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CRISPRi_Screening_AceticAcid.R
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CRISPRi_Screening_AceticAcid.R
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#CRISPRi_Screening_AceticAcid_rscript
AA_titration_data <- read.csv("COMPILED_DATA/20210120_AA_titration_absolute_compiled.csv", na.strings = "NoGrowth")
library(reshape)
AA_titration_data_reshape <- reshape(data=AA_titration_data, idvar="gRNA_name",
varying = colnames(AA_titration_data)[3:7],
v.name=c("Generation_time"),
new.row.names = 1:30000,
direction="long",
timevar = "Condition",
times = colnames(AA_titration_data)[3:7])
library(ggplot2)
library(reshape)
library(ggridges)
plt0 <- ggplot(AA_titration_data_reshape, aes(x = Generation_time, y = Condition, height = stat(density))) +
geom_density_ridges2(stat = "binline", bins = 200, scale = 2, draw_baseline = FALSE)+
theme_ridges()
suppressWarnings(print(plt0))
Metadata_CRISPRi <- read.csv("COMPILED_DATA/library_keyfile1536.csv", na.strings = "#N/A", stringsAsFactors = FALSE)
str(Metadata_CRISPRi)
m <- vector(mode = "character", length = 0)
file.names<-vector(mode = "character", length = 0)
temp_df<-data.frame()
data_Ctrl_Abs <- data.frame()
for(i in 1:24){
m <- paste0("Ctrl", i, ".phenotypes.Absolute")
file.names[i] <- dir("RAW_DATA/SOM_SCR_R002/", pattern = m, full.names = TRUE)
temp_df <- read.csv(file.names[i], na.strings = "NoGrowth")
data_Ctrl_Abs <- rbind(data_Ctrl_Abs, temp_df)
}
str(data_Ctrl_Abs)
data_Ctrl_Abs_Trim <- data_Ctrl_Abs[, 14:15]
colnames(data_Ctrl_Abs_Trim) <- c("CTRL_Y_ABS", "CTRL_GT_ABS")
str(data_Ctrl_Abs_Trim)
m <- vector(mode = "character", length = 0)
file.names<-vector(mode = "character", length = 0)
temp_df<-data.frame()
data_AA_Abs <- data.frame()
for(i in 1:24){
m <- paste0("aa", i, ".phenotypes.Absolute")
file.names[i] <- dir("RAW_DATA/SOM_SCR_R002/", pattern = m, full.names = TRUE)
temp_df <- read.csv(file.names[i], na.strings = "NoGrowth")
data_AA_Abs <- rbind(data_AA_Abs, temp_df)
}
data_AA_Abs_Trim <- data_AA_Abs[, 14:15]
colnames(data_AA_Abs_Trim) <- c("AA_Y_ABS", "AA_GT_ABS")
m <- vector(mode = "character", length = 0)
file.names<-vector(mode = "character", length = 0)
temp_df<-data.frame()
data_Ctrl_Norm <- data.frame()
for(i in 1:24){
m <- paste0("Ctrl", i, ".phenotypes.Normalized")
file.names[i] <- dir("RAW_DATA/SOM_SCR_R002/", pattern = m, full.names = TRUE)
temp_df <- read.csv(file.names[i], na.strings = "NoGrowth")
data_Ctrl_Norm <- rbind(data_Ctrl_Norm, temp_df)
}
str(data_Ctrl_Norm)
data_Ctrl_Norm_Trim <- data_Ctrl_Norm[, 4:5]
colnames(data_Ctrl_Norm_Trim) <- c("CTRL_Y_NORM", "CTRL_GT_NORM")
m <- vector(mode = "character", length = 0)
file.names<-vector(mode = "character", length = 0)
temp_df<-data.frame()
data_AA_Norm <- data.frame()
for(i in 1:24){
m <- paste0("aa", i, ".phenotypes.Normalized")
file.names[i] <- dir("RAW_DATA/SOM_SCR_R002/", pattern = m, full.names = TRUE)
temp_df <- read.csv(file.names[i], na.strings = "NoGrowth")
data_AA_Norm <- rbind(data_AA_Norm, temp_df)
}
data_AA_Norm_Trim <- data_AA_Norm[, 4:5]
colnames(data_AA_Norm_Trim) <- c("AA_Y_NORM", "AA_GT_NORM")
R <- rep("2nd_round", 36864)
Round_ID <- data.frame(R, stringsAsFactors = FALSE)
whole_data_R2 <- cbind(Metadata_CRISPRi,
Round_ID,
data_Ctrl_Abs_Trim,
data_AA_Abs_Trim,
data_Ctrl_Norm_Trim,
data_AA_Norm_Trim)
colnames(whole_data_R2)[12] <- "Round_ID"
str(whole_data_R2)
First_round <- read.csv("COMPILED_DATA/20190903_CRISPRi_Screen_aa_1st_round.csv",
na.strings = c("#N/A", "NoGrowth"),
stringsAsFactors = FALSE)
R <- rep("1st_round", 36864)
Round_ID <- data.frame(R, stringsAsFactors = FALSE)
whole_data_R1 <- cbind(Metadata_CRISPRi, Round_ID, First_round[, 12:19])
colnames(whole_data_R1)[12] <- "Round_ID"
str(whole_data_R1)
whole_data_CRISPRi_aa <- rbind(whole_data_R1, whole_data_R2)
whole_data_CRISPRi_aa[, 21] <- whole_data_CRISPRi_aa[, 19]-whole_data_CRISPRi_aa[, 17]
whole_data_CRISPRi_aa[, 22] <- whole_data_CRISPRi_aa[, 20]-whole_data_CRISPRi_aa[, 18]
colnames(whole_data_CRISPRi_aa)[21] <- "LPI_Y"
colnames(whole_data_CRISPRi_aa)[22] <- "LPI_GT"
plate_ID <- as.character(unique(whole_data_CRISPRi_aa$SOURCEPLATEID))
whole_data_CRISPRi_aa_corrected <- whole_data_CRISPRi_aa
med_LogLSCctrl_RND1_GT <- vector(mode = "integer", length = 0)
med_LogLSCaa_RND1_GT <- vector(mode = "integer", length = 0)
med_LogLSCctrl_RND2_GT <- vector(mode = "integer", length = 0)
med_LogLSCaa_RND2_GT <- vector(mode = "integer", length = 0)
med_LogLSCctrl_RND1_Y <- vector(mode = "integer", length = 0)
med_LogLSCaa_RND1_Y <- vector(mode = "integer", length = 0)
med_LogLSCctrl_RND2_Y <- vector(mode = "integer", length = 0)
