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figures_02.R
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figures_02.R
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library(DropletUtils)
library(Seurat)
library(tidyverse)
library(wesanderson)
library(reshape2)
library(RColorBrewer)
library(grid)
library(gridExtra)
library(cowplot)
library(devtools)
library(beyonce, lib.loc = "/volumes/lab/users/aschalck/R/")
library(ggalluvial)
require(foreign)
require(nnet)
library(lme4)
library(GGally)
library(fgsea)
library(future)
library(future.apply)
library(tidyverse)
library(fuzzyjoin)
library(ggrepel)
library(GSVA)
library(GSVAdata)
library(GSEABase)
data(c2BroadSets)
library(org.Hs.eg.db)
data(leukemia)
library(GO.db)
library(msigdbr)
library(fgsea)
library(ggpubr)
source("~/TENX/analysis/MP/MP_project_4/TCR_functions_script.R")
options(future.globals.maxSize = 60000 * 1024^2)
NewSet1 = function(n){return(RColorBrewer::colorRampPalette(brewer.pal(9,"Set1")[c(1,5,6,3,2,4,8,7,9)])(n))}
patients.2_colors = patients.2_colors[sort(names(patients.2_colors))]
theme_one_line <- theme_classic() + theme(axis.text = element_text(size=12),
axis.ticks = element_line(size=0.5),
axis.line = element_line(size=0.5),
axis.title = element_text(size=16),
axis.title.x = element_blank(),
axis.line.x = element_blank(),
axis.ticks.x = element_blank())
theme_two_line <- theme_classic() + theme(axis.text = element_text(size=12),
axis.ticks = element_line(size=0.5),
axis.line = element_line(size=0.5),
axis.title = element_text(size=16))
SCTall_fil2_tcells$minor_clusters5 = as.character(SCTall_fil2_tcells$minor_clusters4)
SCTall_fil2_tcells$minor_clusters5[SCTall_fil2_tcells$minor_clusters5 == "Tcells - CD4:Naive"] = "Tcells - CD4:CCR7"
SCTall_fil2_tcells$minor_clusters5[SCTall_fil2_tcells$minor_clusters5 == "Tcells - CD8:Naive"] = "Tcells - CD8:CCR7"
SCTall_fil2_tcells$minor_clusters5[SCTall_fil2_tcells$minor_clusters5 == "Tcells - CD4:TREG1"] = "Tcells - CD4:FOXP3"
SCTall_fil2_tcells$minor_clusters5[SCTall_fil2_tcells$minor_clusters5 == "Tcells - CD4:TREG2"] = "Tcells - CD4:LAG3"
SCTall_fil2_tcells$minor_clusters5[SCTall_fil2_tcells$minor_clusters5 == "Tcells - CD4:TEMRA"] = "Tcells - CD4:GNLY"
SCTall_fil2_tcells$minor_clusters5[SCTall_fil2_tcells$minor_clusters5 == "Tcells - CD4:TFH"] = "Tcells - CD4:GNLY"
sample_colors = c("#94E3D3", "#517D74", "#A882B7", "#3C2544", "#AA936F", "#685230", "#5167c9", "#26305E", "#FC6F60", "#872219", "#ED9D2D", "#774601", "#5E92A4", "#112630", "#a3a3a3", "#4a4a4a", "#d8db6e", "#5d5e30", "#B9FF94", "#5B7D48", "#FF9CB9", "#6B414E")
patient_colors = sample_colors[c(1,3,5,7,9,11,13,15,17,19,21)]
tcell_colors_df = data.frame(cluster = unique(SCTall_fil2_tcells$minor_clusters4), color = c("#2E99C7","#2E64C7","#FF0000","#FF9E9E","#005717","#757067","#00FF44","black","#BF8828","#6E4444","#487554","#C7C2B9","#A13030","#6E0000","#9CFFB6","#36BF5A","#523CAB","#7F3CAB"), stringsAsFactors = F)
#tcell_colors_df = tcell_colors_df[c(2,1,14,13,3,4,10,11,5,16,7,15,17,18,9,6,12,8),]
tcell_colors_df = tcell_colors_df[c(2,14,13,3,4,10,17,9,1,11,5,16,7,15,18,6,12,8),]
tcell_colors_df = left_join(tcell_colors_df, data.frame("cluster" = SCTall_fil2_tcells$minor_clusters4, "clusters2" = SCTall_fil2_tcells$minor_clusters5, row.names = NULL) %>% unique())
tcell_colors = tcell_colors_df$color
names(tcell_colors) = tcell_colors_df$clusters2
cytokines_df = read_csv("~/code/resources/gene_lists/cytokines.csv")
cytokines = cytokines_df$gene[cytokines_df$gene %in% row.names(SCTall_fil2)]
cytokines = c(cytokines, "GZMA","GZMB","GZMH","GZMK","GZMM","NKG7","GNLY","PRF1","TNF","IFNG")
cytokines = cytokines[cytokines %in% row.names(SCTall_fil2)]
receptors_df = read_csv("~/code/resources/gene_lists/greceptors.csv")
greceptors = receptors_df$gene[receptors_df$gene %in% row.names(SCTall_fil2)]
lectins_df = read_csv("~/code/resources/gene_lists/lectins.csv")
lectins = lectins_df$gene[lectins_df$gene %in% row.names(SCTall_fil2)]
CD_df = read_csv("~/code/resources/gene_lists/CDs.csv")
CDs = CD_df$gene[CD_df$gene %in% row.names(SCTall_fil2)]
CDs = CDs[!CDs %in% c("CD8A","CD8B","CD4","CD3E","CD3D","CD3G")]
ISGs = row.names(SCTall_fil2)[str_detect(row.names(SCTall_fil2),"^ISG")]
ISGs = c(ISGs,row.names(SCTall_fil2)[str_detect(row.names(SCTall_fil2),"^IFI")])
ISGs = c(ISGs,"MX1","MX2")
revlog_trans <- function(base = exp(1)) {
trans <- function(x) -log1p(x)
inv <- function(x) expm1(-x)
scales::trans_new("revlog1p", trans, inv, domain = c(0, Inf))
}
SCTall_fil2_tcells@meta.data %>%
group_by(sample, timepoint,patient,cohort, minor_clusters4) %>%
summarise(n = n()) %>%
#group_by(sample, timepoint,patient,cohort) %>%
ungroup() %>%
tidyr::complete(minor_clusters4,nesting(sample, timepoint,patient,cohort), fill=list(n=0)) %>%
group_by(sample) %>%
mutate(p = n/sum(n)) %>%
filter(!str_detect(minor_clusters4, "Cycling")) %>%
filter(!str_detect(minor_clusters4, "NK")) %>%
ungroup() %>%
