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Figure2.Rmd
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---
title: "Cell composition - supplementary"
author: "E Onur Karakaslar, Lori D Kregar, Ahrim Youn, ..., Duygu Ucar"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
# Prepare the environment
```{r libraries, include=TRUE, echo=F}
library(dplyr)
library(readxl)
library(glmnet)
library(ggpubr)
library(ggplot2)
library(writexl)
library(pheatmap)
library(circlize)
library(reshape2)
library(DescTools)
library(patchwork)
library(colorspace)
library(ComplexHeatmap)
require(knitr)
knitr::opts_chunk$set(echo = TRUE)
opts_knit$set(root.dir = "/Users/karako/Dropbox (JAX)/MouseAging_clean/NatureAgingSubmission/") #set root dir!
```
```{r import, echo=TRUE, include=TRUE}
source("code/library_function_wf_flow.r")
source("code/PVCA_wf.r")
load("data/flow/flow_data_table.Rdata")
color.palette <- paletteer::paletteer_d("ggsci::nrc_npg")
plots_in_pdf <<- TRUE
q_value_cutoff <- 0.05
```
```{r flow_raw_table, include=TRUE, echo=TRUE}
#### Ahrim's Data #####
# # to spleen
# flow[flow=="SPL"]="spleen"
#
# # convert weeks to months
# flow[,4]=round(as.numeric(flow[,4])*7/30)
# flow[flow[,4]==13,4]=12
# flow[flow[,4]==17,4]=18
#
# # remove bone marrow
# flow=flow[flow[,"tissue"]!="BM",]
#### Onur Data ####
flow <- read_excel("data/flow/raw/Flow_Data_Merged_v2.xlsx")
flow[flow=="SPL"]="spleen"
flow[,"Age"] <- round((flow[,"Age"]) * 7/30)
flow[flow[,"Age"] == 13, "Age"]=12
flow[flow[,"Age"] == 17, "Age"]=18
meta <- flow [,1:4] # Tissue, Strain, Sex, Age
flow <- flow %>% as.matrix
meta <- meta %>% as.matrix
colnames(flow)[colnames(flow)=="Strain"] ="type"
write_xlsx(data.frame(flow), path = "output/F2/raw_data_flow_cell_percentages.xlsx",
col_names = TRUE)
```
# Supplementary figure 1: changes in PBL and spleen
```{r percentage_plots_suplementary, include=TRUE, echo=TRUE}
# change colnames
colnames(flow)[colnames(flow)=="B.Viable"]="B"
colnames(flow)[colnames(flow)=="CD4.Viable"]="CD4"
colnames(flow)[colnames(flow)=="CD8.Viable"]="CD8"
### get rid of problematic middle age samples
flow <- flow[flow[,"Age"]!=6 ,]
### get rid of CD38 and percentage of viable cells ###
# colnames(flow)[c(9:11,31:41,53:56)]
# [1] "B.Lymphocytes" "CD4.Lymphocytes"
# [3] "CD8.Lymphocytes" "CD38.B"
# [5] "CD38.CD4" "CD38.CD8"
# [7] "CD38.CD4.Naive" "CD38.CD4.CM"
# [9] "CD38.CD4.EM" "CD38.CD4.EMRA"
# [11] "CD38.CD8.Naive" "CD38.CD8.CM"
# [13] "CD38.CD8.EM" "CD38.CD8.EMRA"
# [15] "X..Viable" "Single.Cells.Viable.Cells.Granulocytes.Fre.Viable.Cells"
# [17] "Single.Cells.Viable.Cells.No.Granulocytes.Monocytes.Fre.Parent" "Single.Cells.Viable.Cells.No.Granulocytes.Monocytes.Fre.Viable.Cells"
# Remove unnecessary cell proportions
flow <- flow[,-c(9:11,31:41,53:56)]
meta <- flow[,1:5]
meta.types <- colnames(meta)
cell.types <- colnames(flow[,6:ncol(flow)])
colnames(meta) <- c("TISSUE","STRAIN","GENDER","AGE","SAMPLEID")
dat <- as.matrix(flow[,cell.types])
dat <- matrix(as.numeric(dat),nrow = nrow(dat),ncol = ncol(dat))
colnames(dat) <- cell.types
# PCA and PVCA
# res=PVCA(t(dat[rowSums(is.na(dat))==0,]),meta[rowSums(is.na(dat))==0,1:4],0.9)
# #pdf(file="../results/PVCA_flow.pdf")
# PlotPVCA(res, "")
#
# # plots prinicpal components for flow data, colored by gender
# PCA(t(dat[rowSums(is.na(dat))==0,]),meta[rowSums(is.na(dat))==0,1:4])
bed <- t(dat[rowSums(is.na(dat))==0,])
bed <- bed[-(1:3),]
meta=meta[rowSums(is.na(dat))==0,]
TISSUE=meta[,1]
STRAIN=meta[,2]
GENDER=meta[,3]
AGE=as.numeric(meta[,4])
SAMPLEMOUSEID=meta[,5]
###run until here in order to generate the bed files and other information needed for age_prediction.r #############
TRANSFORM=F
dat=as.data.frame(flow)
dat=dat[rowSums(is.na(dat))==0,]
for(i in c(4,6:ncol(dat))) dat[,i]=as.numeric(as.matrix(dat[,i]))
#dim(flow)
# add flow cd4/cd8 ratio
dat$CD4.CD8.ratio=as.numeric(dat[,"CD4"])/as.numeric(dat[,"CD8"])
# the following doesn't actually remove anything from flow
dat=dat[!is.na(dat[,2]),]
# keep the original data for later use
stored_dat <- dat
# draw global plot of cell proportions #
convert.numeric <- function(x){
x[x==3]="03"
x[x==6]="06"
return(x)
}
dat0=dat
dat0$new=paste(convert.numeric(dat0$Age),as.character(dat0$Sample),sep="-")
dat0$new=(dat0$Age)
