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Fig_IRIF_BRCA1.Rmd
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---
title: "IRIF of BRCA1 foci analysis"
author: "Weiyan"
date: "07/20/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
>Check the IRIF of BRCA1 in FUS KO cells
**Note**:
> 1. Samples: WT: GUS/U2OS, KO: GUS/Clone110, RE: FUS/Clone110;
2. Antibodies: BRCA1(M, SC-6954), rH2AX(R,2577S,Cell Signaling);
4. Foci were ideatified by CellProfiler;
5. BRCA1 and rH2AX foci identification setting is 3:6.
# 1. packages
```{r}
library(ggbeeswarm)
library(viridis)
library(tidyverse)
library(ggpubr)
library(export)
```
# 2. load raw data
```{r}
# R1
Nuclei_R1 <- read.csv("IRIF_BRCA1_07132020/analysis/raw/BRCA1_rH2AX_2Gy_Nuclei.csv",header = TRUE)
Image_R1 <- read.csv("IRIF_BRCA1_07132020/analysis/raw/BRCA1_rH2AX_2Gy_Image.csv",header = TRUE)
# R2
Nuclei_R2 <- read.csv("IRIF_BRCA1_07152020/analysis/raw/BRCA1_rH2AX_2Gy_Nuclei.csv",header = TRUE)
Image_R2 <- read.csv("IRIF_BRCA1_07152020/analysis/raw/BRCA1_rH2AX_2Gy_Image.csv",header = TRUE)
# R3
Nuclei_R3 <- read.csv("IRIF_BRCA1_07172020/analysis/raw/BRCA1_rH2AX_2Gy_Nuclei.csv",header = TRUE)
Image_R3 <- read.csv("IRIF_BRCA1_07172020/analysis/raw/BRCA1_rH2AX_2Gy_Image.csv",header = TRUE)
```
# 3. QC of samples
## 3.1 R1
```{r}
IRIF_foci_R1 <- Nuclei_R1 %>%
select(ImageNumber,ObjectNumber,Children_IRIF_BRCA1_Count,Children_IRIF_rH2AX_Count,Intensity_IntegratedIntensity_DAPI,Intensity_IntegratedIntensity_BRCA1,Mean_IRIF_BRCA1_Intensity_MeanIntensity_BRCA1,Mean_IRIF_rH2AX_Intensity_MeanIntensity_rH2AX)%>%
rename(BRCA1_Count=Children_IRIF_BRCA1_Count,rH2AX_Count=Children_IRIF_rH2AX_Count,Intensity_DAPI=Intensity_IntegratedIntensity_DAPI, Intensity_BRCA1=Intensity_IntegratedIntensity_BRCA1, MeanIntensity_BRCA1_foci=Mean_IRIF_BRCA1_Intensity_MeanIntensity_BRCA1, MeanIntensity_rH2AX_foci = Mean_IRIF_rH2AX_Intensity_MeanIntensity_rH2AX)%>%
replace(is.na(.), 0)
Image2_R1<- Image_R1%>%
select(ImageNumber, FileName_BRCA1_Image, Count_IRIF_BRCA1,Count_IRIF_rH2AX,Count_Nuclei)%>%
separate(FileName_BRCA1_Image,c("sample","A","B","X","treat"), sep = "-", remove = FALSE)%>%
select(-A,-B,-X)
metadataR1<-Image2_R1%>%
select(ImageNumber,sample,treat)
fociR1<- IRIF_foci_R1%>%
left_join(metadataR1,by='ImageNumber')%>%
mutate(BRCA1_Count=as.numeric(BRCA1_Count), rH2AX_Count=as.numeric(rH2AX_Count))
fociR1$replicate <- "R1"
fociR1$treat<- factor(fociR1$treat, levels = c("mock","15min","2hr"))
```
## 3.2 R2
```{r}
IRIF_foci_R2 <- Nuclei_R2 %>%
select(ImageNumber,ObjectNumber,Children_IRIF_BRCA1_Count,Children_IRIF_rH2AX_Count,Intensity_IntegratedIntensity_DAPI,Intensity_IntegratedIntensity_BRCA1,Mean_IRIF_BRCA1_Intensity_MeanIntensity_BRCA1,Mean_IRIF_rH2AX_Intensity_MeanIntensity_rH2AX)%>%
rename(BRCA1_Count=Children_IRIF_BRCA1_Count,rH2AX_Count=Children_IRIF_rH2AX_Count,Intensity_DAPI=Intensity_IntegratedIntensity_DAPI, Intensity_BRCA1=Intensity_IntegratedIntensity_BRCA1, MeanIntensity_BRCA1_foci=Mean_IRIF_BRCA1_Intensity_MeanIntensity_BRCA1, MeanIntensity_rH2AX_foci = Mean_IRIF_rH2AX_Intensity_MeanIntensity_rH2AX)%>%
