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IRIF of BRCA1 foci analysis

Weiyan 07/20/2020

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);
  3. Foci were ideatified by CellProfiler;
  4. BRCA1 and rH2AX foci identification setting is 3:6.

1. packages

library(ggbeeswarm)
## Loading required package: ggplot2

library(viridis)
## Loading required package: viridisLite

library(tidyverse)
## ── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ tibble  3.0.3     ✓ dplyr   1.0.0
## ✓ tidyr   1.1.0     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ✓ purrr   0.3.4

## ── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggpubr)
library(export)

2. load raw data

# 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

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)
## Warning: Expected 5 pieces. Additional pieces discarded in 72 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

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

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)
## Warning: Expected 5 pieces. Additional pieces discarded in 72 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

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

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)
## Warning: Expected 5 pieces. Additional pieces discarded in 72 rows [1, 2, 3, 4,
## 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

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

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)
## [1] "mock"  "15min" "2hr"

RA <- fociAll%>%
      select(replicate,BRCA1_Count, treat, sample)%>%
      group_by(sample, treat, replicate)%>%
      summarise_each(list(median))
## Warning: `summarise_each_()` is deprecated as of dplyr 0.7.0.
## Please use `across()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

print(as.data.frame(RA)) ## case RA is a tibble.str(RA) or class(RA)
##         sample treat replicate BRCA1_Count
## 1      GUSU2OS  mock        R1         2.0
## 2      GUSU2OS  mock        R2         2.0
## 3      GUSU2OS  mock        R3         9.0
## 4      GUSU2OS 15min        R1        13.0
## 5      GUSU2OS 15min        R2         7.0
## 6      GUSU2OS 15min        R3        12.0
## 7      GUSU2OS   2hr        R1        16.0
## 8      GUSU2OS   2hr        R2        10.0
## 9      GUSU2OS   2hr        R3        13.0
## 10 GUSClone110  mock        R1         2.0
## 11 GUSClone110  mock        R2         1.0
## 12 GUSClone110  mock        R3         3.0
## 13 GUSClone110 15min        R1         3.0
## 14 GUSClone110 15min        R2         1.5
## 15 GUSClone110 15min        R3         3.0
## 16 GUSClone110   2hr        R1        12.0
## 17 GUSClone110   2hr        R2         5.0
## 18 GUSClone110   2hr        R3        11.0
## 19 FUSClone110  mock        R1         7.0
## 20 FUSClone110  mock        R2         2.0
## 21 FUSClone110  mock        R3         7.0
## 22 FUSClone110 15min        R1        11.0
## 23 FUSClone110 15min        R2        11.0
## 24 FUSClone110 15min        R3        16.0
## 25 FUSClone110   2hr        R1        19.0
## 26 FUSClone110   2hr        R2         9.0
## 27 FUSClone110   2hr        R3        13.0

CellCounts <- fociAll%>%
              count(sample,treat, replicate,name = "n_Cell")
CellCounts
##         sample treat replicate n_Cell
## 1      GUSU2OS  mock        R1    178
## 2      GUSU2OS  mock        R2    174
## 3      GUSU2OS  mock        R3    218
## 4      GUSU2OS 15min        R1    212
## 5      GUSU2OS 15min        R2    286
## 6      GUSU2OS 15min        R3    240
## 7      GUSU2OS   2hr        R1    188
## 8      GUSU2OS   2hr        R2    240
## 9      GUSU2OS   2hr        R3    231
## 10 GUSClone110  mock        R1    208
## 11 GUSClone110  mock        R2    191
## 12 GUSClone110  mock        R3    239
## 13 GUSClone110 15min        R1    253
## 14 GUSClone110 15min        R2    298
## 15 GUSClone110 15min        R3    283
## 16 GUSClone110   2hr        R1    248
## 17 GUSClone110   2hr        R2    256
## 18 GUSClone110   2hr        R3    280
## 19 FUSClone110  mock        R1    183
## 20 FUSClone110  mock        R2    158
## 21 FUSClone110  mock        R3    208
## 22 FUSClone110 15min        R1    211
## 23 FUSClone110 15min        R2    235
## 24 FUSClone110 15min        R3    251
## 25 FUSClone110   2hr        R1    169
## 26 FUSClone110   2hr        R2    237
## 27 FUSClone110   2hr        R3    253

summary(CellCounts$n_Cell)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
##   158.0   199.5   235.0   227.0   252.0   298.0

4. BRCA1 foci

4.1 density plot

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

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")

5. rH2AX foci

5.1 density plot

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

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")
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.6
##
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base
##
## other attached packages:
##  [1] export_0.2.2.9001 ggpubr_0.4.0      forcats_0.5.0     stringr_1.4.0
##  [5] dplyr_1.0.0       purrr_0.3.4       readr_1.3.1       tidyr_1.1.0
##  [9] tibble_3.0.3      tidyverse_1.3.0   viridis_0.5.1     viridisLite_0.3.0
## [13] ggbeeswarm_0.6.0  ggplot2_3.3.2
##
## loaded via a namespace (and not attached):
##  [1] fs_1.4.2                lubridate_1.7.9         webshot_0.5.2
##  [4] httr_1.4.1              ggsci_2.9               tools_3.6.3
##  [7] backports_1.1.8         R6_2.4.1                vipor_0.4.5
## [10] DBI_1.1.0               colorspace_1.4-1        manipulateWidget_0.10.1
## [13] withr_2.2.0             tidyselect_1.1.0        gridExtra_2.3
## [16] curl_4.3                compiler_3.6.3          cli_2.0.2
## [19] rvest_0.3.5             flextable_0.5.10        xml2_1.3.2
## [22] officer_0.3.12          labeling_0.3            scales_1.1.1
## [25] systemfonts_0.2.3       digest_0.6.25           foreign_0.8-75
## [28] rmarkdown_2.3           rio_0.5.16              base64enc_0.1-3
## [31] pkgconfig_2.0.3         htmltools_0.5.0         fastmap_1.0.1
## [34] dbplyr_1.4.4            rvg_0.2.5               htmlwidgets_1.5.1
## [37] rlang_0.4.7             readxl_1.3.1            rstudioapi_0.11
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## [43] jsonlite_1.7.0          crosstalk_1.1.0.1       zip_2.0.4
## [46] car_3.0-8               magrittr_1.5            Rcpp_1.0.5
## [49] munsell_0.5.0           fansi_0.4.1             abind_1.4-5
## [52] gdtools_0.2.2           lifecycle_0.2.0         stringi_1.4.6
## [55] yaml_2.2.1              carData_3.0-4           grid_3.6.3
## [58] blob_1.2.1              promises_1.1.1          crayon_1.3.4
## [61] miniUI_0.1.1.1          haven_2.3.1             stargazer_5.2.2
## [64] hms_0.5.3               knitr_1.29              pillar_1.4.6
## [67] uuid_0.1-4              ggsignif_0.6.0          reprex_0.3.0
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