# There are few BM samples therefore removed from the analyses.
dat0=dat0[dat0[,"Tissue"]!="BM",]
stored_dat0 <- dat0
subplot.by.tissue <- function(dat0,subset,title_,seltissue, mouse1 = FALSE){
tmp <- cbind("Major",dat0[,c("Tissue","type","Sex","Age",subset ,"new")])
names(tmp)[1] <- "category"
tmp2 <- cbind("IL7R+", dat0[,c("Tissue","type","Sex","Age",paste("IL7R",subset,sep=".") ,"new")])
tmp3 <- cbind("PD1+", dat0[,c("Tissue","type","Sex","Age",paste("PD1",subset,sep=".") ,"new")])
names(tmp2) <- names(tmp3) <- names(tmp)
tmp_all <- rbind(tmp,tmp2,tmp3)
tmp_all <- tmp_all[tmp_all$Tissue==seltissue,]
tmp_all$Tissue <- tmp_all$category
ggdf <- reshape2::melt(tmp_all,id.vars = c("Tissue","type","Sex","new"),measure.vars=subset)
colnames(ggdf)[grep("type", colnames(ggdf))] <- "Strain"
ggdf$variable <- fomat_variable_names_cd_cells(ggdf$variable)
p1 <- subplot(ggdf,title_, mouse1 = FALSE)
return(p1)
}
p1 <- subplot.by.tissue(dat0, c("B","CD4","CD8"),
"% of major cell types in PBL","PBL", mouse1 = FALSE)
p2 <- subplot.by.tissue(dat0, c("CD4.Naive","CD4.CM","CD4.EM","CD4.EMRA"),
"% of CD4+ T-cells in PBL","PBL", mouse1 = FALSE)
p3 <- subplot.by.tissue(dat0, c("CD8.Naive","CD8.CM","CD8.EM","CD8.EMRA"),
"% of CD8+ T-cells in PBL","PBL", mouse1 = FALSE)
p4 <- subplot.by.tissue(dat0, c("B","CD4","CD8"),"% of major cell types in spleen",
"spleen", mouse1 = FALSE)
p5 <- subplot.by.tissue(dat0, c("CD4.Naive","CD4.CM","CD4.EM","CD4.EMRA"),
"% of CD4+ T-cells in spleen","spleen", mouse1 = FALSE)
p6 <- subplot.by.tissue(dat0, c("CD8.Naive","CD8.CM","CD8.EM","CD8.EMRA"),
"% of CD8+ T-cells in spleen","spleen", mouse1 = FALSE)
# if(plots_in_pdf){
# pdf("output/F2/percentage_of_cells_flow_mice_trend_72.pdf",
# height = 10, width = 15)
# }
# multiplot(p1 + guides(col = FALSE),
# p4 + guides(col = FALSE),
# p2 + guides(col = FALSE),
# p5 + theme(legend.position="bottom", legend.direction="vertical",),
# p3 + guides(col = FALSE),
# p6 + guides(col = FALSE),
# cols=3)
# if(plots_in_pdf){
# dev.off()
# }
# flow_panels_all_separate <-
# {p1 + guides(col = FALSE)} +
# {p2 + guides(col = FALSE)} +
# {p3 + guides(col = FALSE)} +
# {p4 + guides(col = FALSE)} +
# {p5 + theme(legend.position="bottom", legend.direction="horizontal")} +
# {p6 + guides(col = FALSE)} +
# plot_layout(ncol = 3)
flow_panels_all_separate <-
{p1 + theme(legend.position="none")} +
{p2 + theme(legend.position="none")} +
{p3 + theme(legend.position="none")} +
{p4 + theme(legend.position="none")} +
{p5 + theme(legend.position="bottom", legend.direction="horizontal")} +
{p6 + theme(legend.position="none")} +
plot_layout(ncol = 3)
if(plots_in_pdf){
ggsave("output/F2/flow_panels_all_separate.pdf",
plot = flow_panels_all_separate,
height = 10,
width = 13,
units = "in",
useDingbats = FALSE)
ggsave("output/F2/flow_panels_all_separate.png",
plot = flow_panels_all_separate,
height = 10,
width = 13,
units = "in",
dpi = 300)
}else{
print(flow_panels_all_separate)
}
```
```{r PIE CHART}
main.cell.types <- c("B", "CD4", "CD8")
flow.main <- as.data.frame(flow)
flow.main <- apply(flow.main[-c(1:3)],2, as.numeric) %>% as.data.frame
flow.main <- cbind(flow[,c(1:3)] , flow.main)
flow.main$Monocytes <- (100 - rowSums(flow.main[,main.cell.types]))
df.pie <- flow.main[,c("Tissue", "type", "Age", main.cell.types, "Monocytes")] %>%
melt (id.vars = c("Tissue", "type", "Age"), variable.name = "cell.type")
library(broom)
library(tidyverse)
t.test.strain <- df.pie %>%