replace(is.na(.), 0)
Image2_R2<- Image_R2%>%
select(ImageNumber, FileName_BRCA1_Image, Count_IRIF_BRCA1,Count_IRIF_rH2AX,Count_Nuclei)%>%
separate(FileName_BRCA1_Image,c("sample","A","B","X","treat"), sep = "-", remove = FALSE)%>%
select(-A,-B,-X)
metadataR2<-Image2_R2%>%
select(ImageNumber,sample,treat)
fociR2<- IRIF_foci_R2%>%
left_join(metadataR2,by='ImageNumber')%>%
mutate(BRCA1_Count=as.numeric(BRCA1_Count), rH2AX_Count=as.numeric(rH2AX_Count))
fociR2$replicate <- "R2"
fociR2$treat<- factor(fociR2$treat, levels = c("mock","15min","2hr"))
```
## 3.3 R3
```{r}
IRIF_foci_R3 <- Nuclei_R3 %>%
select(ImageNumber,ObjectNumber,Children_IRIF_BRCA1_Count,Children_IRIF_rH2AX_Count,Intensity_IntegratedIntensity_DAPI,Intensity_IntegratedIntensity_BRCA1,Mean_IRIF_BRCA1_Intensity_MeanIntensity_BRCA1,Mean_IRIF_rH2AX_Intensity_MeanIntensity_rH2AX)%>%
rename(BRCA1_Count=Children_IRIF_BRCA1_Count,rH2AX_Count=Children_IRIF_rH2AX_Count,Intensity_DAPI=Intensity_IntegratedIntensity_DAPI, Intensity_BRCA1=Intensity_IntegratedIntensity_BRCA1, MeanIntensity_BRCA1_foci=Mean_IRIF_BRCA1_Intensity_MeanIntensity_BRCA1, MeanIntensity_rH2AX_foci = Mean_IRIF_rH2AX_Intensity_MeanIntensity_rH2AX)%>%
replace(is.na(.), 0)
Image2_R3<- Image_R3%>%
select(ImageNumber, FileName_BRCA1_Image, Count_IRIF_BRCA1,Count_IRIF_rH2AX,Count_Nuclei)%>%
separate(FileName_BRCA1_Image,c("sample","A","B","X","treat"), sep = "-", remove = FALSE)%>%
select(-A,-B,-X)
metadataR3<-Image2_R3%>%
select(ImageNumber,sample,treat)
fociR3<- IRIF_foci_R3%>%
left_join(metadataR3,by='ImageNumber')%>%
mutate(BRCA1_Count=as.numeric(BRCA1_Count), rH2AX_Count=as.numeric(rH2AX_Count))
fociR3$replicate <- "R3"
fociR3$treat<- factor(fociR3$treat, levels = c("mock","15min","2hr"))
```
## 3.4 foci counts data
```{r}
fociAll <-bind_rows(fociR1,fociR2,fociR3)%>%
filter(BRCA1_Count<=50, rH2AX_Count <=100)
fociAll$treat<- factor(fociAll$treat, levels = c("mock","15min","2hr"))
fociAll$sample<- factor(fociAll$sample, levels = c("GUSU2OS","GUSClone110","FUSClone110"))
levels(fociAll$treat)
RA <- fociAll%>%
select(replicate,BRCA1_Count, treat, sample)%>%
group_by(sample, treat, replicate)%>%
summarise_each(list(median))
print(as.data.frame(RA)) ## case RA is a tibble.str(RA) or class(RA)
CellCounts <- fociAll%>%
count(sample,treat, replicate,name = "n_Cell")
CellCounts
summary(CellCounts$n_Cell)
```
# 4. BRCA1 foci
## 4.1 density plot
```{r fig.height=8, fig.width=10}
ggdensity(fociAll,
x = "BRCA1_Count",
add = "median",
rug = TRUE,
color = "sample",
legend = "top",
facet.by = c("replicate","treat"),
ncol = 3,
palette = "aaas"
)
# graph2pdf(file="figures/DensityPlotBRCA1.pdf", width=12, aspectr=sqrt(2),font = "Arial",bg = "transparent")
```
## 4.2 box plot
```{r fig.height=8, fig.width=12}
my_comparison <- list(c("GUSClone110","GUSU2OS"), c("GUSClone110","FUSClone110"), c("FUSClone110","GUSU2OS"))
ggboxplot(fociAll,
x="sample",