med_LogLSCaa_RND2_Y <- vector(mode = "integer", length = 0)
for(i in 1:24){
med_LogLSCctrl_RND1_GT[i] <- median(whole_data_CRISPRi_aa_corrected$CTRL_GT_NORM[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i]
& !is.na(whole_data_CRISPRi_aa_corrected$CTRL_GT_NORM)
& whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round")])
med_LogLSCaa_RND1_GT[i] <- median(whole_data_CRISPRi_aa_corrected$AA_GT_NORM[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i]
& !is.na(whole_data_CRISPRi_aa_corrected$AA_GT_NORM)
& whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round")])
med_LogLSCctrl_RND2_GT[i] <- median(whole_data_CRISPRi_aa_corrected$CTRL_GT_NORM[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i]
& !is.na(whole_data_CRISPRi_aa_corrected$CTRL_GT_NORM)
& whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round")])
med_LogLSCaa_RND2_GT[i] <- median(whole_data_CRISPRi_aa_corrected$AA_GT_NORM[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i]
& !is.na(whole_data_CRISPRi_aa_corrected$AA_GT_NORM)
& whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round")])
med_LogLSCctrl_RND1_Y[i] <- median(whole_data_CRISPRi_aa_corrected$CTRL_Y_NORM[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i]
& !is.na(whole_data_CRISPRi_aa_corrected$CTRL_Y_NORM)
& whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round")])
med_LogLSCaa_RND1_Y[i] <- median(whole_data_CRISPRi_aa_corrected$AA_Y_NORM[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i]
& !is.na(whole_data_CRISPRi_aa_corrected$AA_Y_NORM)
& whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round")])
med_LogLSCctrl_RND2_Y[i] <- median(whole_data_CRISPRi_aa_corrected$CTRL_Y_NORM[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i]
& !is.na(whole_data_CRISPRi_aa_corrected$CTRL_Y_NORM)
& whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round")])
med_LogLSCaa_RND2_Y[i] <- median(whole_data_CRISPRi_aa_corrected$AA_Y_NORM[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i]
& !is.na(whole_data_CRISPRi_aa_corrected$AA_Y_NORM)
& whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round")])
whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round") , 23] <- whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round"), 17] - med_LogLSCctrl_RND1_Y[i]
whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round") , 24] <- whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round"), 18] - med_LogLSCctrl_RND1_GT[i]
whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round") , 23] <- whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round"), 17] - med_LogLSCctrl_RND2_Y[i]
whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round") , 24] <- whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round"), 18] - med_LogLSCctrl_RND2_GT[i]
whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round") , 25] <- whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round"), 19] - med_LogLSCaa_RND1_Y[i]
whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round") , 26] <- whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="1st_round"), 20] - med_LogLSCaa_RND1_GT[i]
whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round") , 25] <- whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round"), 19] - med_LogLSCaa_RND2_Y[i]
whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round") , 26] <- whole_data_CRISPRi_aa_corrected[which(whole_data_CRISPRi_aa_corrected$SOURCEPLATEID==plate_ID[i] &
whole_data_CRISPRi_aa_corrected$Round_ID=="2nd_round"), 20] - med_LogLSCaa_RND2_GT[i]
}
whole_data_CRISPRi_aa_corrected[, 27] <- whole_data_CRISPRi_aa_corrected[, 25] - whole_data_CRISPRi_aa_corrected[, 23]
whole_data_CRISPRi_aa_corrected[, 28] <- whole_data_CRISPRi_aa_corrected[, 26] - whole_data_CRISPRi_aa_corrected[, 24]
colnm <- colnames(whole_data_CRISPRi_aa)[17:22]
colnm <- paste0(colnm, "_CR")
colnames(whole_data_CRISPRi_aa_corrected)[23:28] <- colnm
str(whole_data_CRISPRi_aa_corrected)
whole_data_CRISPRi_aa_2 <- whole_data_CRISPRi_aa_corrected[, c(1:16, 23:28)]
colnames(whole_data_CRISPRi_aa_2)[17:22] <- colnames(whole_data_CRISPRi_aa)[17:22]
str(whole_data_CRISPRi_aa_2)
Data_CRISPRi_aa <- subset(whole_data_CRISPRi_aa_2, whole_data_CRISPRi_aa_2$gRNA_name!="SP_Ctrl_CC23")
df_unique_sgRNA <- data.frame(table(Data_CRISPRi_aa$gRNA_name))
R1<-vector(mode = "integer", length = 0)
R2<-vector(mode = "integer", length = 0)
test2<-data.frame()
n<-nrow(df_unique_sgRNA)
for(i in 1:n){
R1 <- which(Data_CRISPRi_aa$gRNA_name==df_unique_sgRNA$Var1[i] & Data_CRISPRi_aa$Round_ID=="1st_round")
R2 <- which(Data_CRISPRi_aa$gRNA_name==df_unique_sgRNA$Var1[i] & Data_CRISPRi_aa$Round_ID=="2nd_round")