select(-sample,-n,-cohort) %>%
spread(key = minor_clusters4, value = p, fill=0) %>%
gather(-timepoint, -patient, key = minor_clusters4, value =p) %>%
ggplot() + aes(x = timepoint, y=p, group=patient, color=patient) +
geom_point() + geom_line() + scale_color_manual(values = patient_colors) +
facet_wrap(~minor_clusters4, nrow = 4) +
theme(panel.background = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title=element_blank(), legend.title = element_blank(),
strip.background = element_blank())
#######################
####tcells lineplot####
#######################
SCTall_fil2@meta.data %>%
filter(major_clusters %in% c("B - Cells", "Myeloid","T - Cells", "NK Cells")) %>%
group_by(sample, timepoint,patient,cohort, minor_clusters2) %>%
summarise(n = n()) %>%
ungroup() %>%
group_by(sample) %>%
mutate(p = n/sum(n)) %>%
ungroup() %>%
filter(str_detect(minor_clusters2, "Tcells")|str_detect(minor_clusters2, "^NK")) %>%
#filter(!str_detect(minor_clusters2, "Cycling")) %>%
dplyr::select(-sample,-n,-cohort) %>%
spread(key = minor_clusters2, value = p, fill=0) %>%
gather(-timepoint, -patient, key = minor_clusters2, value =p) %>%
mutate(minor_clusters2 = factor(minor_clusters2, levels = names(tcell_colors))) %>%
ggplot() + aes(x = timepoint, y=p, group=patient, color=patient) +
geom_point(size=2.5) + geom_line(size=0.8) + scale_color_manual(values = patient_colors) +
facet_wrap(~minor_clusters2, nrow = 3) +
theme(panel.background = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.title=element_blank(), legend.title = element_blank(),
strip.background = element_blank(),
legend.key = element_rect(colour = NA, fill = NA),
strip.text = element_text(size=12), legend.text = element_text(size=12),
axis.text = element_text(size=12),
panel.spacing.y = unit(2, "lines")) +
guides(color = guide_legend(override.aes = list(size = 2, shape=15))) ->
tcells_lineplot_grid
y.grob <- textGrob("Fraction of Immune Cells",
gp=gpar(col="black", fontsize=20), rot=90)
grid.arrange(arrangeGrob(tcells_lineplot_grid, left = y.grob, padding = unit(1,"cm"))) -> tcells_lineplot
ggsave(tcells_lineplot, filename = "./plots/fig_pieces_01/tcells_lineplot.pdf", width=13, height=7, device = cairo_pdf)
temp<-plot_grid(tcells_lineplot)
ggdraw(add_sub(plot = tcells_lineplot, label = "Fraction of Immune Cells", angle = 90, vpadding=grid::unit(0,"lines"),y=0.5, x=0, vjust=0))
for(i in names(tcell_colors)){
print(i)
SCTall_fil2@meta.data %>%
filter(major_clusters %in% c("B - Cells", "Myeloid","T - Cells", "NK Cells")) %>%
group_by(sample, timepoint,patient,cohort, minor_clusters2) %>%
summarise(n = n()) %>%
ungroup() %>%
group_by(sample) %>%
mutate(p = n/sum(n)) %>%
ungroup() %>%
mutate(minor_clusters2 = as.character(minor_clusters2)) %>%
filter(minor_clusters2 == i) %>%
#filter(str_detect(minor_clusters2, "Tcells - CD8:CCL4")) %>%
# ungroup() %>%
dplyr::select(-sample,-cohort,-n) %>%
spread(key = timepoint, value = p, fill = 0) -> temp
print(t.test(temp$pre, temp$post, paired = T)$p.value)
}
SCTall_fil2_tcells@meta.data %>%
#filter(major_clusters %in% c("B - Cells", "Myeloid","T - Cells", "NK Cells")) %>%
group_by(sample, timepoint,patient,cohort, minor_clusters4) %>%
summarise(n = n()) %>%
ungroup() %>%
group_by(sample) %>%
mutate(p = n/sum(n)) %>%
filter(str_detect(minor_clusters4, "Tcells")) %>%
filter(str_detect(minor_clusters4, "Tcells - CD8:CCL4")) %>%
ungroup() %>%
dplyr::select(-sample,-cohort,-n) %>%
spread(key = timepoint, value = p, fill = 0) -> temp
t.test(temp$pre, temp$post, paired = T)
Idents(SCTall_fil2_tcells) = "minor_clusters4"
temp = AverageExpression(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD8")], assays = c("RNA","SCT"), features = tfs[tfs%in%row.names(SCTall_fil2_tcells)], return.seurat = T)
SCTall_fil2_tcells_minor_clusters4_markers_CD8 = FindAllMarkers(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD8")], only.pos = T, assay = "SCT", logfc.threshold = 0.1)
SCTall_fil2_tcells_minor_clusters4_markers_CD4 = FindAllMarkers(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD4")], only.pos = T, assay = "SCT", logfc.threshold = 0.1)
SCTall_fil2_tcells_minor_clusters4_markers_CD8 %>%
filter(avg_logFC >= 0.5) %>%
pull(gene) %>%
unique() ->
SCTall_fil2_tcells_minor_clusters4_markers_CD8_high
SCTall_fil2_tcells_minor_clusters4_markers_CD4 %>%
filter(avg_logFC >= 0.5) %>%
pull(gene) %>%
unique() ->
SCTall_fil2_tcells_minor_clusters4_markers_CD4_high
SCTall_fil2_tcells_minor_clusters4_markers %>%
filter(cluster %in% c("NK Cells", "Tcells - Cycling","Tcells - GD")) %>%
filter(avg_logFC >= 0.5) %>%
pull(gene) %>%
unique() ->
SCTall_fil2_tcells_minor_clusters4_markers_high
################
###tcell umap###
################
SCTall_fil2_tcells = RunUMAP(SCTall_fil2_tcells, n.neighbors = 30,
features = c(SCTall_fil2_tcells_minor_clusters4_markers_CD8_high,
SCTall_fil2_tcells_minor_clusters4_markers_CD4_high,
SCTall_fil2_tcells_minor_clusters4_markers_high),
reduction.name = "UMAP_top", reduction.key = "umap_")
