---
title: "Validation_NELL1_PDGFRA"
author: "Luise A. Seeker"
date: "03/08/2022"
output: html_document
---
```{r}
library(ggplot2)
library(lme4)
library(lmerTest)
library(here)
library(tidyr)
library(ggsci)
```
```{r, echo = F}
mypal <- pal_npg("nrc", alpha = 0.7)(10)
mypal2 <-pal_tron("legacy", alpha = 0.7)(7)
mypal3 <- pal_lancet("lanonc", alpha = 0.7)(9)
mypal4 <- pal_simpsons(palette = c("springfield"), alpha = 0.7)(16)
mypal5 <- pal_rickandmorty(palette = c("schwifty"), alpha = 0.7)(6)
mypal6 <- pal_futurama(palette = c("planetexpress"), alpha = 0.7)(5)
mypal7 <- pal_startrek(palette = c("uniform"), alpha = 0.7)(5)
mycoloursP<- c(mypal, mypal2, mypal3, mypal4, mypal5, mypal6, mypal7)
```
```{r}
nell_data <- read.csv(here("data",
"validation_data",
"PDGFRA_NELL1_count_data.csv"))
```
```{r}
names(nell_data)
nell_data$tissue <- as.factor(nell_data$tissue)
#nell_data$age_group <- as.factor(nell_data$age_group)
#nell_data$sex <- as.factor(nell_data$sex)
nell_data$sample_id <- paste(nell_data$donor,
nell_data$tissue,
sep = "_")
```
Convert from wide to long format to see how many single, double and triple
positive cells are in each sample
```{r}
keycol <- "sample_id"
valuecol <- "count"
gathercols <- c("PDRFRA_C1",
"NELL1_C2",
"PDGFRA_NELL1")
long_data <- gather(nell_data, keycol, valuecol, gathercols)
head(long_data)
nrow(long_data)
names(long_data)
```
```{r}
long_data$sample_id <- factor(long_data$sample_id, levels =
c("SD012_15_BA4",
"SD021_17_BA4",
"SD042_18_BA4",
"SD001_07_CSC",
"SD004_12_CSC",
"SD037_14_CSC"
))
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol,
colour = tissue)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:30])
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="fill", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +scale_fill_manual(values= mycoloursP[25:30])
```
```{r}
opcs <- subset(long_data, long_data$keycol != "NELL1_C2")
ggplot(opcs, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="fill", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +scale_fill_manual(values= mycoloursP[26:30])
```
```{r}
nell_data$PDGFRA_NELL1 <- as.numeric(nell_data$PDGFRA_NELL1)
nell_data$perc_Nell <- (nell_data$PDGFRA_NELL1/(nell_data$PDGFRA_NELL1 +
nell_data$PDRFRA_C1)) * 100
ggplot(nell_data, aes(x = tissue, y = perc_Nell)) + geom_boxplot() +
geom_jitter(width = 0.2, height = 0, aes(colour = sample_id)) +
theme_bw() + ylab("NELL1+ PDGFRA+ (%)") + xlab("Tissue")
ggplot(nell_data, aes(x = tissue, y = PDGFRA_NELL1)) + geom_boxplot() +
geom_jitter(width = 0.2, height = 0, aes(colour = sample_id)) +
theme_bw() + ylab("NELL1+ PDGFRA+") + xlab("Tissue")
```
Are Nell1 positive cells depending on tissue as well?
```{r}
ggplot(nell_data, aes(x = tissue, y = NELL1_C2)) + geom_boxplot() +
geom_jitter(width = 0.2, height = 0, aes(colour = sample_id)) +
theme_bw() + ylab("NELL1+") + xlab("Tissue")
```
Are PDGFRA positive cells depending on tissue as well?
```{r}
ggplot(nell_data, aes(x = tissue, y = PDRFRA_C1)) + geom_boxplot() +
geom_jitter(width = 0.2, height = 0, aes(colour = sample_id)) +
theme_bw() + ylab("PDGFRA+") + xlab("Tissue")
```
```{r}
mod_1 <- lmer(PDGFRA_NELL1 ~
tissue +
PDRFRA_C1+
(1 | sample_id),
data = nell_data)
summary(mod_1)
```
```{r}
mod_2 <- lmer(PDGFRA_NELL1 ~
tissue +
(1 | sample_id),
data = nell_data)
summary(mod_2)
```
```{r}
mod_lm <- lm(PDGFRA_NELL1 ~
tissue,
data = nell_data)
summary(mod_lm)
anova(mod_lm)
```
```{r}
long_data$staining <- "NELL1_PDGFRA"
ggplot(long_data, aes(x = staining ,
y = valuecol,
fill = keycol)) +
geom_bar(position="fill", stat="identity")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_fill_manual(values= mycoloursP[16:30])
nell1_subs_long <- subset(long_data, long_data$keycol != "NELL1_C2")
ggplot(nell1_subs_long, aes(x = staining ,
y = valuecol,
fill = keycol)) +
geom_bar(position="fill", stat="identity")+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
scale_fill_manual(values= mycoloursP[16:30])
```
Conclusion:
In accordance to the snRNAseq data, NELL 1 is expressed in a proportion of BA4
OPCs but while it is not being detected in CSC OPCs. There are not very many
cells expressing it though.
The question is what it does in OPCs and if there is a difference in brain OPCs
that do express NELL1 and those that don't.
NELL1 is also widely expressed in GM cells including neurons but it looks like
GM OPCs are expressing it in the brain as well. Above only white matter areas
were quantified.
```{r}
sessionInfo()
```