CUT&RUN

CUT & RUN Analysis for validation of ATAC-seq and footprint analysis.

library(tidyverse)
library(ggplot2)
library(cowplot)
library(rstatix)

Concentration after CUT & RUN Capture with Irf1 and Stat2

conc <- read.csv("220425_picogreen.csv", stringsAsFactors = TRUE)

conc %>% filter(Ab == "Irf1" & Group == "CON")  %>% shapiro_test(conc_pg.uL)

## # A tibble: 1 × 3
##   variable   statistic     p
##   <chr>          <dbl> <dbl>
## 1 conc_pg.uL     0.875 0.319

conc %>% filter(Ab == "Irf1" & Group == "MIA")  %>% shapiro_test(conc_pg.uL)

## # A tibble: 1 × 3
##   variable   statistic     p
##   <chr>          <dbl> <dbl>
## 1 conc_pg.uL     0.926 0.568

conc %>% filter(Ab == "Stat2" & Group == "CON")  %>% shapiro_test(conc_pg.uL)

## # A tibble: 1 × 3
##   variable   statistic     p
##   <chr>          <dbl> <dbl>
## 1 conc_pg.uL     0.917 0.523

conc %>% filter(Ab == "Stat2" & Group == "MIA")  %>% shapiro_test(conc_pg.uL)

## # A tibble: 1 × 3
##   variable   statistic     p
##   <chr>          <dbl> <dbl>
## 1 conc_pg.uL     0.927 0.578

conc %>% filter(Ab == "Irf1") %>% t.test(conc_pg.uL~ Group, data = ., var.equal = TRUE)

## 
##  Two Sample t-test
## 
## data:  conc_pg.uL by Group
## t = 1.5893, df = 6, p-value = 0.1631
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -13.02752  61.30921
## sample estimates:
## mean in group CON mean in group MIA 
##          51.62626          27.48542

conc %>% filter(Ab == "Stat2") %>% t.test(conc_pg.uL~ Group, data = ., var.equal = TRUE)

## 
##  Two Sample t-test
## 
## data:  conc_pg.uL by Group
## t = 2.0392, df = 6, p-value = 0.08754
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.113024 45.260354
## sample estimates:
## mean in group CON mean in group MIA 
##          44.95868          24.38501

conc %>% filter(Ab == "Irf1") %>% ggplot(., aes(x = Group, y=conc_pg.uL, fill = Group)) + geom_boxplot() + geom_point(size = 2) + theme_cowplot() + scale_fill_manual(values = c("grey", "red")) + theme(legend.position = "none") + ylim(0,100) + labs(subtitle = "Irf1")

conc %>% filter(Ab == "Stat2") %>% ggplot(., aes(x = Group, y=conc_pg.uL, fill = Group)) + geom_boxplot() + geom_point(size = 2) + theme_cowplot() + scale_fill_manual(values = c("grey", "red")) + theme(legend.position = "none") + ylim(0,100) + labs(subtitle = "Stat2")

Analysis of qPCR data for capture of transcription factor target genes compared to input control and represented as perecent of input.

IRF1

data <- read.csv("220425_data.csv", stringsAsFactors = TRUE, header = TRUE)

data %>% filter(Ab == "Irf1" & Target == "Fas" & Group == "CON")  %>% shapiro_test(percent.input)

## # A tibble: 1 × 3
##   variable      statistic     p
##   <chr>             <dbl> <dbl>
## 1 percent.input     0.955 0.747

data %>% filter(Ab == "Irf1" & Target == "Fas" & Group == "MIA")  %>% shapiro_test(percent.input)

## # A tibble: 1 × 3
##   variable      statistic     p
##   <chr>             <dbl> <dbl>
## 1 percent.input     0.913 0.499

data %>% filter(Ab == "Irf1" & Target == "IL6" & Group == "CON")  %>% shapiro_test(percent.input)

## # A tibble: 1 × 3
##   variable      statistic     p
##   <chr>             <dbl> <dbl>
## 1 percent.input     0.992 0.967

data %>% filter(Ab == "Irf1" & Target == "IL6" & Group == "MIA")  %>% shapiro_test(percent.input)

## # A tibble: 1 × 3
##   variable      statistic     p
##   <chr>             <dbl> <dbl>
## 1 percent.input     0.890 0.385

data %>% filter(Ab == "Irf1" & Target == "Fas")  %>% t.test(percent.input ~ Group, data = ., var.equal=T)

## 
##  Two Sample t-test
## 
## data:  percent.input by Group
## t = 2.4945, df = 6, p-value = 0.04688
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.01029868 1.06970132
## sample estimates:
## mean in group CON mean in group MIA 
##            1.2625            0.7225

data %>% filter(Ab == "Irf1" & Target == "IL6")  %>% t.test(percent.input ~ Group, data = ., var.equal=T)

## 
##  Two Sample t-test
## 
## data:  percent.input by Group
## t = 3.1471, df = 6, p-value = 0.01989
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.3392888 2.7107112
## sample estimates:
## mean in group CON mean in group MIA 
##            3.5025            1.9775

data %>% filter(Ab == "Irf1" & Target == "Fas") %>% 
  ggplot(., aes(x = Group, y = percent.input, fill = Group)) + geom_boxplot() + geom_point(size = 2) + scale_fill_manual(values = c("grey", "red")) + theme_cowplot() + labs(subtitle = "Irf1 capture") + ylim(0,2) + theme(legend.position = "none") + ylab("% Input (Fas)")

data %>% filter(Ab == "Irf1" & Target == "IL6") %>% 
  ggplot(., aes(x = Group, y = percent.input, fill = Group)) + geom_boxplot() + geom_point(size = 2) + scale_fill_manual(values = c("grey", "red")) + theme_cowplot() + labs(subtitle = "Irf1 capture") + ylim(0,5) + theme(legend.position = "none") + ylab("% Input (IL-6)")

Stat2

Stat2 <- data %>% filter(Ab == "Stat2" & Sample != "21-75-3")
# 21-75-3 showed 3x more expression than the remaining 3 animals

Stat2 %>% filter(Target == "IL23" & Group == "CON")  %>% shapiro_test(percent.input)

## # A tibble: 1 × 3
##   variable      statistic     p
##   <chr>             <dbl> <dbl>
## 1 percent.input     0.977 0.882

Stat2 %>% filter(Target == "IL23" & Group == "MIA")  %>% shapiro_test(percent.input)

## # A tibble: 1 × 3
##   variable      statistic     p
##   <chr>             <dbl> <dbl>
## 1 percent.input     0.904 0.398

Stat2 %>% filter(Target == "IL23")  %>% t.test(percent.input ~ Group, data = ., var.equal=T)

## 
##  Two Sample t-test
## 
## data:  percent.input by Group
## t = 4.7383, df = 5, p-value = 0.005158
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.3450225 1.1633109
## sample estimates:
## mean in group CON mean in group MIA 
##         1.2775000         0.5233333

Stat2 %>% filter(Target == "IL23") %>% 
  ggplot(., aes(x = Group, y = percent.input, fill = Group)) + geom_boxplot() + geom_point(size = 2) + scale_fill_manual(values = c("grey", "red")) + theme_cowplot() + labs(subtitle = "Stat2 capture") + ylim(0,2) + theme(legend.position = "none") + ylab("% Input (IL-23)")