DimPlot(SCTall_fil2_tcells, reduction = "UMAP_top", cols = tcell_colors, pt.size = 1)
DimPlot(SCTall_fil2_tcells, reduction = "umap", cols = tcell_colors, pt.size = 0.5)
SCTall_fil2_tcells = RunUMAP(SCTall_fil2_tcells, n.neighbors = 50,
dims = 1:30,
reduction.name = "UMAP_all", reduction.key = "umap_all_")
DimPlot(SCTall_fil2_tcells, reduction = "UMAP_all", cols = tcell_colors, pt.size = 0.5)
tcell_umap_df = data.frame(umap1 = SCTall_fil2_tcells@reductions$UMAP_top@cell.embeddings[,1], umap2 = SCTall_fil2_tcells@reductions$UMAP_top@cell.embeddings[,2], cluster = SCTall_fil2_tcells$minor_clusters4, timepoint = SCTall_fil2_tcells$timepoint, stringsAsFactors = F)
tcell_umap_df = left_join(tcell_umap_df, tcell_colors_df)
tcell_umap_df$color = as.character(tcell_umap_df$color)
tcell_umap_df %>%
ggplot() + aes(x=umap1, y=umap2, color=color) +
geom_point(aes(alpha=timepoint), size=0.005) +
theme_void() + theme(axis.title = element_text(size=16), axis.title.y = element_text(angle = 90)) +
scale_color_identity("cluster", labels = names(tcell_colors), breaks = tcell_colors,
guide = "legend") + scale_alpha_manual(values = c(0.05,1)) +
theme(legend.position = "none", plot.margin = margin(0, 0, 20, 10)) ->
plot_tcell_umap_post
tcell_umap_df %>%
ggplot() + aes(x=umap1, y=umap2, color=color) +
geom_point(aes(alpha=timepoint), size=0.01) +
theme_void() + theme(axis.title = element_text(size=16), axis.title.y = element_text(angle = 90)) + scale_alpha_discrete(guide = FALSE) +
scale_color_identity("cluster", labels = names(tcell_colors), breaks = tcell_colors, guide = "legend") +
guides(color = guide_legend(override.aes = list(size=5))) + theme(legend.title = element_blank()) ->
plot_tcell_umap_legend
plot_tcell_umap_legend = get_legend(plot_tcell_umap_legend)
plot_grid(plot_tcell_umap_legend) -> plot_tcell_umap_legend_plot
ggsave(plot = plot_tcell_umap_legend_plot, filename = "./plots/fig_pieces_01/tcell_umap_legend.pdf", width = 2, height = 4.5, dpi = 300)
tcell_umap_df %>%
mutate(timepoint = factor(timepoint, levels = c("post","pre"))) %>%
ggplot() + aes(x=umap1, y=umap2, color=color) +
geom_point(aes(alpha=timepoint), size=0.005) + scale_alpha_manual(values = c(0.05,1)) +
theme_void() + theme(axis.title = element_text(size=16), axis.title.y = element_text(angle = 90)) +
scale_color_identity("cluster", labels = names(tcell_colors), breaks = tcell_colors,
guide = "legend") + theme(legend.position = "none", plot.margin = margin(0, 0, 20, 10)) ->
plot_tcell_umap_pre
plot_grid(plot_tcell_umap_pre,plot_tcell_umap_post) -> plot_tcell_umap
ggsave(plot = plot_tcell_umap, filename = "./plots/fig_pieces_01/tcell_umap.png", width = 6, height = 2.5, dpi = 300)
#####################
###tcell umap all####
#####################
tcell_umap_df = data.frame(umap1 = SCTall_fil2_tcells@reductions$umap@cell.embeddings[,1], umap2 = SCTall_fil2_tcells@reductions$umap@cell.embeddings[,2], cluster = SCTall_fil2_tcells$minor_clusters4, timepoint = SCTall_fil2_tcells$timepoint, stringsAsFactors = F)
tcell_umap_df = left_join(tcell_umap_df, tcell_colors_df)
tcell_umap_df$color = as.character(tcell_umap_df$color)
tcell_umap_df %>%
ggplot() + aes(x=umap1, y=umap2, color=color) +
geom_point(size=0.05) +
theme_void() + theme(axis.title = element_text(size=16), axis.title.y = element_text(angle = 90)) +
scale_color_identity("cluster", labels = names(tcell_colors), breaks = tcell_colors,guide = "legend") +
theme(legend.position = "none", plot.margin = margin(0, 0, 20, 10)) ->
plot_tcell_umap_all
#########################
###tcell umap_barchart###
#########################
SCTall_fil2_tcells@meta.data %>%
ggplot() + aes(x=timepoint, fill=minor_clusters4) + geom_bar(position="fill") +
scale_fill_manual(values = tcell_colors) + theme_minimal() +
scale_y_continuous(limits = c(0,1), expand = c(0,0)) +
theme(legend.position = "none") + theme(panel.grid = element_blank()) +
theme(axis.title = element_blank()) + theme(axis.text.y=element_blank()) +
theme(axis.text.x = element_text(size=12)) -> tcell_umap_bar
ggsave(plot = tcell_umap_bar, filename = "./plots/fig_pieces_01/tcell_umap_bar.pdf", width = 1.5, height = 4)
####################
###tcell heatmap####
####################
rbind(SCTall_fil2_tcells_minor_clusters4_markers,SCTall_fil2_tcells_minor_clusters4_markers_CD4,SCTall_fil2_tcells_minor_clusters4_markers_CD8) %>%
group_by(cluster) %>%
top_n(5, avg_logFC) %>%
filter(!gene %in% c(tfs,cytokines,checkpoint_genes)) %>%
filter(!gene %in% greceptors) %>%
filter(!gene %in% lectins) %>%
filter(!gene %in% CDs) %>%
filter(!gene %in% ISGs) %>%
View()
top_gene_filter_tcells = function(genes_list,n=5){
SCTall_fil2_tcells_minor_clusters4_markers %>%
mutate(cluster = factor(cluster, levels = as.character(tcell_colors_df$cluster))) %>%
filter(cluster %in% c("NK Cells", "Tcells - Cycling","Tcells - GD")) %>%
filter(gene %in% genes_list) %>%
group_by(cluster) %>%
top_n(n,avg_logFC) %>%
pull(gene) %>%
