---
title: "Validation_gpnmb_p1621_iba1_OLIG2_IF"
author: "Luise A. Seeker"
date: "09/08/2022"
output: html_document
---
HCN2 SPARC and OLIG2 co-labelling in 3 BA4 and 2 CSC samples
```{r}
library(ggplot2)
library(lme4)
library(lmerTest)
library(here)
library(tidyr)
library(ggsci)
library(Seurat)
```
```{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}
gpnmb_p1621_iba1 <- read.csv(here("data",
"validation_data",
"20220820_GPNMB_P16_P21_IBA1_FOV_thresholds_quant.csv"))
```
```{r}
names(gpnmb_p1621_iba1)
gpnmb_p1621_iba1$tissue <- as.factor(gpnmb_p1621_iba1$tissue)
gpnmb_p1621_iba1$sample_id <- factor(gpnmb_p1621_iba1$sample_id,
levels = c("SD008_18_BA4",
"SD021_17_BA4",
"SD042_18_BA4",
"SD015_12_CB",
"SD030_12_CB",
"SD039_14_CB",
"SD015_12_CSC",
"SD042_10_CSC"))
```
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("Num_Detections",
"Num_GPNMB",
"Num_GPNMB_IBA1",
"Num_GPNMB_IBA1_P16P21",
"Num_GPNMB_P16P21",
"Num_IBA1",
"Num_P16P21",
"Num_P16P21_IBA1")
long_data <- gather(gpnmb_p1621_iba1, keycol, valuecol, gathercols)
head(long_data)
nrow(long_data)
names(long_data)
```
```{r}
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:50])
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[25:50])
```
Remove cells that are negative for all tested markers:
```{r}
keycol <- "sample_id"
valuecol <- "count"
gathercols <- c("Num_GPNMB",
"Num_GPNMB_IBA1",
"Num_GPNMB_IBA1_P16P21",
"Num_GPNMB_P16P21",
"Num_IBA1",
"Num_P16P21",
"Num_P16P21_IBA1")
long_data <- gather(gpnmb_p1621_iba1, keycol, valuecol, gathercols)
head(long_data)
nrow(long_data)
names(long_data)
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:50])
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[25:50])
```
Exclude cells that are not positive for OLIG2
```{r}
keycol <- "sample_id"
valuecol <- "count"
gathercols <- c("Num_GPNMB_IBA1",
"Num_GPNMB_IBA1_P16P21",
"Num_IBA1",
"Num_P16P21_IBA1")
long_data <- gather(gpnmb_p1621_iba1, keycol, valuecol, gathercols)
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:50])
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[25:50])
```
collapse tissue
```{r}
ggplot(long_data, aes(x = tissue ,
y = valuecol,
fill = keycol)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:50])
ggplot(long_data, aes(x = tissue,
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[25:50])
```
```{r}
keycol <- "sample_id"
valuecol <- "count"
gathercols <- c("sum_GPNMB_neg_IBA1_Pos",
"sum_GPNMB_IBA1")
long_data <- gather(gpnmb_p1621_iba1, keycol, valuecol, gathercols)
long_data$keycol <- factor(long_data$keycol, levels = c("sum_GPNMB_neg_IBA1_Pos",
"sum_GPNMB_IBA1"))
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:50])
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[25:50])
```
```{r}
ggplot(long_data, aes(x = tissue ,
y = valuecol,
fill = keycol)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:50])
ggplot(long_data, aes(x = tissue,
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[25:50])
```
```{r}
ggplot(gpnmb_p1621_iba1, aes(x = tissue, y = GPNMB_perc_IBA1)) +
geom_boxplot()+ geom_jitter(width = 0.2, aes(colour = sample_id)) +
theme_bw(19) + ylab("GPNMB+IBA1+HOECHST+ (%)") + xlab("Tissue")
```
```{r}
lm_prot <- lm(gpnmb_p1621_iba1$GPNMB_perc_IBA1~ gpnmb_p1621_iba1$tissue)