test1 <- Data_CRISPRi_aa[c(R1, R2), ]
test2[i, c(1:8)]<-test1[1, c(2:4, 6:7, 9:11)]
test2[i, c(9:14)] <- test1$CTRL_GT_NORM
test2[i, 15] <- mean(test1$CTRL_GT_NORM[1:3])
test2[i, 16] <- mean(test1$CTRL_GT_NORM[4:6])
test2[i, 17] <- sd(test1$CTRL_GT_NORM[1:3])
test2[i, 18] <- sd(test1$CTRL_GT_NORM[4:6])
test2[i, 19] <- mean(test1$CTRL_GT_NORM[1:6])
test2[i, 20] <- median(test1$CTRL_GT_NORM[1:6])
test2[i, 21] <- sd(test1$CTRL_GT_NORM[1:6])
test2[i, c(22:27)] <- test1$AA_GT_NORM
test2[i, 28] <- mean(test1$AA_GT_NORM[1:3])
test2[i, 29] <- mean(test1$AA_GT_NORM[4:6])
test2[i, 30] <- sd(test1$AA_GT_NORM[1:3])
test2[i, 31] <- sd(test1$AA_GT_NORM[4:6])
test2[i, 32] <- mean(test1$AA_GT_NORM[1:6])
test2[i, 33] <- median(test1$AA_GT_NORM[1:6])
test2[i, 34] <- sd(test1$AA_GT_NORM[1:6])
test2[i, c(35:40)] <- test1$LPI_GT
test2[i, 41] <- mean(test1$LPI_GT[1:3])
test2[i, 42] <- mean(test1$LPI_GT[4:6])
test2[i, 43] <- sd(test1$LPI_GT[1:3])
test2[i, 44] <- sd(test1$LPI_GT[4:6])
test2[i, 45] <- mean(test1$LPI_GT[1:6])
test2[i, 46] <- median(test1$LPI_GT[1:6])
test2[i, 47] <- sd(test1$LPI_GT[1:6])
test2[i, c(48:53)] <- test1$CTRL_Y_NORM
test2[i, 54] <- mean(test1$CTRL_Y_NORM[1:3])
test2[i, 55] <- mean(test1$CTRL_Y_NORM[4:6])
test2[i, 56] <- sd(test1$CTRL_Y_NORM[1:3])
test2[i, 57] <- sd(test1$CTRL_Y_NORM[4:6])
test2[i, 58] <- mean(test1$CTRL_Y_NORM[1:6])
test2[i, 59] <- median(test1$CTRL_Y_NORM[1:6])
test2[i, 60] <- sd(test1$CTRL_Y_NORM[1:6])
test2[i, c(61:66)] <- test1$AA_Y_NORM
test2[i, 67] <- mean(test1$AA_Y_NORM[1:3])
test2[i, 68] <- mean(test1$AA_Y_NORM[4:6])
test2[i, 69] <- sd(test1$AA_Y_NORM[1:3])
test2[i, 70] <- sd(test1$AA_Y_NORM[4:6])
test2[i, 71] <- mean(test1$AA_Y_NORM[1:6])
test2[i, 72] <- median(test1$AA_Y_NORM[1:6])
test2[i, 73] <- sd(test1$AA_Y_NORM[1:6])
test2[i, c(74:79)] <- test1$LPI_Y
test2[i, 80] <- mean(test1$LPI_Y[1:3])
test2[i, 81] <- mean(test1$LPI_Y[4:6])
test2[i, 82] <- sd(test1$LPI_Y[1:3])
test2[i, 83] <- sd(test1$LPI_Y[4:6])
test2[i, 84] <- mean(test1$LPI_Y[1:6])
test2[i, 85] <- median(test1$LPI_Y[1:6])
test2[i, 86] <- sd(test1$LPI_Y[1:6])
}
column_names <- read.table("COMPILED_DATA/Column_names.txt", header = FALSE, sep = "\t", as.is = TRUE)
colnames(test2) <- column_names$V1
Analysis_CRISPRi_aa_Complete <- test2
str(Analysis_CRISPRi_aa_Complete)
Analysis_Final <- Analysis_CRISPRi_aa_Complete
for(i in 1:nrow(Analysis_Final)){
x1 <- as.numeric(Analysis_Final[i, 9:11][which(!is.na(Analysis_Final[i, 9:11]))])
x2 <- as.numeric(Analysis_Final[i, 12:14][which(!is.na(Analysis_Final[i, 12:14]))])
if(length(x1)==0){
Analysis_Final$CTRL_GT_RND1_MEAN[i] <- NA
} else{
Analysis_Final$CTRL_GT_RND1_MEAN[i] <- as.numeric(mean(x1))
}
if(length(x2)==0){
Analysis_Final$CTRL_GT_RND2_MEAN[i] <- NA
} else{
Analysis_Final$CTRL_GT_RND2_MEAN[i] <- as.numeric(mean(x2))
}
if(sum(is.na(c(Analysis_Final$CTRL_GT_RND1_MEAN[i], Analysis_Final$CTRL_GT_RND2_MEAN[i])))==0){
Analysis_Final$CTRL_GT_RND1_2_MEAN[i] <- as.numeric(mean(c(Analysis_Final$CTRL_GT_RND1_MEAN[i], Analysis_Final$CTRL_GT_RND2_MEAN[i])))
Analysis_Final[i, 87] <- as.numeric(sd(c(Analysis_Final$CTRL_GT_RND1_MEAN[i], Analysis_Final$CTRL_GT_RND2_MEAN[i])))
} else{
Analysis_Final$CTRL_GT_RND1_2_MEAN[i] <- NA
Analysis_Final[i, 87] <- NA
}
}
colnames(Analysis_Final)[87] <- "CTRL_GT_MEAN_RND1_2_SD"
for(i in 1:nrow(Analysis_Final)){
x1 <- as.numeric(Analysis_Final[i, 22:24][which(!is.na(Analysis_Final[i, 22:24]))])
x2 <- as.numeric(Analysis_Final[i, 25:27][which(!is.na(Analysis_Final[i, 25:27]))])
if(length(x1)==0){
Analysis_Final$AA_GT_RND1_MEAN[i] <- NA
} else{
Analysis_Final$AA_GT_RND1_MEAN[i] <- as.numeric(mean(x1))
}
if(length(x2)==0){
Analysis_Final$AA_GT_RND2_MEAN[i] <- NA
} else{
Analysis_Final$AA_GT_RND2_MEAN[i] <- as.numeric(mean(x2))
}
if(sum(is.na(c(Analysis_Final$AA_GT_RND1_MEAN[i], Analysis_Final$AA_GT_RND2_MEAN[i])))==0){
Analysis_Final$AA_GT_RND1_2_MEAN[i] <- as.numeric(mean(c(Analysis_Final$AA_GT_RND1_MEAN[i], Analysis_Final$AA_GT_RND2_MEAN[i])))
Analysis_Final[i, 88] <- as.numeric(sd(c(Analysis_Final$AA_GT_RND1_MEAN[i], Analysis_Final$AA_GT_RND2_MEAN[i])))
} else{
Analysis_Final$AA_GT_RND1_2_MEAN[i] <- NA
Analysis_Final[i, 88] <- NA
}
}
colnames(Analysis_Final)[88] <- "AA_GT_MEAN_RND1_2_SD"
for(i in 1:nrow(Analysis_Final)){
x1 <- as.numeric(Analysis_Final[i, 35:37][which(!is.na(Analysis_Final[i, 35:37]))])
x2 <- as.numeric(Analysis_Final[i, 38:40][which(!is.na(Analysis_Final[i, 38:40]))])
if(length(x1)==0){
Analysis_Final$LPI_GT_RND1_MEAN[i] <- NA
} else{
Analysis_Final$LPI_GT_RND1_MEAN[i] <- as.numeric(mean(x1))
}
if(length(x2)==0){
Analysis_Final$LPI_GT_RND2_MEAN[i] <- NA
} else{