filter(Age != 12) %>%
group_by(cell.type, Tissue, Age, type) %>%
nest() %>%
spread(key = type, value = data) %>%
mutate(
t_test = map2(B6, NZO, ~{t.test(.x$value, .y$value, var.equal = T) %>% tidy()}),
B6 = map(B6, nrow),
NZO = map(NZO, nrow)
) %>%
unnest(cols = c(B6, NZO, t_test))
t.test.tissue <- df.pie %>%
filter(Age != 12) %>%
group_by(cell.type, Tissue, Age, type) %>%
nest() %>%
spread(key = Tissue, value = data) %>%
mutate(
t_test = map2(spleen, PBL, ~{t.test(.x$value, .y$value, var.equal = T) %>% tidy()}),
spleen = map(spleen, nrow),
PBL = map(PBL, nrow)
) %>%
unnest(cols = c(spleen, PBL, t_test))
write_xlsx(list(Tissue_comparison = data.frame(t.test.tissue),
Strain_comparison = data.frame(t.test.strain)), path = "output/F2/t_test_pie_charts.xlsx")
df.pie.means <- df.pie %>% group_by(cell.type ,Age, type, Tissue) %>% summarize(value = sum(value)/n()) %>% filter(Age != 12)
# df.pie.means %>% group_by(Tissue, cell.type) %>% summarize(value = sum(value)/n())
levels(df.pie.means$Tissue) <- c("PBL", "Spleen")
plot.cell.type.pie <- ggplot(df.pie.means, aes(x =1, y = value, fill = cell.type)) +
geom_bar(stat = "identity", width=1, color="white") +
facet_wrap(~ type + Tissue + Age, nrow = 2, strip.position = "bottom") +
coord_polar("y") + theme_void(base_size = 16) +
scale_fill_manual(values = color.palette) +
xlab("") + ylab("") + labs(fill = "Cell Type")
ggsave(plot = plot.cell.type.pie, filename = "output/F2/main_cell_types_pie.pdf", useDingbats = F,
width = 7, height = 5)
plot.cell.type.pie.minimal <- plot.cell.type.pie + theme_minimal()
ggsave(plot = plot.cell.type.pie.minimal,
filename = "output/F2/main_cell_types_pie_minimal_theme.pdf", useDingbats = F,
width = 7, height = 5)
```
```{r produce_flow_table, include=TRUE, echo=TRUE}
xls_table <- dat0
colnames(xls_table)[2] <- "strain"
colnames(xls_table) <- sapply(colnames(xls_table), function(c_name){
#c_name <- colnames(xls_table)[1]
first <- toupper(substr(c_name, 1, 1))
with_capital <- paste0(first, substr(c_name, 2, nchar(c_name)))
return(with_capital)
})
xls_table$new <- NULL
write_xlsx(xls_table, path = "output/F2/flow_cell_percentages.xlsx",
col_names = TRUE)
```
```{r anoter_panel_for_flow_trends, include=TRUE, echo=TRUE}
flow_panels <- dat0
colnames(flow_panels)[6:16] <- paste0("major.", colnames(flow_panels)[6:16])
```
```{r extract_major_b6_spleen_cd8cm, include=TRUE, echo=TRUE}
cd8cm <- subplot.by.tissue(dat0, c("CD8.CM"),
"% of major cell types in PBL","PBL", mouse1 = FALSE)
just_major <- as.data.frame(cd8cm$data %>% filter(Tissue == "Major"))
cd8cmplot <- ggplot(just_major, aes(x = new, y = value, group = new, color = Strain)) +
geom_bar(width = 6) +
geom_jitter(width = 0.1, height = 0.1, na.rm = TRUE, size = 3) +
# geom_point() +
facet_wrap(~Strain) +
stat_compare_means(method = "t.test") +
theme_pubr() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5)) +
xlab("") +
ylab("") +
# ggtitle("CD8+ CM T-cells from\nmajor in PBL") +
geom_smooth(method = "lm", se = FALSE,size=1, na.rm = TRUE) +
scale_x_continuous(breaks = c(3,18)) +
scale_color_manual(values = color_values)
cd8cmplot
ggsave(plot = cd8cmplot,
filename = "output/F2/cd8cd_slope.pdf",
height = 4, width = 4, useDingbats = FALSE)
ggsave(plot = cd8cmplot,
filename = "output/F2/cd8cd_slope.png",
height = 4, width = 4, dpi = 300)
```
```{r extract_major_b6_spleen_cd8em, include=TRUE, echo=TRUE}
cd8em <- subplot.by.tissue(dat0,c("CD8.EM"),"Percentage of CD8+ T-cells in PBL","PBL", mouse1 = FALSE)