unique() ->
return_genes
SCTall_fil2_tcells_minor_clusters4_markers_CD4 %>%
mutate(cluster = factor(cluster, levels = as.character(tcell_colors_df$cluster))) %>%
arrange(cluster) %>%
filter(gene %in% genes_list) %>%
group_by(cluster) %>%
top_n(n,avg_logFC) %>%
pull(gene) %>%
unique() %>%
c(return_genes) %>%
unique() ->
return_genes
SCTall_fil2_tcells_minor_clusters4_markers_CD8 %>%
mutate(cluster = factor(cluster, levels = as.character(tcell_colors_df$cluster))) %>%
arrange(cluster) %>%
filter(gene %in% genes_list) %>%
group_by(cluster) %>%
top_n(n,avg_logFC) %>%
pull(gene) %>%
c(return_genes) %>%
unique() ->
return_genes
return(return_genes)
}
tcell_genes_tfs = top_gene_filter_tcells(tfs, 5)
tcell_genes_cyto = top_gene_filter_tcells(cytokines, 5)
tcell_genes_ckpts = top_gene_filter_tcells(checkpoint_tcells, 5)
recepts = c(greceptors,lectins,CDs)
tcell_genes_recepts = top_gene_filter_tcells(recepts, 5)
tcell_genes_ISGs = top_gene_filter_tcells(ISGs, 5)
tcell_genes_allgroups = c(tcell_genes_tfs,tcell_genes_cyto,tcell_genes_ckpts,figures_01,tcell_genes_ISGs)
tcell_genes_allgroups = unique(tcell_genes_allgroups)
#tcell_genes_tfs = rev(tcell_genes_tfs)
SCTall_fil2_tcells$minor_clusters4 = factor(SCTall_fil2_tcells$minor_clusters4, levels = as.character(tcell_colors_df$cluster))
Idents(SCTall_fil2_tcells) = "minor_clusters4"
SCTall_fil2_tcells_avg_CD8 = AverageExpression(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD8")], features = tcell_genes_allgroups, return.seurat = T)
DoHeatmap(SCTall_fil2_tcells_avg_CD8, features = tcell_genes_allgroups, assay = "SCT", slot = "scale.data", label = FALSE,
disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") +
theme(axis.text.y = element_text(size=6)) -> tcell_heatmap_CD8
library(gtable)
g <- ggplotGrob(tcell_heatmap_CD8)
s <- gtable_filter(g, 'axis-l|ylab', trim=F) # use trim depending on need
grid.newpage()
grid.draw(s)
DoHeatmap(SCTall_fil2_tcells_avg_CD8, features = tcell_genes_allgroups, assay = "SCT", slot = "scale.data", label = FALSE,
disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_blank()) -> tcell_heatmap_CD8
SCTall_fil2_tcells_avg_CD4 = AverageExpression(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD4")], features = tcell_genes_allgroups, return.seurat = T)
DoHeatmap(SCTall_fil2_tcells_avg_CD4, features = tcell_genes_allgroups, assay = "SCT", label = FALSE,
slot = "scale.data", disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_blank()) -> tcell_heatmap_CD4
SCTall_fil2_tcells_avg_all = AverageExpression(SCTall_fil2_tcells, features = tcell_genes_allgroups, return.seurat = T)
SCTall_fil2_tcells_avg_other = SCTall_fil2_tcells_avg_all[,!str_detect(colnames(SCTall_fil2_tcells_avg_all),"CD[48]")]
DoHeatmap(SCTall_fil2_tcells_avg_other, features = tcell_genes_allgroups, assay = "SCT",label = FALSE,
slot = "scale.data", disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_blank()) -> tcell_heatmap_other
plot_grid(s,tcell_heatmap_CD8, tcell_heatmap_CD4, tcell_heatmap_other, nrow=1, align = "h", rel_widths = c(4,8,7,3)) -> tcells_heatmap_bottom
ggsave(tcells_heatmap_bottom, filename = "./plots/fig_pieces_01/tcells_heatmap_bottom.pdf", width=2, height=9)
#ggsave(tcells_heatmap_bottom, filename = "./plots/fig_pieces_01/tcells_heatmap_bottom.png", width=2, height=9)
tcell_genes_lineage = c("CD3E","CD3D","CD3G","CD8A","CD4","TRAC","TRBC2","TRDC","NCAM1")
SCTall_fil2_tcells_avg_all_lin = AverageExpression(SCTall_fil2_tcells, features = tcell_genes_lineage, return.seurat = T)
DoHeatmap(SCTall_fil2_tcells_avg_all_lin, features = tcell_genes_lineage, assay = "SCT", label = FALSE,
slot = "scale.data", disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") +
theme(axis.text.y = element_text(size=6)) -> tcell_heatmap_markers
ggsave(tcell_heatmap_markers, filename = "./plots/fig_pieces_01/tcell_heatmap_top.pdf", width=2, height=1.1)
DoHeatmap(SCTall_fil2_tcells_avg_all_lin, features = tcell_genes_lineage, assay = "SCT", label = FALSE,
slot = "scale.data", disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") +
theme(axis.text.y = element_text(size=6)) -> temp
tcell_heatmap_legend = get_legend(temp)
plot_grid(tcell_heatmap_legend) -> tcell_heatmap_legend
ggsave(tcell_heatmap_legend, filename = "./plots/fig_pieces_01/tcell_heatmap_legend.pdf", width=2, height=9)
##################################
###tcell heatmap ungrouped########
##################################
SCTall_fil2_tcells_minor_clusters4_markers_CD4 %>%
filter(!str_detect(gene, "MT-")) %>%
filter(!str_detect(gene, "RP[LS]")) %>%
filter(!str_detect(gene, "^HSP")) %>%
filter(!gene %in% c("FOS","FOSB","JUN")) %>%
filter(!gene %in% tcell_genes_lineage) %>%
mutate(pct = pct.1-pct.2) %>%
filter(pct > 0.12) %>%
filter(pct.1 > 0.25) %>%
filter(pct.2 < 0.5) %>%
group_by(gene) %>%
top_n(1, pct.1) %>%
#group_by(gene) %>%
#top_n(2, pct.1) %>%
#arrange(cluster, -pct) %>%
arrange(cluster, -avg_logFC) %>%
group_by(cluster) %>%