Analysis_Final$LPI_GT_RND2_MEAN[i] <- as.numeric(mean(x2))
}
if(sum(is.na(c(Analysis_Final$LPI_GT_RND1_MEAN[i], Analysis_Final$LPI_GT_RND2_MEAN[i])))==0){
Analysis_Final$LPI_GT_RND1_2_MEAN[i] <- as.numeric(mean(c(Analysis_Final$LPI_GT_RND1_MEAN[i], Analysis_Final$LPI_GT_RND2_MEAN[i])))
Analysis_Final[i, 89] <- as.numeric(sd(c(Analysis_Final$LPI_GT_RND1_MEAN[i], Analysis_Final$LPI_GT_RND2_MEAN[i])))
} else{
Analysis_Final$LPI_GT_RND1_2_MEAN[i] <- NA
Analysis_Final[i, 89] <- NA
}
}
colnames(Analysis_Final)[89] <- "LPI_GT_MEAN_RND1_2_SD"
box_stat_LPI_GT_R1_2_mean <- boxplot(Analysis_Final$LPI_GT_RND1_2_MEAN, cex=0.3)
box_stat_LPI_GT_R1_2_mean$stats
Intermediate_50 <- Analysis_Final$LPI_GT_RND1_2_MEAN[which(Analysis_Final$LPI_GT_RND1_2_MEAN>=-0.02428792
&Analysis_Final$LPI_GT_RND1_2_MEAN<=0.07255828)]
summary(Intermediate_50)
for(i in 1:nrow(Analysis_Final)){
if(sum(is.na(c(Analysis_Final$LPI_GT_RND1_MEAN[i], Analysis_Final$LPI_GT_RND2_MEAN[i])))==0){
P_value <- t.test(Intermediate_50, c(Analysis_Final$LPI_GT_RND1_MEAN[i], Analysis_Final$LPI_GT_RND2_MEAN[i]))
Analysis_Final[i, 90] <- P_value$p.value
} else{
Analysis_Final[i, 90] <- NA
}
}
colnames(Analysis_Final)[90] <- "P_value_M1"
Analysis_Final[which(!is.na(Analysis_Final$P_value_M1)), 91] <- p.adjust(Analysis_Final$P_value_M1[which(!is.na(Analysis_Final$P_value_M1))],
method = "BH",
n = length(Analysis_Final$P_value_M1[which(!is.na(Analysis_Final$P_value_M1))]))
colnames(Analysis_Final)[91] <- "P.adjusted_M1"
length(Analysis_Final$P_value_M1[which(Analysis_Final$P_value_M1<=0.05)])
length(Analysis_Final$P.adjusted_M1[which(Analysis_Final$P.adjusted_M1<=0.05)])
length(Analysis_Final$P_value_M1[which(Analysis_Final$P_value_M1<=0.1)])
length(Analysis_Final$P.adjusted_M1[which(Analysis_Final$P.adjusted_M1<=0.1)])
par(mfrow=c(2,2))
hist(Analysis_Final$P_value_M1,
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-values of all strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 1000))
hist(Analysis_Final$P_value_M1[which(Analysis_Final$Control.gRNA==1)],
breaks = 10,
xlab = "P-value",
ylab = "Frequency",
main = "P-values of control strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 5))
hist(Analysis_Final$P.adjusted_M1,
breaks = 20,
xlab = "P.adj",
ylab = "Frequency",
main = "P.adjusted values of all strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 1000))
hist(Analysis_Final$P.adjusted_M1[which(Analysis_Final$Control.gRNA==1)],
breaks = 2,
xlab = "P.adj",
ylab = "Frequency",
main = "P.adjusted values of control strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 10))
Analysis_Final_2 <- Analysis_Final
str(Analysis_Final_2)
CRISPRi_Ctrl_Round1 <- whole_data_CRISPRi_aa_2$LPI_GT[which(whole_data_CRISPRi_aa_2$Control.gRNA == 1
& whole_data_CRISPRi_aa_2$Round_ID=="1st_round")]
summary(CRISPRi_Ctrl_Round1)
CRISPRi_Ctrl_Round2 <- whole_data_CRISPRi_aa_2$LPI_GT[which(whole_data_CRISPRi_aa_2$Control.gRNA == 1
& whole_data_CRISPRi_aa_2$Round_ID=="2nd_round")]
summary(CRISPRi_Ctrl_Round2)
for(i in 1:nrow(Analysis_Final_2)){
test1 <- t(Analysis_Final_2[i, 35:37])
test2 <- t(Analysis_Final_2[i, 38:40])
if(sum(!is.na(test1[, 1]))>=2){
P_value_RND1 <- t.test(CRISPRi_Ctrl_Round1, test1[which(!is.na(test1[, 1]))])
Analysis_Final_2[i, 92] <- P_value_RND1$p.value
} else {
Analysis_Final_2[i, 92] <- NA
}
if(sum(!is.na(test2[, 1]))>=2){
P_value_RND2 <- t.test(CRISPRi_Ctrl_Round2, test2[which(!is.na(test2[, 1]))])
Analysis_Final_2[i, 93] <- P_value_RND2$p.value
} else {
Analysis_Final_2[i, 93] <- NA
}
}
colnames(Analysis_Final_2)[92:93] <- c("P_value_RND1_M2", "P_value_RND2_M2")
Analysis_Final_2[which(!is.na(Analysis_Final_2$P_value_RND1_M2)), 94] <- p.adjust(Analysis_Final_2$P_value_RND1_M2[which(!is.na(Analysis_Final_2$P_value_RND1_M2))],
method = "BH",
n = length(Analysis_Final_2$P_value_RND1_M2[which(!is.na(Analysis_Final_2$P_value_RND1_M2))]))
Analysis_Final_2[which(!is.na(Analysis_Final_2$P_value_RND2_M2)), 95] <- p.adjust(Analysis_Final_2$P_value_RND2_M2[which(!is.na(Analysis_Final_2$P_value_RND2_M2))],
method = "BH",
n = length(Analysis_Final_2$P_value_RND2_M2[which(!is.na(Analysis_Final_2$P_value_RND2_M2))]))
colnames(Analysis_Final_2)[94:95] <- c("P.adjusted_RND1_M2", "P.adjusted_RND2_M2")
length(Analysis_Final_2$P_value_RND1_M2[which(Analysis_Final_2$P_value_RND1_M2<=0.05)])
length(Analysis_Final_2$P.adjusted_RND1_M2[which(Analysis_Final_2$P.adjusted_RND1_M2<=0.05)])
length(Analysis_Final_2$P_value_RND1_M2[which(Analysis_Final_2$P_value_RND1_M2<=0.1)])
length(Analysis_Final_2$P.adjusted_RND1_M2[which(Analysis_Final_2$P.adjusted_RND1_M2<=0.1)])
par(mfrow=c(2,2))
hist(Analysis_Final_2$P_value_RND1_M2,