just_major <- as.data.frame(cd8em$data %>% filter(Tissue == "Major"))
cd8emplot <- ggplot(just_major, aes(x = new, group =new, y = value, color = Strain)) +
geom_boxplot() +
geom_jitter(width = 0.1, height = 0.1, na.rm = TRUE, size = 3) +
theme_pubr(base_size = 15, base_family = "sans") +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5)) +
xlab("") +
ylab("") +
ggtitle("CD8+ EM T-cells in PBL") +
geom_smooth(method = "lm", se = FALSE,size=1, na.rm = TRUE) +
scale_x_continuous(breaks = c(3,12,18)) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) +
ylim(c(0,100)) +
theme(legend.position = "none") +
scale_color_manual(values = color_values) +
facet_wrap(~ Strain)
signif(summary(lm(value ~ new, data= just_major %>%filter(Strain == "NZO")))$coef[2,4],1)
signif(summary(lm(value ~ new, data= just_major %>%filter(Strain == "B6")))$coef[2,4], 1)
ggsave(plot = cd8emplot,
filename = "output/F2/cd8em_slope.pdf",
height = 4, width = 4, useDingbats = FALSE)
ggsave(plot = cd8emplot,
filename = "output/F2/cd8em_slope.png",
height = 4, width = 4, dpi = 300)
```
```{r extract_major_b6_spleen_cd8em, include=TRUE, echo=TRUE}
cd8em <- subplot.by.tissue(dat0,c("CD8.EM"),"Percentage of CD8+ T-cells in spleen","spleen", mouse1 = FALSE)
just_major <- as.data.frame(cd8em$data %>% filter(Tissue == "Major"))
cd8emplot <- ggplot(just_major, aes(x = new, group = new, y = value, color = Strain)) +
geom_boxplot(width = 6) +
geom_jitter(width = 0.1, height = 0.1, na.rm = TRUE, size = 3) +
theme_pubr(base_size = 15, base_family = "sans") +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5)) +
xlab("") +
ylab("") +
ggtitle("CD8+ EM T-cells in PBL") +
geom_smooth(method = "lm", se = FALSE,size=1, na.rm = TRUE) +
scale_x_continuous(breaks = c(3,12,18)) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) +
ylim(c(0,100)) +
theme(legend.position = "none") +
scale_color_manual(values = color_values) +
facet_wrap(~Strain)
signif(summary(lm(value ~ new, data= just_major %>%filter(Strain == "NZO")))$coef[2,4],1)
signif(summary(lm(value ~ new, data= just_major %>%filter(Strain == "B6")))$coef[2,4], 1)
ggsave(plot = cd8emplot,
filename = "output/F2/cd8em_slope_spleen.pdf",
height = 4, width = 4, useDingbats = FALSE)
ggsave(plot = cd8emplot,
filename = "output/F2/cd8em_slope_spleen.png",
height = 4, width = 4, dpi = 300)
```
```{r extract_major_b6_spleen_c4naive, include=TRUE, echo=TRUE}
cd4naive <- subplot.by.tissue(dat0,c("CD4.Naive"),"Percentage of CD4+ T-cells in PBL","PBL", mouse1 = FALSE)
just_major.naive <- as.data.frame(cd4naive$data %>% filter(Tissue == "Major"))
signif(summary(lm(value ~ new, data= just_major.naive %>%filter(Strain == "NZO")))$coef[2,4],1)
signif(summary(lm(value ~ new, data= just_major.naive %>%filter(Strain == "B6")))$coef[2,4], 1)
cd4.naive.plot <- ggplot(just_major.naive, aes(x = new, y = value, color = Strain)) +
geom_jitter(width = 0.1, height = 0.1, na.rm = TRUE, size = 3) +
theme_pubr(base_size = 15) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5)) +
xlab("") +
ylab("") +
ggtitle("CD4+ Naive T-cells in PBL") +
geom_smooth(method = "lm", se = FALSE,size=1, na.rm = TRUE) +
scale_x_continuous(breaks = c(3,12,18)) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) +
ylim(c(0,100)) +
theme(legend.position = "none") +
scale_color_manual(values = color_values)
ggsave(plot = cd4.naive.plot,
filename = "output/F2/cd4naive_slope.pdf",
height = 4, width = 4, useDingbats = FALSE)