top_n(10, avg_logFC) %>%
pull(gene) %>%
unique() ->
tcell_genes_CD4
SCTall_fil2_tcells_minor_clusters4_markers_CD8 %>%
filter(!str_detect(gene, "MT-")) %>%
filter(!str_detect(gene, "RP[LS]")) %>%
filter(!str_detect(gene, "^HSP")) %>%
filter(!str_detect(gene, "^HB")) %>%
filter(!str_detect(gene, "^TR[ABDG][VDJ]")) %>%
filter(!gene %in% c("FOS","FOSB","JUN")) %>%
filter(!gene %in% tcell_genes_lineage) %>%
mutate(pct = pct.1-pct.2) %>%
#filter(pct > 0.12) %>%
#filter(pct.1 > 0.25) %>%
group_by(gene) %>%
top_n(1, pct.1) %>%
arrange(cluster, -avg_logFC) %>%
group_by(cluster) %>%
top_n(10, avg_logFC) %>%
pull(gene) %>%
unique() ->
tcell_genes_CD8
SCTall_fil2_tcells_minor_clusters4_markers %>%
mutate(cluster = factor(cluster, levels = tcell_colors_df$cluster)) %>%
filter(cluster %in% c("NK Cells", "Tcells - Cycling","Tcells - GD")) %>%
filter(!str_detect(gene, "MT-")) %>%
filter(!str_detect(gene, "RP[LS]")) %>%
filter(!str_detect(gene, "^HSP")) %>%
filter(!gene %in% c("FOS","FOSB","JUN")) %>%
filter(!gene %in% tcell_genes_lineage) %>%
mutate(pct = pct.1-pct.2) %>%
filter(pct > 0.12) %>%
filter(pct.1 > 0.25) %>%
filter(pct.2 < 0.5) %>%
group_by(gene) %>%
top_n(1, pct.1) %>%
#group_by(gene) %>%
#top_n(2, pct.1) %>%
#arrange(cluster, -pct) %>%
arrange(cluster, -avg_logFC) %>%
group_by(cluster) %>%
top_n(10, avg_logFC) %>%
pull(gene) %>%
unique() ->
tcell_genes_other
tcell_genes_all= c(tcell_genes_CD8, tcell_genes_CD4,tcell_genes_other)
SCTall_fil2_tcells$minor_clusters4 = factor(SCTall_fil2_tcells$minor_clusters4, levels = as.character(tcell_colors_df$cluster))
Idents(SCTall_fil2_tcells) = "minor_clusters4"
#SCTall_fil2_tcells_avg_CD8 = AverageExpression(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[48]")], features = tcell_genes_CD8, return.seurat = T)
SCTall_fil2_tcells_avg_CD8 = AverageExpression(SCTall_fil2_tcells, features = tcell_genes_CD8, return.seurat = T)
DoHeatmap(SCTall_fil2_tcells_avg_CD8, features = tcell_genes_CD8, assay = "SCT", slot = "scale.data", label = FALSE,
disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") +
theme(axis.text.y = element_text(size=6)) -> tcell_heatmap_CD8
#SCTall_fil2_tcells_avg_CD4 = AverageExpression(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD4")], features = tcell_genes_all, return.seurat = T)
SCTall_fil2_tcells_avg_CD4 = AverageExpression(SCTall_fil2_tcells, features = tcell_genes_CD4, return.seurat = T)
DoHeatmap(SCTall_fil2_tcells_avg_CD4, features = tcell_genes_CD4, assay = "SCT", label = FALSE,
slot = "scale.data", disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_text(size=6)) -> tcell_heatmap_CD4
SCTall_fil2_tcells_avg_all = AverageExpression(SCTall_fil2_tcells, features = tcell_genes_other, return.seurat = T)
DoHeatmap(SCTall_fil2_tcells_avg_all, features = tcell_genes_other, assay = "SCT", label = FALSE,
slot = "scale.data", disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_text(size=6)) -> tcell_heatmap_other
tcell_genes_lineage = c("CD3E","CD3D","CD3G","CD8A","CD4","TRAC","TRBC2","TRDC","NCAM1")
SCTall_fil2_tcells_avg_all_lin = AverageExpression(SCTall_fil2_tcells, features = tcell_genes_lineage, return.seurat = T)
DoHeatmap(SCTall_fil2_tcells_avg_all_lin, features = tcell_genes_lineage, assay = "SCT", slot = "scale.data", label = FALSE,
disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_text(size=6)) -> tcell_heatmap_markers
library(gtable)
g <- ggplotGrob(tcell_heatmap_CD8)
s1 <- gtable_filter(g, 'axis-l|ylab', trim=F) # use trim depending on need
grid.newpage()
grid.draw(s1)
library(gtable)
g <- ggplotGrob(tcell_heatmap_CD4)
s2 <- gtable_filter(g, 'axis-l|ylab', trim=F) # use trim depending on need
grid.newpage()
grid.draw(s2)
library(gtable)
g <- ggplotGrob(tcell_heatmap_other)
s3 <- gtable_filter(g, 'axis-l|ylab', trim=F) # use trim depending on need
grid.newpage()
grid.draw(s3)
g <- ggplotGrob(tcell_heatmap_markers)
s4 <- gtable_filter(g, 'axis-l|ylab', trim=F) # use trim depending on need
grid.newpage()
grid.draw(s4)
DoHeatmap(SCTall_fil2_tcells_avg_CD8, features = tcell_genes_CD8, assay = "SCT", slot = "scale.data", label = FALSE,
disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_blank()) -> tcell_heatmap_CD8
DoHeatmap(SCTall_fil2_tcells_avg_CD4, features = tcell_genes_CD4, assay = "SCT", slot = "scale.data", label = FALSE,
disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_blank()) -> tcell_heatmap_CD4
DoHeatmap(SCTall_fil2_tcells_avg_all, features = tcell_genes_other, assay = "SCT", slot = "scale.data", label = FALSE,
disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_blank()) -> tcell_heatmap_other
DoHeatmap(SCTall_fil2_tcells_avg_all_lin, features = tcell_genes_lineage, assay = "SCT", slot = "scale.data", label = FALSE,
disp.min = -2, disp.max = 2, draw.lines = F, group.colors = tcell_colors, raster = F) + NoLegend() +
scale_fill_gradient2(low = "#0054b5",mid = "#dbdbdb", high = "#ebac00", na.value = "00000000") + theme(plot.margin = unit(c(0,0,0,0), "cm")) +