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-values of all strains ROUND1",
col = "skyblue",
ylim = c(0, 5000))
hist(Analysis_Final_2$P_value_RND1_M2[which(Analysis_Final_2$Control.gRNA==1)],
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-values of control strains ROUND1",
col = "skyblue",
ylim = c(0, 10))
hist(Analysis_Final_2$P.adjusted_RND1_M2,
breaks = 20,
xlab = "P.adj",
ylab = "Frequency",
main = "P.adjusted values of all strains ROUND1",
col = "skyblue",
ylim = c(0, 5000))
hist(Analysis_Final_2$P.adjusted_RND1_M2[which(Analysis_Final_2$Control.gRNA==1)],
breaks = 20,
xlab = "P.adj",
ylab = "Frequency",
main = "P.adjusted values of control strains ROUND1",
col = "skyblue",
ylim = c(0, 10))
length(Analysis_Final_2$P_value_RND2_M2[which(Analysis_Final_2$P_value_RND2_M2<=0.05)])
length(Analysis_Final_2$P.adjusted_RND2_M2[which(Analysis_Final_2$P.adjusted_RND2_M2<=0.05)])
length(Analysis_Final_2$P_value_RND2_M2[which(Analysis_Final_2$P_value_RND2_M2<=0.1)])
length(Analysis_Final_2$P.adjusted_RND2_M2[which(Analysis_Final_2$P.adjusted_RND2_M2<=0.1)])
par(mfrow=c(2,2))
hist(Analysis_Final_2$P_value_RND2_M2,
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-values of all strains ROUND2",
col = "skyblue",
ylim = c(0, 5000))
hist(Analysis_Final_2$P_value_RND2_M2[which(Analysis_Final_2$Control.gRNA==1)],
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-values of control strains ROUND2",
col = "skyblue",
ylim = c(0, 10))
hist(Analysis_Final_2$P.adjusted_RND2_M2,
breaks = 20,
xlab = "P.adj",
ylab = "Frequency",
main = "P.adjusted values of all strains ROUND2",
col = "skyblue",
ylim = c(0, 5000))
hist(Analysis_Final_2$P.adjusted_RND2_M2[which(Analysis_Final_2$Control.gRNA==1)],
breaks = 20,
xlab = "P.adj",
ylab = "Frequency",
main = "P.adjusted values of control strains ROUND2",
col = "skyblue",
ylim = c(0, 10))
Analysis_Final_3 <- Analysis_Final_2
boxplot_stat_LPI_GT <- boxplot(Data_CRISPRi_aa$LPI_GT, cex = 0.3)
boxplot_stat_LPI_GT$stats
Intermediate_50_M3 <- Data_CRISPRi_aa$LPI_GT[which(Data_CRISPRi_aa$LPI_GT >=-0.04373846
&Data_CRISPRi_aa$LPI_GT<=0.09804938)]
summary(Intermediate_50_M3)
Crispri_control_M4 <- Data_CRISPRi_aa$LPI_GT[which(Data_CRISPRi_aa$Control.gRNA==1)]
summary(Crispri_control_M4)
for(i in 1:nrow(Analysis_Final_3)){
test1 <- t(Analysis_Final_3[i, 9:14])
test2 <- t(Analysis_Final_3[i, 35:40])
x1 <- sum(!is.na(test1[, 1]))
x2 <- sum(!is.na(test2[, 1]))
CTRL_GT_Mean_temp <- mean(test1[which(!is.na(test1[, 1]))])
LPI_GT_Mean_temp <- mean(test2[which(!is.na(test2[, 1]))])
Analysis_Final_3[i, 96] <- CTRL_GT_Mean_temp
Analysis_Final_3[i, 97] <- x1
Analysis_Final_3[i, 98] <- LPI_GT_Mean_temp
Analysis_Final_3[i, 99] <- x2
}
colnames(Analysis_Final_3)[96:99] <- c("CTRL_GT_Mean_all", "n_CTRL", "LPI_GT_Mean_all", "n_LPI")
for(i in 1:nrow(Analysis_Final_3)){
test <- t(Analysis_Final_3[i, 35:40])
x <- sum(!is.na(test[, 1]))
if(x>2){
P.value_temp_M3 <- t.test(Intermediate_50_M3, test[which(!is.na(test[, 1]))])
P.value_temp_M4 <- t.test(Crispri_control_M4, test[which(!is.na(test[, 1]))])
Analysis_Final_3[i, 100] <- P.value_temp_M3$p.value
Analysis_Final_3[i, 101] <- P.value_temp_M4$p.value
} else {
Analysis_Final_3[i, 100] <- NA
Analysis_Final_3[i, 101] <- NA
}
}
colnames(Analysis_Final_3)[100:101] <- c("P.value_M3", "P.value_M4")
Analysis_Final_3[which(!is.na(Analysis_Final_3$P.value_M3)), 102] <- p.adjust(Analysis_Final_3$P.value_M3[which(!is.na(Analysis_Final_3$P.value_M3))],
method = "BH",
n = length(Analysis_Final_3$P.value_M3[which(!is.na(Analysis_Final_3$P.value_M3))]))
Analysis_Final_3[which(!is.na(Analysis_Final_3$P.value_M4)), 103] <- p.adjust(Analysis_Final_3$P.value_M4[which(!is.na(Analysis_Final_3$P.value_M4))],
method = "BH",
n = length(Analysis_Final_3$P.value_M4[which(!is.na(Analysis_Final_3$P.value_M4))]))
colnames(Analysis_Final_3)[102:103] <- c("P.adjusted_M3", "P.adjusted_M4")
length(Analysis_Final_3$P.value_M3[which(Analysis_Final_3$P.value_M3<=0.05)])
length(Analysis_Final_3$P.adjusted_M3[which(Analysis_Final_3$P.adjusted_M3<=0.05)])
length(Analysis_Final_3$P.value_M3[which(Analysis_Final_3$P.value_M3<=0.1)])
length(Analysis_Final_3$P.adjusted_M3[which(Analysis_Final_3$P.adjusted_M3<=0.1)])
par(mfrow=c(2,2))
hist(Analysis_Final_3$P.value_M3,
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-value of all strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 3000),
cex.lab= 1.5)
hist(Analysis_Final_3$P.value_M3[which(Analysis_Final_3$Control.gRNA==1)],
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-value of control strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 10),
cex.lab= 1.5)
hist(Analysis_Final_3$P.adjusted_M3,