ggsave(plot = cd4.naive.plot,
filename = "output/F2/cd4naive_slope.png",
height = 4, width = 4, dpi = 300)
```
```{r extract_major_b6_spleen_c4naive, include=TRUE, echo=TRUE}
cd4naive <- subplot.by.tissue(dat0,c("CD4.Naive"),"Percentage of CD4+ T-cells in PBL","spleen", mouse1 = FALSE)
just_major.naive <- as.data.frame(cd4naive$data %>% filter(Tissue == "Major"))
signif(summary(lm(value ~ new, data= just_major.naive %>%filter(Strain == "NZO")))$coef[2,4],1)
signif(summary(lm(value ~ new, data= just_major.naive %>%filter(Strain == "B6")))$coef[2,4], 1)
cd4.naive.plot <- ggplot(just_major.naive, aes(x = new, y = value, color = Strain)) +
geom_jitter(width = 0.1, height = 0.1, na.rm = TRUE, size = 3) +
theme_pubr(base_size = 15) +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5)) +
xlab("") +
ylab("") +
ggtitle("CD4+ Naive T-cells in Spleen") +
geom_smooth(method = "lm", se = FALSE,size=1, na.rm = TRUE) +
scale_x_continuous(breaks = c(3,12,18)) +
theme(axis.text.x = element_text(angle = 0, hjust = 0.5)) +
ylim(c(0,100)) +
theme(legend.position = "none") +
scale_color_manual(values = color_values)
ggsave(plot = cd4.naive.plot,
filename = "output/F2/cd4naive_slope_spleen.pdf",
height = 4, width = 4, useDingbats = FALSE)
ggsave(plot = cd4.naive.plot,
filename = "output/F2/cd4naive_slope_spleen.png",
height = 4, width = 4, dpi = 300)
```
```{r extract_all_majors, include=TRUE, echo=TRUE}
plot_cell_pop <- function(cell_pop, inputs){
#cell_pop <- "Major"
#inputs <- all_ps
extract_data <- lapply(inputs, function(x, y){
#x <- inputs[[1]]
dataf <- x$data %>% filter(Tissue == y)
dataf <- cbind(dataf, title_name = x$labels$title)
dataf$source <- ifelse(grepl("PBL", dataf$title_name), "PBL", "Spleen")
return(dataf)
}, y = cell_pop)
all_major_in_df <- do.call("rbind", extract_data)
colnames(all_major_in_df)[1] <- "Cell population" #rename "tissue" to "cell population"
colnames(all_major_in_df)[8] <- "Tissue" # rename "source" to "tissue"
#unique(all_major_in_df$variable)
all_major_in_df$offset <- 0
for(i in 1:nrow(all_major_in_df)){
if(all_major_in_df[i,"variable"] == "B"){
all_major_in_df$offset[i] <- 1
}else if(grepl("\\+$", all_major_in_df$variable[i])){
all_major_in_df$offset[i] <- 2
}else if(grepl("CM$", all_major_in_df$variable[i])){
all_major_in_df$offset[i] <- 3
}else if(grepl("EM$", all_major_in_df$variable[i])){
all_major_in_df$offset[i] <- 4
}else if(grepl("EMRA$", all_major_in_df$variable[i])){
all_major_in_df$offset[i] <- 5
}else if(grepl("naive$", all_major_in_df$variable[i])){
all_major_in_df$offset[i] <- 6
}
}
#all_major_in_df$offset <- ifelse(grepl("B", all_major_in_df$variable))
shapes <- c(1, 16)
names(shapes) <- c("Spleen", "PBL")
linetypes <- c("solid", "22")
names(linetypes) <- c("PBL", "Spleen")
#all_major_in_df$row <- ifelse(grepl("4|B", all_major_in_df$variable), "t", "b")
#all_major_in_df$offset <- factor(all_major_in_df$offset,
# levels = sort(unique(all_major_in_df$offset)),
# ordered = TRUE)
all_trend <- ggplot(all_major_in_df, aes(x = new,
y = value,
color = Strain)) +
geom_jitter(width = 0.1, height = 0.1, na.rm = TRUE,
aes(shape = Tissue)) +
facet_wrap(~variable, nrow = 2, scales = "free_x") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.background = element_blank(),
legend.position="bottom",
#panel.spacing = unit(2, "mm"),
panel.spacing.x=unit(1.5, "mm"),
panel.spacing.y=unit(0.5, "mm"),
legend.margin = margin(rep(0, 4)),
legend.box.margin=margin(c(-8, rep(-5, 3)))