theme(axis.text.y = element_blank()) -> tcell_heatmap_markers
plot_grid(s4, tcell_heatmap_markers, s1,tcell_heatmap_CD8, s2, tcell_heatmap_CD4, s3, tcell_heatmap_other, nrow=4, align = "h", rel_widths = c(0.1,1), rel_heights = c(0.15,1,1,0.43)) -> tcells_heatmap_bottom
ggsave(tcells_heatmap_bottom, filename = "./plots/fig_pieces_01/tcells_heatmap_ungroup.pdf", device = cairo_pdf, width=4.5, height=24)
c("CD3E","CD3D","CD3G","CD8A","CD4","TRAC","TRBC1","TRDC","TRGC1","B3GAT1",checkpoint_tcells, "IFNG","TNF","CCR7","TCF7","LEF1", SCTall_fil2_t_CD4_markers_top,SCTall_fil2_t_CD8_markers_top,"MKI67","TUBB","ACTB")
rbind(SCTall_fil2_t_CD4_markers,SCTall_fil2_t_CD8_markers) %>%
filter(gene %in% cytokines) %>% View()
group_by(cluster) %>%
top_n(5,avg_logFC) %>%
pull(gene) %>%
unique() ->
tcell_genes_cyto
pal = colorRampPalette(c("#0079c9", "#cedbcc","#dbb700"))
scales::show_col(pal(25))
##########################
##########################
###expresison channges####
##########################
##########################
Idents(SCTall_fil2_tcells) = "timepoint"
SCTall_fil2_tcells_timepoint_SCT_markers = FindAllMarkers(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[48]")], logfc.threshold = 0, assay = "SCT", min.pct = 0.01)
SCTall_fil2_tcells_timepoint_RNA_markers = FindAllMarkers(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[48]")], logfc.threshold = 0, assay = "RNA", min.pct = 0.01)
SCTall_fil2_tcells_timepoint_SCT_markers %>%
filter(!str_detect(gene, "^HB[AB]")) %>%
filter(cluster == "post") ->
SCTall_fil2_tcells_timepoint_SCT_markers_fil
SCTall_fil2_tcells_timepoint_RNA_markers %>%
filter(!is.infinite(avg_logFC)) %>%
filter(!str_detect(gene, "^HB[AB]")) %>%
filter(!str_detect(gene, "^MT-")) %>%
filter(cluster == "post") ->
SCTall_fil2_tcells_timepoint_RNA_markers_fil
#SCTall_fil2_tcells_patient_timepoint_SCT_markers = c()
#SCTall_fil2_tcells_patient_timepoint_RNA_markers = c()
#for(i in patients[c(2,4,5,6,7,9,10,11)]){
# temp = FindAllMarkers(SCTall_fil2_tcells[,(str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[48]") & SCTall_fil2_tcells$patient==i)], logfc.threshold = 0, assay = "SCT", only.pos = TRUE, min.pct = 0.01)
# temp$patient = i
# SCTall_fil2_tcells_patient_timepoint_SCT_markers = rbind(temp, SCTall_fil2_tcells_patient_timepoint_SCT_markers)
# temp = FindAllMarkers(SCTall_fil2_tcells[,(str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[48]") & SCTall_fil2_tcells$patient==i)], logfc.threshold = 0, assay = "RNA", only.pos = TRUE, min.pct = 0.01)
# temp$patient = i
# SCTall_fil2_tcells_patient_timepoint_RNA_markers = rbind(temp, SCTall_fil2_tcells_patient_timepoint_RNA_markers)
#}
####################
###cd4cd8 prepost###
####################
Idents(SCTall_fil2_tcells) = "timepoint"
SCTall_fil2_tcells_timepoint_SCT_markers_CD8 = FindAllMarkers(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[8]")], logfc.threshold = 0, assay = "SCT", min.pct = 0.01)
SCTall_fil2_tcells_timepoint_SCT_markers_CD4 = FindAllMarkers(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[4]")], logfc.threshold = 0, assay = "SCT", min.pct = 0.01)
SCTall_fil2_tcells_timepoint_SCT_markers_CD8 %>%
filter(!str_detect(gene, "^HB[AB]")) %>%
filter(cluster == "post") %>%
arrange(-avg_logFC) ->
SCTall_fil2_tcells_timepoint_SCT_markers_CD8_fil
SCTall_fil2_tcells_timepoint_SCT_markers_CD4 %>%
filter(!str_detect(gene, "^HB[AB]")) %>%
filter(cluster == "post") %>%
arrange(-avg_logFC) ->
SCTall_fil2_tcells_timepoint_SCT_markers_CD4_fil
SCTall_fil2_tcells_timepoint_SCT_CD8_gsea_C7 = rungsea(SCTall_fil2_tcells_timepoint_SCT_markers_CD8_fil, cats = "C7")
SCTall_fil2_tcells_timepoint_SCT_CD4_gsea_C7 = rungsea(SCTall_fil2_tcells_timepoint_SCT_markers_CD4_fil, cats = "C7")
SCTall_fil2_tcells_timepoint_SCT_CD8_gsea_C7 %>%
filter((str_detect(pathway, "TCELL")|str_detect(pathway, "TREG")|str_detect(pathway, "EFF")|str_detect(pathway, "CONV")|str_detect(pathway, "MEM")|str_detect(pathway, "CD[48]"))) %>%
filter(!(str_detect(pathway, "DC"))) %>%
filter(!(str_detect(pathway, "BCELL") & (!str_detect(pathway,"TCELL")))) %>%
arrange(-NES) ->
SCTall_fil2_tcells_timepoint_SCT_CD8_gsea_C7_fil
SCTall_fil2_tcells_timepoint_SCT_CD4_gsea_C7 %>%
filter((str_detect(pathway, "TCELL")|str_detect(pathway, "TREG")|str_detect(pathway, "EFF")|str_detect(pathway, "CONV")|str_detect(pathway, "MEM")|str_detect(pathway, "CD[48]"))) %>%
filter(!(str_detect(pathway, "DC"))) %>%
filter(!(str_detect(pathway, "BCELL") & (!str_detect(pathway,"TCELL")))) %>%
arrange(-NES) ->
SCTall_fil2_tcells_timepoint_SCT_CD4_gsea_C7_fil
######################
###cluster markers####
######################
Idents(SCTall_fil2_tcells) = "minor_clusters4"
SCTall_fil2_tcells_SCT_CD4_naive_markers = FindMarkers(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[4]")], ident.1 = "Tcells - CD4:Naive", logfc.threshold = 0, assay = "SCT", min.pct = 0.01)
SCTall_fil2_tcells_SCT_CD4_naive_markers$gene = row.names(SCTall_fil2_tcells_SCT_CD4_naive_markers)