breaks = 20,
xlab = "P.value adjusted",
ylab = "Frequency",
main = "P.adjusted values of all strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 3000),
cex.lab= 1.5)
hist(Analysis_Final_3$P.adjusted_M3[which(Analysis_Final_3$Control.gRNA==1)],
breaks = 20,
xlab = "P.value adjusted",
ylab = "Frequency",
main = "P.adjusted values of control strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 10),
cex.lab= 1.5)
length(Analysis_Final_3$P.value_M4[which(Analysis_Final_3$P.value_M4<=0.05)])
length(Analysis_Final_3$P.adjusted_M4[which(Analysis_Final_3$P.adjusted_M4<=0.05)])
length(Analysis_Final_3$P.value_M4[which(Analysis_Final_3$P.value_M4<=0.1)])
length(Analysis_Final_3$P.adjusted_M4[which(Analysis_Final_3$P.adjusted_M4<=0.1)])
par(mfrow=c(2,2))
hist(Analysis_Final_3$P.value_M4,
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-value of all strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 3000),
cex.lab= 1.5)
hist(Analysis_Final_3$P.value_M4[which(Analysis_Final_3$Control.gRNA==1)],
breaks = 20,
xlab = "P-value",
ylab = "Frequency",
main = "P-value of control strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 10),
cex.lab= 1.5)
hist(Analysis_Final_3$P.adjusted_M4,
breaks = 20,
xlab = "P.value adjusted",
ylab = "Frequency",
main = "P.adjusted values of all strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 3000),
cex.lab= 1.5)
hist(Analysis_Final_3$P.adjusted_M4[which(Analysis_Final_3$Control.gRNA==1)],
breaks = 20,
xlab = "P.value adjusted",
ylab = "Frequency",
main = "P.adjusted values of control strains",
col = "skyblue",
xlim = c(0, 1),
ylim = c(0, 10),
cex.lab= 1.5)
length(Analysis_Final_3$P.adjusted_M3[which(Analysis_Final_3$P.adjusted_M3 <= 0.1)])
max(Analysis_Final_3$LPI_GT_Mean_all[which(Analysis_Final_3$Control.gRNA==1)])
min(Analysis_Final_3$LPI_GT_Mean_all[which(Analysis_Final_3$Control.gRNA==1)])
candidate_padj_0.1_FIT_M3 <- which((Analysis_Final_3$LPI_GT_Mean_all < -0.03680838 & Analysis_Final_3$P.adjusted_M3<= 0.1))
length(candidate_padj_0.1_FIT_M3)
Fit_M3_complete <- Analysis_Final_3[candidate_padj_0.1_FIT_M3, ]
Fit_M3_complete <- Fit_M3_complete[order(Fit_M3_complete$LPI_GT_Mean_all, decreasing = FALSE), ]
str(Fit_M3_complete)
whole_Gene_list_Final <- read.csv("COMPILED_DATA/Gene_List_CRISPRi_lib.csv", na.strings = "", stringsAsFactors = FALSE)
rownames(whole_Gene_list_Final) <- whole_Gene_list_Final$LIB_ID
Fit_all_M3 <- data.frame(sort(table(Analysis_Final_3$GENE[candidate_padj_0.1_FIT_M3]), decreasing = TRUE))
y <- as.character(Fit_all_M3$Var1)
x <- whole_Gene_list_Final[y, ]
Fit_all_M3_description <- cbind(Fit_all_M3, x[, -1])
str(Fit_all_M3_description)
nrow(Fit_all_M3_description)
super_sen_M3 <- Analysis_Final_3[which(!is.na(Analysis_Final_3$CTRL_GT_Mean_all)
&(Analysis_Final_3$n_LPI<3)
&(
is.na(Analysis_Final_3$LPI_GT_Mean_all)
|(Analysis_Final_3$LPI_GT_Mean_all> 0.165662)
)
), ]
nrow(super_sen_M3)
candidate_padj_0.1_SEN_M3 <- which((Analysis_Final_3$LPI_GT_Mean_all > 0.165662 & Analysis_Final_3$P.adjusted_M3<= 0.1))
length(candidate_padj_0.1_SEN_M3)
Sen_M3_complete <- rbind(super_sen_M3, Analysis_Final_3[candidate_padj_0.1_SEN_M3, ])
Sen_M3_complete <- Sen_M3_complete[order(Sen_M3_complete$LPI_GT_Mean_all, decreasing = TRUE), ]
nrow(Sen_M3_complete)
Sen_all_M3 <- data.frame(sort(table(c(Analysis_Final_3$GENE[candidate_padj_0.1_SEN_M3], super_sen_M3$GENE)), decreasing = TRUE))
y <- as.character(Sen_all_M3$Var1)
x <- whole_Gene_list_Final[y, ]
Sen_all_M3_description <- cbind(Sen_all_M3, x[, -1])
nrow(Sen_all_M3_description)
Fit_unique_M3 <- as.character(Fit_all_M3$Var1)
x <- whole_Gene_list_Final[Fit_unique_M3, ]
Fit_unique_M3_SGD_ID <- x$SGD_DB_ID
str(Fit_unique_M3_SGD_ID)
Sen_unique_M3 <- as.character(Sen_all_M3$Var1)
x <- whole_Gene_list_Final[Sen_unique_M3, ]
Sen_unique_M3_SGD_ID <- x$SGD_DB_ID
str(Sen_unique_M3_SGD_ID)
Growth_curve_data <- read.csv("COMPILED_DATA/Data_for_Representative_GC_SOM.csv", sep = "\t", header = TRUE)
str(Growth_curve_data)
Growth_curve_data_selected <- Growth_curve_data[, c("X", "X0_20_3", "X2_20_3", "X0_4_3", "X2_4_3", "X1_30_22", "X3_30_22")]
colnames(Growth_curve_data_selected) <- c("Time", "POL2-NRg-1_Basal", "POL2-NRg-1_Acetic", "RRP15-TRg-4_Basal", "RRP15-TRg-4_Acetic", "CC23_Basal", "CC23_Acetic")
str(Growth_curve_data_selected)
Growth_curve_data_selected[, 1] <- Growth_curve_data_selected[, 1]*20/60
library(reshape)
Growth_curve_data_selected_long <- reshape(data=Growth_curve_data_selected, idvar="Time",
varying = colnames(Growth_curve_data_selected)[2:7],
v.name=c("Population_size"),
new.row.names = 1:30000,
direction="long",
timevar = "gRNA_condition",