#plot.margin = margin(rep(0,4)),
#legend.spacing.x = unit(0.5, "cm"),
#legend.spacing.y = unit(1,"cm")
) +
xlab("Age (in months)") +
ylab("Percentage") +
ggtitle("Cellular composition in PBL and spleen",
subtitle = paste0(cell_pop, " cell population")) +
geom_smooth(method = "lm",
se = FALSE,
size=1,
na.rm = TRUE,
#color = "black",
aes(linetype = Tissue)) +
scale_x_continuous(breaks = c(3,12,18)) +
scale_color_manual(values = color_values) +
scale_fill_manual(values = color_values) +
scale_shape_manual(values = shapes) +
scale_linetype_manual(values = linetypes) #+
# guides(color=guide_legend(
# #margin(rep(0.5, 4))
# keywidth=0.1,
# keyheight=0.1,
# default.unit="inch"
# )
# )
if(plots_in_pdf){
ggsave(filename = paste0("output/F2/", cell_pop, "_cell_with_age.pdf"),
plot = all_trend,
height = 6, width = 8, useDingbats = FALSE)
ggsave(filename = paste0("output/F2/", cell_pop, "_cell_with_age.png"),
plot = all_trend,
height = 6, width = 8, dpi = 300)
}else{
print(all_trend)
}
}
all_ps <- list(p1, p2, p3, p4, p5, p6)
major_trends <- plot_cell_pop("Major", all_ps)
il7r_trends <- plot_cell_pop("IL7R+", all_ps)
pd1_trends <- plot_cell_pop("PD1+", all_ps)
```
```{r change_with_aging_per_tissue, include=TRUE, echo=TRUE}
orig.dat <- dat <- stored_dat
store_hm_matrices <- vector("list", 2)
coefficients_matrix <- vector("list", 2)
idx_rud <- 0
for(analyze.spleen in c(TRUE,FALSE)){
idx_rud <- idx_rud + 1
if(analyze.spleen){
dat=orig.dat[orig.dat[,"Tissue"]=="spleen",]
tissue_now <-"spleen"
}else{
dat=orig.dat[orig.dat[,"Tissue"]=="PBL",]
tissue_now <-"PBL"
}
if(TRANSFORM){
for(i in 6:ncol(dat)){
dat[,i]=transform(as.numeric(as.matrix(dat[,i])))
}
}
# Add cd4/cd8 ratio
cell.types.all <- colnames(dat)[6:ncol(dat)]
# An example -- B cell composition assocation via age, strain and their interactions
exmp=signif(summary(lm(dat[,"B"]~dat$Age*dat$type))$coef[,1],2) %>% as.data.frame
# Create empty matrix
percent.vs.type.interaction.coef <- percent.vs.type.interaction.p <-
matrix(NA,nrow = ncol(dat)-5,ncol =nrow(exmp))
colnames(percent.vs.type.interaction.coef) <- colnames(percent.vs.type.interaction.p) <-
rownames(exmp)
rownames(percent.vs.type.interaction.coef) <- rownames(percent.vs.type.interaction.p) <-
cell.types.all
for(i in cell.types.all){
# temp is a column from the matrix summary of the linear model, it
# contains the Beta values of the model ("Estimate")
# It's values are: (Intercept) dat$Age dat$typeNZO dat$Age:dat$typeNZO
temp=signif(summary(lm(dat[,i]~dat$Age*dat$type))$coef[,1],2)
percent.vs.type.interaction.coef[i,names(temp)]=temp
# this temp will contain the p-values for the model: Pr(>|t|)
temp=signif(summary(lm(dat[,i]~dat$Age*dat$type))$coef[,4],2)
percent.vs.type.interaction.p[i,names(temp)]=temp
}
### variables that have significant interaction
# temp=rowSums(percent.vs.type.interaction.p[,c("dat$typeNZO","dat$Age:dat$typeNZO")]<0.005)>0
# CHANGED FIXED BOUNDARY FOR FDR ADJUSTMENT AND CUTOFF at fdr cut off
by_type <- p.adjust(percent.vs.type.interaction.p[,c("dat$typeNZO")], "fdr")
by_type_and_age <- p.adjust(percent.vs.type.interaction.p[,c("dat$Age:dat$typeNZO")], "fdr")
temp=rowSums(cbind((by_type < q_value_cutoff), (by_type_and_age < q_value_cutoff))) > 0
sel=percent.vs.type.interaction.p[temp,1]
# dat0=dat[,c( "tissue","type","Sex","age","Sample", sel )]
dat0=dat
datF=dat0[dat0$Sex=="F",]
datM=dat0[dat0$Sex=="M",]
datB6=dat0[dat0$type=="B6",]
datNZO=dat0[dat0$type=="NZO",]