y="BRCA1_Count",
color = "sample",
palette = "aaas",
facet.by = c("replicate", "treat"),
order = c("GUSU2OS","GUSClone110","FUSClone110"),
add = "jitter",
ylab = "BRCA1 Foci Counts",
xlab = ""
)+
stat_compare_means(comparisons = my_comparison,
method = "wilcox.test",
label = "p.format"
)+
stat_compare_means(method = "kruskal.test", # this step for mutiple groups comparison
label.y = 70)
# graph2pdf(file="figures/BoxPlotBRCA1.pdf", width=12, aspectr=sqrt(2),font = "Arial",bg = "transparent")
```
## 4.3 box plot(pool data)
```{r fig.height=4, fig.width=12}
my_comparison <- list(c("GUSClone110","GUSU2OS"), c("GUSClone110","FUSClone110"), c("FUSClone110","GUSU2OS"))
ggboxplot(fociAll,
x="sample",
y="BRCA1_Count",
color = "sample",
palette = "aaas",
facet.by = "treat",
order = c("GUSU2OS","GUSClone110","FUSClone110"),
add = "jitter",
ylab = "BRCA1 Foci Counts",
xlab = ""
)+
stat_compare_means(comparisons = my_comparison,
method = "wilcox.test",
label = "p.format"
)+
stat_compare_means(method = "kruskal.test", # this step for mutiple groups comparison
label.y = 70)
# graph2pdf(file="figures/BoxPlotBRCA1_all.pdf", width=12, aspectr=sqrt(2),font = "Arial",bg = "transparent")
```
# 5. rH2AX foci
## 5.1 density plot
```{r fig.height=8, fig.width=10}
ggdensity(fociAll,
x = "rH2AX_Count",
add = "median",
rug = TRUE,
color = "sample",
legend = "top",
facet.by = c("replicate","treat"),
ncol = 3,
palette = "aaas"
)
# graph2pdf(file="figures/DensityPlotrH2AX.pdf", width=12, aspectr=sqrt(2),font = "Arial",bg = "transparent")
```
## 5.2 box plot
```{r fig.height=8, fig.width=12}
my_comparison <- list(c("GUSClone110","GUSU2OS"), c("GUSClone110","FUSClone110"), c("FUSClone110","GUSU2OS"))
ggboxplot(fociAll,
x="sample",
y="rH2AX_Count",
color = "sample",
palette = "aaas",
facet.by = c("replicate", "treat"),
order = c("GUSU2OS","GUSClone110","FUSClone110"),
add = "jitter",
ylab = "rH2AX Foci Counts",
xlab = ""
)+
stat_compare_means(comparisons = my_comparison, # this step for group comparison
method = "wilcox.test",
label = "p.format"
)+
stat_compare_means(method = "kruskal.test", # this step for mutiple groups comparison
label.y = 150)
# graph2pdf(file="figures/BoxPlotrH2AX.pdf", width=12, aspectr=sqrt(2),font = "Arial",bg = "transparent")
```
## 5.3 box plot(pool data)
```{r fig.height=4, fig.width=12}
my_comparison <- list(c("GUSClone110","GUSU2OS"), c("GUSClone110","FUSClone110"), c("FUSClone110","GUSU2OS"))
ggboxplot(fociAll,
x="sample",
y="rH2AX_Count",
color = "sample",
palette = "aaas",
facet.by = "treat",
order = c("GUSU2OS","GUSClone110","FUSClone110"),
add = "jitter",
ylab = "rH2AX Foci Counts",
xlab = ""
)+
stat_compare_means(comparisons = my_comparison, # this step for group comparison
method = "wilcox.test",
label = "p.format"
)+
stat_compare_means(method = "kruskal.test", # this step for mutiple groups comparison
label.y = 150)
# graph2pdf(file="figures/BoxPlotrH2AX_all.pdf", width=12, aspectr=sqrt(2),font = "Arial",bg = "transparent")
```
```{r}
sessionInfo()
```