times = colnames(Growth_curve_data_selected)[2:7])
str(Growth_curve_data_selected_long)
library(ggplot2)
ggplot(Growth_curve_data_selected_long, aes(x=Time, y=Population_size, color=gRNA_condition)) +
geom_smooth() +
scale_y_continuous(trans='log10') +
theme_classic()
plot(Analysis_Final_3$LPI_GT_RND1_MEAN, Analysis_Final_3$LPI_GT_RND2_MEAN,
pch = 16,
cex = 0.5,
col = "black",
main = "Correlation between mean relative generation time (LPI GT) of Round 1 and 2",
xlab = "LPI GT Round1",
ylab = "LPI GT Round2",
xlim = c(-0.5, 2),
ylim = c(-0.5, 2),
cex.lab=1.3,
cex.axis=1.3)
points(Analysis_Final_3$LPI_GT_RND1_MEAN[candidate_padj_0.1_FIT_M3],
Analysis_Final_3$LPI_GT_RND2_MEAN[candidate_padj_0.1_FIT_M3],
pch=16,
cex = 0.7,
col = "blue")
points(Analysis_Final_3$LPI_GT_RND1_MEAN[candidate_padj_0.1_SEN_M3],
Analysis_Final_3$LPI_GT_RND2_MEAN[candidate_padj_0.1_SEN_M3],
pch=16,
cex = 0.7,
col = "red")
points(Analysis_Final_3$LPI_GT_RND1_MEAN[which(Analysis_Final_3$Control.gRNA==1)],
Analysis_Final_3$LPI_GT_RND2_MEAN[which(Analysis_Final_3$Control.gRNA==1)],
pch=16,
cex = 0.7,
col = "green")
stats_LPI_GT_Mean_RND1vsRND2_M3 <- lm(LPI_GT_RND2_MEAN ~ LPI_GT_RND1_MEAN, data = Analysis_Final_3)
stats_LPI_GT_Mean_RND1vsRND2_M3_selected <- lm(LPI_GT_RND2_MEAN[c(candidate_padj_0.1_SEN_M3, candidate_padj_0.1_FIT_M3)] ~ LPI_GT_RND1_MEAN[c(candidate_padj_0.1_SEN_M3, candidate_padj_0.1_FIT_M3)], data = Analysis_Final_3)
abline(stats_LPI_GT_Mean_RND1vsRND2_M3, lty=2, lwd=2)
abline(stats_LPI_GT_Mean_RND1vsRND2_M3_selected, col="red", lty=2, lwd=2)
summary(stats_LPI_GT_Mean_RND1vsRND2_M3)
cor(Analysis_Final_3$LPI_GT_RND1_MEAN,
Analysis_Final_3$LPI_GT_RND2_MEAN,
method = "pearson",
use = "complete.obs")
summary(stats_LPI_GT_Mean_RND1vsRND2_M3_selected)
cor(Analysis_Final_3$LPI_GT_RND1_MEAN[c(candidate_padj_0.1_SEN_M3, candidate_padj_0.1_FIT_M3)],
Analysis_Final_3$LPI_GT_RND2_MEAN[c(candidate_padj_0.1_SEN_M3, candidate_padj_0.1_FIT_M3)],
method = "pearson",
use = "complete.obs")
plot(Analysis_Final_3$CTRL_GT_Mean_all, Analysis_Final_3$LPI_GT_Mean_all,
pch = 16,
cex = 0.5,
col = "black",
main = "Selection of sensitive and tolerant strains",
xlab = "Normalized generation time (LSC GT) Basal.condition",
ylab = "Relative generation time (LPI GT) in 150mM Acetic acid",
xlim = c(-0.5, 2),
ylim = c(-0.5, 2),
yaxt="n",
xaxt="n",
cex.lab=1.5)
points(Analysis_Final_3$CTRL_GT_Mean_all[candidate_padj_0.1_FIT_M3],
Analysis_Final_3$LPI_GT_Mean_all[candidate_padj_0.1_FIT_M3],
pch = 16,
cex = 0.5,
col = "blue")
points(Analysis_Final_3$CTRL_GT_Mean_all[candidate_padj_0.1_SEN_M3],
Analysis_Final_3$LPI_GT_Mean_all[candidate_padj_0.1_SEN_M3],
pch = 16,
cex = 0.5,
col = "red")
points(super_sen_M3$CTRL_GT_Mean_all,
super_sen_M3$LPI_GT_Mean_all,
pch = 16,
cex = 0.7,
col = "red")
points(Analysis_Final_3$CTRL_GT_Mean_all[which(Analysis_Final_3$Control.gRNA==1)],
Analysis_Final_3$LPI_GT_Mean_all[which(Analysis_Final_3$Control.gRNA==1)],
pch = 16,
cex = 0.6,
col = "green")
axis(side = 2,
at = c(-0.5, 0, 0.5, 1, 1.5, 2),
cex.axis = 1.2,
labels = c("-0.5", "0", "0.5", "1", "1.5", "2"),
tick = 0.05)
axis(side = 1,
at = c(-0.5, 0, 0.5, 1, 1.5, 2),
cex.axis = 1.2,
labels = c("-0.5", "0", "0.5", "1", "1.5", "2"),
tick = 0.05)
abline(h=c(-0.03680838, 0.165662), col="gray", lty=2, lwd=2)
Violin_LPI_Mean_M3 <- data.frame()
R <- length(which((Analysis_Final_3$Control.gRNA==0)
&(!is.na(Analysis_Final_3$LPI_GT_Mean_all))
))
Violin_LPI_Mean_M3[1:R, 1] <- Analysis_Final_3$LPI_GT_Mean_all[which((Analysis_Final_3$Control.gRNA==0)
&(!is.na(Analysis_Final_3$LPI_GT_Mean_all)))]
Violin_LPI_Mean_M3[1:R, 2] <- "ALL"
R2 <- length(which(Analysis_Final_3$Control.gRNA==1))
Violin_LPI_Mean_M3[(R+1):(R+R2), 1] <- Analysis_Final_3$LPI_GT_Mean_all[which(Analysis_Final_3$Control.gRNA==1)]
Violin_LPI_Mean_M3[(R+1):(R+R2), 2] <- "CONTROL"
colnames(Violin_LPI_Mean_M3)[1:2] <- c("Mean", "Label")
library(ggplot2)
p_gg_M3 <- ggplot(Violin_LPI_Mean_M3, aes(x=Label, y=Mean, fill=Label)) +
geom_violin(trim=FALSE) +
geom_boxplot(width=0.1, fill="white") +
labs(title="Violin plot",x="Data Type", y = "LPI_GT") +
scale_fill_manual(values=c("white", "green"))
p_gg_M3 + theme_classic()
library("wordcloud")
wordcloud(words = Fit_all_M3$Var1,
freq = Fit_all_M3$Freq,
min.freq = 2,
random.order=FALSE, rot.per=0.35,
colors=c("black", "red", "dark green", "blue"))
library("wordcloud")
wordcloud(words = Sen_all_M3$Var1,
freq = Sen_all_M3$Freq,
min.freq = 2,
random.order=FALSE, rot.per=0.35,
colors=c("black", "red", "dark green", "blue"))
row.names(Analysis_Final_3) <- Analysis_Final_3$gRNA_name
Smith_Yepg_data <- read.csv("COMPILED_DATA/smith_YEPGdata.csv", na.strings = "")