# Fit linear model for AGE only
R5=regression(datB6)
R6=regression(datNZO)
if(analyze.spleen){
B6.spleen=R5
NZO.spleen=R6
}else{
B6.PBL=R5
NZO.PBL=R6
}
### test if the intercept and slope for aging effect is different between gender and strain. For the column "M-F in B6” , 1 means slope/intercept for male is larger than female, -1 means the opposite and blank means that there is no significant difference
# aging_coef is the lm coefficient of cell type ~ age
maineffect.coef=cbind(R5[,"aging_coef"],R6[,"aging_coef"])
colnames(maineffect.coef) <- c("B6", "NZO")
maineffect.p=cbind(R5[,"aging_p"],R6[,"aging_p"])
colnames(maineffect.coef) = colnames(maineffect.p) <- c("B6", "NZO")
x=sign(maineffect.coef)*maineffect.p
colnames(x)=c("B6","NZO")
coefficients_matrix[[idx_rud]] <- list(coef = maineffect.coef,
p = maineffect.p)
names(coefficients_matrix)[idx_rud] <- tissue_now
# x0 is a table of p-values where the sign corresponds to the sign of coefficent
x0=x
var=c("dat$Age:dat$typeNZO")
x=sign(percent.vs.type.interaction.coef[,var])*percent.vs.type.interaction.p[,var]
# x is a table of p-values (signed according to coeffcient[,var])*percent.vs.type.interaction.p[,var]
#x[,c(2,4)]= -x[,c(2,4)]
x=cbind(x)
colnames(x)=c("NZO-B6")
x=cbind(x0,x)
store_hm_matrices[[idx_rud]] <- x
names(store_hm_matrices)[idx_rud] <- tissue_now
# with p values
coef.heatmap(x, paste("Change with aging in",dat[1,"tissue"]),
path_to_File = paste0("output/F2/heatmap_flow_",
dat[1,"tissue"],"_72.pdf"),
plots_in_pdf = TRUE, -1)
# with dots
coef.heatmap(x, paste("Change with aging in",dat[1,"tissue"]),
path_to_File = paste0("output/F2/heatmap_flow_",
dat[1,"tissue"],"_points_72.pdf"),
plots_in_pdf = TRUE, 16)
# with significant slopes
coef.heatmap(x, paste("Change with aging in",dat[1,"tissue"]),
path_to_File = paste0("output/F2/heatmap_flow_",
dat[1,"tissue"],"_slopes_72.pdf"),
plots_in_pdf = TRUE, -1, main_c = maineffect.coef,
main_p = maineffect.p, drop_third = TRUE)
}
```
```{r extraextra}
library(dplyr)
dat %>% group_by(Tissue) %>% summarise(meany = mean(B, na.rm = TRUE), sdy = sd(B, na.rm = TRUE))
dat %>% group_by(type) %>% summarise(meany = mean(B, na.rm = TRUE), sdy = sd(B, na.rm = TRUE))
```
```{r matrices_side_by_side, include=TRUE, echo=TRUE}
coef.heatmap_both(store_hm_matrices,
path_to_File = "output/F2/heatmap_flow_both_72.pdf",
plots_in_pdf = TRUE,
with_symbols = -1)
coef.heatmap_both(store_hm_matrices,
path_to_File = "output/F2/heatmap_flow_both_points_72.pdf",
plots_in_pdf = TRUE,
with_symbols = 16)
coef.heatmap_both(store_hm_matrices,
path_to_File = "output/F2/heatmap_flow_both_slope_72.pdf",
plots_in_pdf = TRUE,
with_symbols = -1,
drop_third = TRUE,
coef_both = coefficients_matrix)
coef.heatmap_both(store_hm_matrices,
path_to_File = "output/F2/heatmap_flow_both_points_72_only2.pdf",
plots_in_pdf = TRUE,
with_symbols = 16,
drop_third = TRUE,
coef_both = coefficients_matrix)
```
```{r correlations, include=TRUE, echo=TRUE}
ggpoint <- function(x,y,xl,yl,title){
temp=cor.test(x,y)
minx=min(x,na.rm=T)+0.5
maxy=max(y,na.rm=T)
dat=data.frame(x=x,y=y,type=names(x))
p <- ggplot(dat, aes(x,y,color=type)) +
geom_point() +
labs(x=xl,y=yl,title=title) +
annotate("text", label = paste("r=",signif(temp$estimate,2)),
x = minx, y = maxy, size = 5, colour ="red") +
theme(legend.text=element_text(size=7)) +
geom_vline(xintercept=0,size=0.3) +
geom_hline(yintercept=0,size=0.3)
return(p)
}
#pdf(file="output/FC_comparison_flow.pdf",width=10)
