---
title: "Validation_FMN1_RBFOX1_OLIG2_IF"
author: "Luise A. Seeker"
date: "09/08/2022"
output: html_document
---
This is based on only three stainings each of BA4 and CSC tissue. FMN1 staining
looks very interesting, particularly in the brain where it almost marks tiling
cells of unclear identity that I think may be microglia based in them mostly
being OLIG2 negative and based on the RNAseq data that also marks neurons and
microglia as expressing FMN1 but not astrocytes.
OPCs and COPs also do not express FMN1 which means it may be responsible
for cytoskeleton reshaping in other situations than myelination (where the
Arp2/3 complex is important). Maybe it is important for re-myelinaton?
So, what I need to do here is to only show that FMN+ olig2+ cells exist which
is more tricky than it sounds, because FMN1 is located in processes.
I first tried with global fluorescence thresholds for nuclear OLIG2, cellular
RMFOX1 and cytoplasmic FMN1:
```{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}
fmn1_rbfox1 <- read.csv(here("data",
"validation_data",
"20220808_RBFOX1_FMN1_OLIG2_constant_thresholds.csv"))
```
```{r}
names(fmn1_rbfox1)
fmn1_rbfox1$tissue <- as.factor(fmn1_rbfox1$tissue)
fmn1_rbfox1$other_cells <- as.numeric(fmn1_rbfox1$Num_Detections)-(
as.numeric(fmn1_rbfox1$Num_FMN1) +
as.numeric(fmn1_rbfox1$Num_OLIG2)+
as.numeric(fmn1_rbfox1$Num_RBFOX1) +
as.numeric(fmn1_rbfox1$Num_OLIG2_FMN1) +
as.numeric(fmn1_rbfox1$Num_RBFOX1_FMN1) +
as.numeric(fmn1_rbfox1$Num_RBFOX1_OLIG2) +
as.numeric(fmn1_rbfox1$Num_RBFOX1_OLIG2_FMN1)
)
```
Convert from wide to long format to see how many single, double and triple
positive cells are in each sample
```{r}
keycol <- "sample_id"
valuecol <- "count"
gathercols <- c("Num_FMN1",
"Num_OLIG2",
"Num_OLIG2_FMN1",
"Num_RBFOX1",
"Num_RBFOX1_FMN1",
"Num_RBFOX1_OLIG2",
"Num_RBFOX1_OLIG2_FMN1",
"other_cells")
long_data <- gather(fmn1_rbfox1, keycol, valuecol, gathercols)
head(long_data)
nrow(long_data)
names(long_data)
```
```{r}
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:50])
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="fill", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +scale_fill_manual(values= mycoloursP[25:50])
```
Oligodendrocytes only
```{r}
keycol <- "sample_id"
valuecol <- "count"
gathercols <- c("Num_OLIG2",
"Num_OLIG2_FMN1",
"Num_RBFOX1_OLIG2",
"Num_RBFOX1_OLIG2_FMN1")
long_data <- gather(fmn1_rbfox1, keycol, valuecol, gathercols)
head(long_data)
nrow(long_data)
names(long_data)
```
```{r}
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="stack", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+scale_fill_manual(values= mycoloursP[25:50])
ggplot(long_data, aes(x = sample_id ,
y = valuecol,
fill = keycol)) +
geom_bar(position="fill", stat="identity")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +scale_fill_manual(values= mycoloursP[25:50])
```
```{r}
fmn1_rbfox1$Num_RBFOX1_OLIG2_FMN1 <- as.numeric(fmn1_rbfox1$Num_RBFOX1_OLIG2_FMN1)
fmn1_rbfox1$perc_triple_pos_olig <- (fmn1_rbfox1$Num_RBFOX1_OLIG2_FMN1/(
fmn1_rbfox1$Num_OLIG2+
fmn1_rbfox1$Num_OLIG2_FMN1+
fmn1_rbfox1$Num_RBFOX1_OLIG2))*100
ggplot(fmn1_rbfox1, aes(x = tissue, y = perc_triple_pos_olig)) + geom_boxplot() +
geom_jitter(width = 0.2, height = 0, aes(colour = sample_id)) +
theme_bw() + ylab("FMN1+ RBFOX1+ OLIG2+ (%)") + xlab("Tissue")
ggplot(fmn1_rbfox1, aes(x = tissue, y = Num_RBFOX1_OLIG2_FMN1)) + geom_boxplot() +
geom_jitter(width = 0.2, height = 0, aes(colour = sample_id)) +
theme_bw() + ylab("FMN1+ RBFOX1+ OLIG2+") + xlab("Tissue")
```
Conclusion:
Based in the very variable number of unclassified cells and classified cells
per sample, I believe the global thresholds are again not working very well in
this context and sample specific thresholds or manual classification
need to be used.
```{r}
sessionInfo()
```