean_cl, genes_per_cl, by.x = 1, by.y = 0)
genes_mean_cl = merge(genes_mean_cl, pairwise_subcl_tiss_list$Treg_NLT.in.colon.vs.Treg_suppressive.in.colon[,c(1,4)],
by = 1, all.x = T)
genes_mean_cl$avg_logFC[is.na(genes_mean_cl$avg_logFC)] = 0
genes_mean_cl$avg_logFC = abs(genes_mean_cl$avg_logFC)
degenes = pairwise_subcl_tiss_list$Treg_NLT.in.colon.vs.Treg_suppressive.in.colon$V2[abs(pairwise_subcl_tiss_list$Treg_NLT.in.colon.vs.Treg_suppressive.in.colon$avg_logFC)>=0.25 & pairwise_subcl_tiss_list$Treg_NLT.in.colon.vs.Treg_suppressive.in.colon$p_val_adj<=0.05]
genes_mean_cl$isDE = genes_mean_cl$V2 %in% degenes
genes_mean_cl$isDE = ifelse(genes_mean_cl$isDE & genes_mean_cl$Treg_NLT>genes_mean_cl$Treg_suppressive,
"Treg NLT",
ifelse(genes_mean_cl$isDE & genes_mean_cl$Treg_NLT<genes_mean_cl$Treg_suppressive,
"Treg suppressive", "Non-DE"))
genes_mean_cl$isDE = factor(genes_mean_cl$isDE, levels = c("Treg suppressive", "Treg NLT", "Non-DE"))
genes_mean_cl$per_size = ifelse(genes_mean_cl$isDE=="Treg NLT", genes_mean_cl$Treg_NLT,
ifelse(genes_mean_cl$isDE=="Treg suppressive", genes_mean_cl$Treg_suppressive, 0))
text_df = genes_mean_cl[genes_mean_cl$V2 %in% c("Tnfrsf9", "Il1rl1", "Gata3", "Il10","Cd83","Ccr2", "Fth1","Gzmb", "Dgat2","Kdm6b","Nrp1",
"Maf", "Lag3", "Tnfrsf9", "Tnfrsf4", "Itgb8", "Cxcr3", "Ikzf2", "Ly6a", "Areg"),]
genes_mean_cl = genes_mean_cl[order(genes_mean_cl$isDE, decreasing = T),]
de_nlt_sup = ggplot(genes_mean_cl, aes(x = per_Treg_NLT, y = per_Treg_suppressive, colour = isDE))+
geom_point(shape = 19, size = 0.6)+
ggrepel::geom_text_repel(data = text_df, colour = "black", max.iter = 4000, seed = 352,
mapping = aes(x = per_Treg_NLT, y = per_Treg_suppressive, label = V2),
min.segment.length = 0, fontface = "bold", size = 1.9, show.legend = F)+
scale_colour_manual(values = c(cl_treg_pal, "Non-DE" = "grey85"))+
scale_x_continuous(breaks = c(0, .25, .5, .75, 1), labels = c(0, 25, 50, 75, 100))+
scale_y_continuous(breaks = c(0, .25, .5, .75, 1), labels = c(0, 25, 50, 75, 100))+
labs(x = "Colon Treg NLT %", y = "Colon Treg suppressive %")+
guides(colour = guide_legend(override.aes = list(size = 1.7), title = "Subpopulation", order = 1,
title.position = "top", direction = "horizontal", title.hjust = 0))+
de_theme+
theme(legend.key.size = unit(0.5, "lines"),