find_correlation_per_celltype <- function(list_of_type_tissue,
cells,
cell_type,
only_significant = FALSE){
# list_of_type_tissue <- list(b6_spl = B6.spleen,
# b6_pbl = B6.PBL,
# nzo_spl = NZO.spleen,
# nzo_pbl = NZO.PBL)
# cells <- major.cell.id
# cells <- pd1.cell.id
# cells <- il7r.cell.id
filtered_dfs <- lapply(list_of_type_tissue, function(x, cell_rows){
x <- data.frame(x)
x$names <- rownames(x)
adjusted_ps <- p.adjust(x$aging_p, "fdr")
x <- cbind(x, adjusted_ps)
if(only_significant){
x <- x[which(x[,"adjusted_ps"] < 0.05),]
}
y <- x %>% filter(names %in% cell_rows)
return(y)
}, cell_rows = cells)
if(only_significant){
idx_spleen <- intersect(filtered_dfs[["b6_spl"]][["names"]],
filtered_dfs[["nzo_spl"]][["names"]])
idx_pbl <- intersect(filtered_dfs[["b6_pbl"]][["names"]],
filtered_dfs[["nzo_pbl"]][["names"]])
idx_b6 <- intersect(filtered_dfs[["b6_spl"]][["names"]],
filtered_dfs[["b6_pbl"]][["names"]])
idx_nzo <- intersect(filtered_dfs[["nzo_spl"]][["names"]],
filtered_dfs[["nzo_pbl"]][["names"]])
}else{
idx_spleen = idx_pbl = idx_b6 = idx_nzo <- cells
}
datasets_and_ind <- list(spleen = list("b6_spl", "nzo_spl", idx_spleen),
pbl = list("b6_pbl", "nzo_pbl", idx_pbl),
b6 = list("b6_pbl", "b6_spl", idx_b6),
nzo = list("nzo_spl", "nzo_pbl", idx_nzo))
correlations <- vector("numeric", 4)
names(correlations) <- names(datasets_and_ind)
for(i in 1:4){
ins <- datasets_and_ind[[i]]
x <- filtered_dfs[[ins[[1]]]] %>%
filter(names %in% ins[[3]]) %>%
select(aging_coef)
y <- filtered_dfs[[ins[[2]]]] %>%
filter(names %in% ins[[3]]) %>%
select(aging_coef)
correlations[i] <- cor(x,y)
}
var_and_val <- data.frame(Variable = names(correlations),
Correlation = correlations)
df_of_cor <- cbind(Cell_type = cell_type, var_and_val)
df_of_cor$Option <- ifelse(grepl("pbl|spleen|spl", df_of_cor$Variable),
"Tissue", "Strain")
df_of_cor <- df_of_cor[,c("Cell_type", "Variable", "Option", "Correlation")]
return(df_of_cor)
}
major.cell.id <- c("B", "CD4", "CD8", "CD4.Naive", "CD4.CM", "CD4.EM",
"CD4.EMRA", "CD8.Naive","CD8.CM", "CD8.EM","CD8.EMRA")
il7r.cell.id <- paste0("IL7R.", major.cell.id)
pd1.cell.id <- paste0("PD1.", major.cell.id)
list_of_dfs <- list(b6_spl = B6.spleen,
b6_pbl = B6.PBL,
nzo_spl = NZO.spleen,
nzo_pbl = NZO.PBL)
analyze_dfs_produce_correlations <- function(list_of_dfs,
filter_to_significant,
path_to_pdf,
produce_pdfs = TRUE,
narrow = -1){
major_df <- find_correlation_per_celltype(list_of_type_tissue =
list_of_dfs,
cells = major.cell.id,
cell_type = "Major",
only_significant =
filter_to_significant)
il7r_df <- find_correlation_per_celltype(list_of_type_tissue = list_of_dfs,
cells = il7r.cell.id,
cell_type = "IL7R+",
only_significant =
filter_to_significant)
pd1_df <- find_correlation_per_celltype(list_of_type_tissue = list_of_dfs,
cells = pd1.cell.id,
cell_type = "PD1+",
only_significant =
filter_to_significant)
correlations_df <- rbind(major_df, il7r_df, pd1_df)
correlations_df$Variable <- as.character(correlations_df$Variable)
correlations_df[grep("pbl|b6|nzo", correlations_df$Variable),"Variable"] <-
toupper(correlations_df[grep("pbl|b6|nzo",
correlations_df$Variable),"Variable"])
correlations_df$Cell_type <- factor(correlations_df$Cell_type,
levels = c("PD1+", "IL7R+", "Major"))
correlations_df$Option[correlations_df$Option == "Tissue"] <- "B6 vs NZO"
correlations_df$Option[correlations_df$Option == "Strain"] <- "PBL vs SPL"
p_all <- ggplot(data=correlations_df, aes(y=Correlation,