SCTall_fil2_tcells_SCT_CD4_naive_markers$cluster = "Tcells - CD4:Naive"
SCTall_fil2_tcells_SCT_CD8_CCL4_markers = FindMarkers(SCTall_fil2_tcells[,str_detect(SCTall_fil2_tcells$minor_clusters4,"CD[8]")], ident.1 = "Tcells - CD8:CCL4", logfc.threshold = 0, assay = "SCT", min.pct = 0.01)
SCTall_fil2_tcells_SCT_CD8_CCL4_markers$gene = row.names(SCTall_fil2_tcells_SCT_CD8_CCL4_markers)
SCTall_fil2_tcells_SCT_CD8_CCL4_markers$cluster = "Tcells - CD8:CCL4"
SCTall_fil2_tcells_SCT_CD8_CCL4_gsea_C7 = rungsea(SCTall_fil2_tcells_SCT_CD8_CCL4_markers, cats = "C7", mincat = 5)
SCTall_fil2_tcells_SCT_CD8_CCL4_gsea_C7 %>%
filter((str_detect(pathway, "TCELL")|str_detect(pathway, "TREG")|str_detect(pathway, "EFF")|str_detect(pathway, "CONV")|str_detect(pathway, "MEM")|str_detect(pathway, "CD[48]"))) %>%
filter(!(str_detect(pathway, "DC"))) %>%
filter(!(str_detect(pathway, "BCELL") & (!str_detect(pathway,"TCELL")))) %>%
arrange(-NES) %>%
#filter(size <= 10) %>%
#filter(!str_detect(pathway, "CD4")) %>%
#filter(!str_detect(pathway, "TREG")) %>%
View()
SCTall_fil2_tcells_SCT_CD4_naive_gsea_C7 = rungsea(SCTall_fil2_tcells_SCT_CD4_naive_markers, cats = "C7", mincat = 5)
SCTall_fil2_tcells_SCT_CD4_naive_gsea_C7 %>%
filter((str_detect(pathway, "TCELL")|str_detect(pathway, "TREG")|str_detect(pathway, "EFF")|str_detect(pathway, "CONV")|str_detect(pathway, "MEM")|str_detect(pathway, "CD[48]"))) %>%
filter(!(str_detect(pathway, "DC"))) %>%
filter(!(str_detect(pathway, "BCELL") & (!str_detect(pathway,"TCELL")))) %>%
arrange(-NES) %>%
#filter(size <= 10) %>%
#filter(!str_detect(pathway, "CD4")) %>%
#filter(!str_detect(pathway, "TREG")) %>%
View()
#################
###volcanoplot###
#################
anno_genes = c("CXCR4","NFKBIA","JUNB","CD69","TSC22D3","FOS","KLF2","KLF6","NFKBIA","FGFBP2","CCL4","CCR7","GZMB","GNLY","GZMH")
anno_genes = c("CXCR4","CD69","FOS","KLF2","FGFBP2","CCL4","CCR7","GZMB","GNLY","GZMH")
SCTall_fil2_tcells_timepoint_SCT_markers_fil = readRDS("./Rda/SCTall_fil2_tcells_timepoint_SCT_markers_fil.rds")
SCTall_fil2_tcells_timepoint_SCT_markers_fil %>%
mutate(p_val_adj = if_else(p_val_adj==0, 10^-300, p_val_adj)) %>%
mutate(anno = if_else(gene %in% anno_genes, "yes", "no")) -> temp
temp %>%
mutate(dir = "no_change") %>%
mutate(dir = if_else(avg_logFC >= 0.25 & p_val_adj <= 0.01, "up", dir)) %>%
mutate(dir = if_else(avg_logFC <= -0.25 & p_val_adj <= 0.01, "down", dir)) %>%
ggplot(aes(x = avg_logFC, y = -log10(p_val_adj))) +
theme_minimal() +
scale_x_continuous(limits = c(-1.3,1.3),expand = c(0,.1)) +
scale_y_continuous(limits = c(0,300),expand = c(0,5)) +
geom_vline(xintercept = 0, linetype = "dashed") +
geom_vline(xintercept = 0.25, linetype = "dashed") +
geom_vline(xintercept = -0.25, linetype = "dashed") +
geom_hline(yintercept = 2, linetype = "dashed") +
#geom_hline(yintercept = 0) +
#geom_hline(yintercept = 25, linetype = "dashed") +
#geom_vline(xintercept = c(-.25, .25), linetype = "dashed") +
theme_linedraw() +
geom_point(aes(color = dir), alpha = 1) +
scale_color_manual(values = c("royalblue4","grey50","goldenrod")) +
theme(panel.grid = element_blank()) +
xlab("Average Log Fold Change") +
ylab("-log10(Adjusted P-Value)") +
theme(legend.position = "none",axis.text = element_text(size=16), axis.title = element_text(size = 20)) +
theme(axis.ticks = element_line(size=0.75)) +
theme(panel.border = element_blank()) +
theme(axis.line.x = element_line(size=0.75), axis.line.y = element_line(size=0.75)) +
geom_text_repel(
data = subset(temp, anno=="yes"),
aes(label = gene),
size = 5,
box.padding = unit(0.35, "lines"),
point.padding = unit(0.3, "lines"),
min.segment.length = 0.1) -> tcell_volcano_plot
ggsave(tcell_volcano_plot, filename = "~/projects/Breast_cancer_radiation/fig_pieces_01/tcells_volcano_plot.pdf", width=6, height=5, device = cairo_pdf)
device = cairo_pdf
#################
###gsea##########
#################
#SCTall_fil2_tcells_timepoint_SCT_gsea_C2 = rungsea(SCTall_fil2_tcells_timepoint_SCT_markers_fil, cats = "C2")
#SCTall_fil2_tcells_timepoint_SCT_gsea_C5 = rungsea(SCTall_fil2_tcells_timepoint_SCT_markers_fil, cats = "C5")
SCTall_fil2_tcells_timepoint_SCT_gsea_C7 = rungsea(SCTall_fil2_tcells_timepoint_SCT_markers_fil, cats = "C7")
#SCTall_fil2_tcells_timepoint_RNA_gsea_C2 = rungsea(SCTall_fil2_tcells_timepoint_RNA_markers_fil, cats = "C2")
#SCTall_fil2_tcells_timepoint_RNA_gsea_C5 = rungsea(SCTall_fil2_tcells_timepoint_RNA_markers_fil, cats = "C5")
#SCTall_fil2_tcells_timepoint_RNA_gsea_C7 = rungsea(SCTall_fil2_tcells_timepoint_RNA_markers_fil, cats = "C7")
SCTall_fil2_tcells_timepoint_SCT_gsea_C7 %>%
filter((str_detect(pathway, "TCELL")|str_detect(pathway, "TREG")|str_detect(pathway, "EFF")|str_detect(pathway, "CONV")|str_detect(pathway, "MEM")|str_detect(pathway, "CD[48]"))) %>%
filter(!(str_detect(pathway, "DC"))) %>%
filter(!(str_detect(pathway, "BCELL") & (!str_detect(pathway,"TCELL")))) ->