summary(lm_prot)
anova(lm_prot)
```
```{r}
ti_csc <- subset(gpnmb_p1621_iba1, gpnmb_p1621_iba1$tissue == "CSC")
ti_cb <- subset(gpnmb_p1621_iba1, gpnmb_p1621_iba1$tissue == "CB")
ti_ba4 <- subset(gpnmb_p1621_iba1, gpnmb_p1621_iba1$tissue == "BA4")
t.test(ti_csc$GPNMB_perc_IBA1, ti_cb$GPNMB_perc_IBA1)
t.test(ti_csc$GPNMB_perc_IBA1, ti_ba4$GPNMB_perc_IBA1)
t.test(ti_cb$GPNMB_perc_IBA1, ti_ba4$GPNMB_perc_IBA1)
```
```{r}
summary(gpnmb_p1621_iba1$GPNMB_perc_IBA1)
```
```{r}
micro_srt <- readRDS(here("data",
"single_nuc_data",
"microglia",
"HCA_microglia.RDS"))
Idents(micro_srt) <- "microglia_clu"
micro_srt <- subset(micro_srt, idents = c("Microglia_1",
"Microglia_2",
"Microglia_3",
"Microglia_4",
"Microglia_5",
"BAM"))
Idents(micro_srt) <- "Tissue"
csc_srt <- subset(micro_srt, idents = "CSC")
cb_srt <- subset(micro_srt, idents = "CB")
ba4_srt <- subset(micro_srt, idents = "BA4")
cd_genes <- c("GPNMB")
a_csc <- DotPlot(object = csc_srt, features = cd_genes)
a_csc$data
mean(a_csc$data$pct.exp)
a_cb <- DotPlot(object = cb_srt, features = cd_genes)
a_cb$data
mean(a_cb$data$pct.exp)
a_ba4 <- DotPlot(object = ba4_srt, features = cd_genes)
a_ba4$data
mean(a_ba4$data$pct.exp)
df <- data.frame(modality = rep(c("GPNMB_neg", "GPNMB_pos"),3),
tissue = c("BA4", "BA4", "CB", "CB", "CSC", "CSC"),
percentage = c(100- mean(a_ba4$data$pct.exp),
mean(a_ba4$data$pct.exp),
100- mean(a_cb$data$pct.exp),
mean(a_cb$data$pct.exp),
100- mean(a_csc$data$pct.exp),
mean(a_csc$data$pct.exp)))
ggplot(df, aes(x = tissue, y=percentage, fill = modality))+
geom_bar(stat="identity")+scale_fill_manual(values= mycoloursP[25:50])
```
```{r}
Idents(csc_srt) <- "process_number"
a_csc_s <- DotPlot(object = csc_srt, features = cd_genes)
a_csc_s$data
mean(a_csc_s$data$pct.exp)
a_csc_s$data$Tissue <- "CSC"
Idents(cb_srt) <- "process_number"
a_cb_s <- DotPlot(object = cb_srt, features = cd_genes)
a_cb_s$data
mean(a_cb_s$data$pct.exp)
a_cb_s$data$Tissue <- "CB"
Idents(ba4_srt) <- "process_number"
a_ba4_s <- DotPlot(object = ba4_srt, features = cd_genes)
a_ba4_s$data
mean(a_ba4_s$data$pct.exp)
a_ba4_s$data$Tissue <- "BA4"
b_data <- rbind(a_csc_s$data,
a_cb_s$data,
a_ba4_s$data)
```
```{r}
ggplot(b_data, aes(x = Tissue, y = pct.exp)) +
geom_boxplot()+ geom_jitter(width = 0.2, aes(colour = id)) +
theme_bw(19) + ylab("GPNMB+ (%)") + xlab("Tissue")
```
```{r}
lm_rna <- lm(b_data$pct.exp~ b_data$Tissue)
summary(lm_rna)
anova(lm_rna)
```
Conclusion:
I found that automatically counting FMN1 positive cells lead to a large variation,
and hoped manual counting would alleviate this. But I still observe a lot of
variation among samples. The FMN1 validation is tricky, because it is
expressed in processes and it is sometimes difficult to decide if a process
belongs to a cell or merely touches it. I think what I can say is that a subset
of oligodendrocytes do express FMN1 and that differences in their percentage
between snRNAseq and IF may be due to technical differences, sample differences
or inaccuracy in quantifying IF images due to FMN1 being expressed in processes.
It was particularly difficult to quantify in Spinal cord due to staining of background structures (ECM/ cytoskelleton).
```{r}
sessionInfo()
```