SCTall_fil2_tcells_timepoint_SCT_gsea_C7_fil
#SCTall_fil2_tcells_timepoint_RNA_gsea_C7 %>%
# filter((str_detect(pathway, "TCELL")|str_detect(pathway, "TREG")|str_detect(pathway, "EFF")|str_detect(pathway, "CONV")|str_detect(pathway, "MEM")|str_detect(pathway, "CD[48]"))) %>%
# filter(!(str_detect(pathway, "DC"))) %>%
# filter(!(str_detect(pathway, "BCELL") & (!str_detect(pathway,"TCELL")))) %>%
# View()
#SCTall_fil2_tcells_timepoint_SCT_gsea_C7_fil %>%
# mutate(avg_logFC = if_else(cluster=="pre", avg_logFC*-1, avg_logFC)) %>%
# mutate(cluster = "post") ->
# SCTall_fil2_tcells_timepoint_SCT_gsea_C7_fil
SCTall_fil2_tcells_timepoint_SCT_gsea_C7_fil %>%
filter(dense_rank(NES) <= 10 | dense_rank(desc(NES)) <= 10) %>%
mutate(dir = if_else(NES >=0, "pos","neg")) %>%
ggplot() + aes(x=reorder(pathway, NES), y = NES, fill=dir) + geom_bar(stat = "identity") +
scale_fill_manual(values = c("steelblue","firebrick")) +
theme_minimal() + theme(axis.title.y = element_blank()) +
scale_y_continuous(limits = c(-3,3), expand = c(0,0)) +
theme(axis.line.x = element_line(size=0.5)) +
theme(axis.title.x = element_text(size=12), axis.text=element_text(size=8)) +
theme(panel.grid = element_blank(), legend.position = "none") + coord_flip() ->
tcells_gsea_plot
ggsave(tcells_gsea_plot, filename = "./plots/fig_pieces_01/tcells_gsea_plot.pdf", width=10, height=5)
SCTall_fil2_tcells_timepoint_SCT_CD4_gsea_C7_fil %>%
filter(dense_rank(NES) <= 10 | dense_rank(desc(NES)) <= 10) %>%
mutate(dir = if_else(NES >=0, "pos","neg")) %>%
ggplot() + aes(x=reorder(pathway, NES), y = NES, fill=dir) + geom_bar(stat = "identity") +
scale_fill_manual(values = c("steelblue","firebrick")) +
theme_minimal() + theme(axis.title.y = element_blank()) +
scale_y_continuous(limits = c(-3,3), expand = c(0,0)) +
theme(axis.line.x = element_line(size=0.5)) +
theme(axis.title.x = element_text(size=12), axis.text=element_text(size=8)) +
theme(panel.grid = element_blank(), legend.position = "none") + coord_flip()
#SCTall_fil2_tcells_patient_timepoint_SCT_gsea_C2 = lapply(split(SCTall_fil2_tcells_patient_timepoint_SCT_markers_fix, SCTall_fil2_tcells_patient_timepoint_SCT_markers_fix$patient), function(x){rungsea(x, cats = "C2", fc_cutoff = 0.2)})
#SCTall_fil2_tcells_patient_timepoint_SCT_gsea_C5 = lapply(split(SCTall_fil2_tcells_patient_timepoint_SCT_markers_fix, SCTall_fil2_tcells_patient_timepoint_SCT_markers_fix$patient), function(x){rungsea(x, cats = "C5", fc_cutoff = 0.2)})
#SCTall_fil2_tcells_patient_timepoint_SCT_gsea_C7 = lapply(split(SCTall_fil2_tcells_patient_timepoint_SCT_markers_fix, SCTall_fil2_tcells_patient_timepoint_SCT_markers_fix$patient), function(x){rungsea(x, cats = "C7", fc_cutoff = 0.2)})
#bind_rows(SCTall_fil2_tcells_patient_timepoint_SCT_gsea_C7, .id = "patient") %>%
# filter(NES <= -2) %>%
# filter((str_detect(pathway, "TCELL")|str_detect(pathway, "TREG")|str_detect(pathway, "EFF")|str_detect(pathway, "CONV")|str_detect(pathway, "MEM")|str_detect(pathway, "CD[48]"))) %>%
# filter(!(str_detect(pathway, "DC"))) %>%
# filter(!(str_detect(pathway, "BCELL") & (!str_detect(pathway,"TCELL")))) %>%
# group_by(pathway, cluster) %>%
# summarise(n=n()) %>%
# View()
#SCTall_fil2_tcells_patient_timepoint_SCT_markers_fix %>%
# group_by(gene) %>%
# summarise(m = mean(avg_logFC), s = sd(avg_logFC)) %>%
# View()
#SCTall_fil2_tcells_timepoint_SCT_markers_fil %>%
# ggplot() + aes(x = avg_logFC, y = -log10(p_val_adj)) + geom_point()
#SCTall_fil2_tcells_timepoint_RNA_markers %>%
# ggplot() + aes(x = avg_logFC, y = -log10(p_val_adj)) + geom_point()
##############
####CD4 imt###
##############
#p = 0.00105
#IMT_TME_merged %>%
# mutate(timepoint = factor(timepoint, levels = c("pre","post"))) %>%
# filter(marker == "CD4") %>%
# ggplot() + aes(x=timepoint, y=density) +
## ylab(expression('CD4'^'+'~'/mm'^2)) +
# geom_boxplot() +
# geom_point(color="grey40", size=2, aes(group=patient)) +
# geom_line(color="grey40", size=0.5, aes(group=patient)) +
# scale_y_continuous(limits = c(0,2000)) +
# theme_one_line
IMT_TME_merged %>%
mutate(timepoint = factor(timepoint, levels = c("pre","post"))) %>%
filter(marker == "CD4") %>%
ggpaired(x = "timepoint", y = "density",
color = "timepoint", line.color = "black", point.size = 2,
line.size = 0.5,palette = c("black","black")) +
#stat_compare_means(paired = TRUE) +
ylab(expression('CD4'^'+'*'/mm'^2)) +
theme_one_line + theme(legend.position = "none") ->
CD4_IHC_plot
ggsave(CD4_IHC_plot, filename = ".fig/plots/fig_pieces_01/CD4_IHC_plot.pdf", width=2.5, height=3, device = cairo_pdf)
IMT_TME_merged %>%
mutate(timepoint = factor(timepoint, levels = c("pre","post"))) %>%
filter(marker == "CD4") %>%
tidyr::spread(timepoint, density) %>%
summarise(ttest = list(t.test(pre, post,paired = TRUE))) %>%
pull(ttest) ->
temp
############################
####tcell lineplot, vert####
############################
SCTall_fil2@meta.data %>%
filter(major_clusters %in% c("B - Cells", "Myeloid","T - Cells", "NK Cells")) %>%