str(Smith_Yepg_data)
for(i in 1:nrow(Analysis_Final_3)){
x <- which(row.names(Analysis_Final_3)[i]==Smith_Yepg_data$guide_id)
if(length(x)==0){
Analysis_Final_3[i, 104:107] <- NA
} else {
Analysis_Final_3[i, 104:107] <- Smith_Yepg_data[x, 3:6]
}
}
colnames(Analysis_Final_3)[104:107] <- colnames(Smith_Yepg_data)[3:6]
str((Analysis_Final_3)[104:107])
gRNA_Freq <- data.frame(sort(table(Analysis_Final_3$GENE), decreasing = TRUE))
par(mfrow=c(2,1))
hist(gRNA_Freq$Freq,
breaks = 20,
xlim = c(0, 20),
ylim = c(0, 300),
xlab = "Strains per gene",
ylab = "Genes per bin",
col = "skyblue",
main = "Total number of strains per Gene")
hist(Analysis_Final_3$Midpoint_TSS_dist,
breaks = 20,
xlim=c(-300, 300),
ylim=c(0, 1200),
xlab = "Distance of gRNA relative to TSS",
ylab = "Number of gRNA per bin",
col = "skyblue",
main= "Frequency of gRNA distance from TSS")
for(i in 1:nrow(Analysis_Final_3)){
test1 <- t(Analysis_Final_3[i, 22:27])
x1 <- sum(!is.na(test1[, 1]))
AA_GT_Mean_temp <- mean(test1[which(!is.na(test1[, 1]))])
Analysis_Final_3[i, 32] <- AA_GT_Mean_temp
}
par(mfrow=c(2,1))
hist(Analysis_Final_3$CTRL_GT_Mean_all,
breaks = 25,
xlim = c(-0.5, 2),
ylim = c(0, 3000),
xlab = "",
ylab= "Strains per bin at Basal condition",
col = "#A0A0A0",
main = " Normalized generation time
in basal and under acetic acid stress")
abline(v= c(log2(0.9), log2(1.1)), col ="black", lty=2)
hist(Analysis_Final_3$AA_GT_RND1_2_MEAN,
breaks = 100,
xlim = c(-0.5, 2),
ylim = c(0, 3000),
xlab = "Normalized generation time LSC GT",
ylab= "Strains per bin at 150mM acetic acid",
main = "",
col = "#FF33FF")
abline(v= c(log2(0.9), log2(1.1)), col ="black", lty=2)
for(i in 1:nrow(whole_Gene_list_Final)){
x <- as.character(unique(Smith_Yepg_data$ORF_Category[(Smith_Yepg_data$gene_name %in% whole_Gene_list_Final$LIB_ID[i])]))
if(length(x)==0){
whole_Gene_list_Final[i, 8] <- NA
} else{
whole_Gene_list_Final[i, 8] <- x
}
}
colnames(whole_Gene_list_Final)[8] <- "ORF_Category"
whole_Gene_list_Final$ORF_Category <- as.factor(whole_Gene_list_Final$ORF_Category)
#Missing values were obtained from SGD
whole_Gene_list_Final$ORF_Category[which(is.na(whole_Gene_list_Final$ORF_Category))] <- c("Respiratory",
"Other",
"Essential",
"Essential",
"Respiratory",
"Other",
"Respiratory",
"Respiratory",
"Other",
"Respiratory",
"Essential",
"Essential",
"Respiratory")
str(whole_Gene_list_Final)
Atc_liq_data <- read.csv("COMPILED_DATA/ATc_liq_titer_data.csv", na.strings = "NaN", header = TRUE)
str(Atc_liq_data)
Atc_liq_cc23 <- Atc_liq_data[which(Atc_liq_data$gRNA_name=="Ctrl-CC23"), ]
uniq_gRNA <- unique(Atc_liq_data$gRNA_name)
uniq_conc <- unique(Atc_liq_data$Atc_concentration)
Atc_liq_data[, 10:15] <- log(Atc_liq_data[, 4:9])
for(i in 1:length(uniq_gRNA)){
for(j in 1:length(uniq_conc)){
Atc_liq_data[which(Atc_liq_data$gRNA_name==uniq_gRNA[i]&
Atc_liq_data$Atc_concentration==uniq_conc[j]), 16:21] <-
Atc_liq_data[which(Atc_liq_data$gRNA_name==uniq_gRNA[i]&
Atc_liq_data$Atc_concentration==uniq_conc[j]), 10:15] -
Atc_liq_data[which(Atc_liq_data$gRNA_name=="Ctrl-CC23"&
Atc_liq_data$Atc_concentration==uniq_conc[j]), 10:15]
}
}
for(i in 1:nrow(Atc_liq_data)){
Atc_liq_data[i, 22] <- mean(as.numeric(Atc_liq_data[i, c(16, 19)][which(!is.na(Atc_liq_data[i, c(16, 19)]))]))
Atc_liq_data[i, 23] <- sd(as.numeric(Atc_liq_data[i, c(16, 19)][which(!is.na(Atc_liq_data[i, c(16, 19)]))]))
Atc_liq_data[i, 24] <- mean(as.numeric(Atc_liq_data[i, c(17, 20)][which(!is.na(Atc_liq_data[i, c(17, 20)]))]))
Atc_liq_data[i, 25] <- sd(as.numeric(Atc_liq_data[i, c(17, 20)][which(!is.na(Atc_liq_data[i, c(17, 20)]))]))
Atc_liq_data[i, 26] <- mean(as.numeric(Atc_liq_data[i, c(18, 21)][which(!is.na(Atc_liq_data[i, c(18, 21)]))]))
Atc_liq_data[i, 27] <- sd(as.numeric(Atc_liq_data[i, c(18, 21)][which(!is.na(Atc_liq_data[i, c(18, 21)]))]))
}
colnames(Atc_liq_data)[10:15] <- paste0("log_", colnames(Atc_liq_data)[4:9])
colnames(Atc_liq_data)[16:21] <- paste0("LSC_", colnames(Atc_liq_data)[4:9])
colnames(Atc_liq_data)[22:23] <- paste0(c("Mean_", "SD_"), "LSC_Lag")
colnames(Atc_liq_data)[24:25] <- paste0(c("Mean_", "SD_"), "LSC_GT")
colnames(Atc_liq_data)[26:27] <- paste0(c("Mean_", "SD_"), "LSC_Yield")
name_gRNA_atc_titer <- c("ACT1-NRg-5", "ACT1-NRg-8", "Ctrl-CC11", "SEC21-NRg-5", "VPS1-TRg-1")
test <- data.frame()
Atc_titer_subset <- data.frame()
for(i in 1:length(name_gRNA_atc_titer)){
test <- Atc_liq_data[which(Atc_liq_data$gRNA_name==name_gRNA_atc_titer[i]), ]
Atc_titer_subset <- rbind(Atc_titer_subset, test)
}
library(ggplot2)
plt1 <- ggplot(Atc_titer_subset, aes(x=Atc_concentration, y=Mean_LSC_Lag, group=gRNA_name, color=gRNA_name)) +
geom_pointrange(aes(ymin=Mean_LSC_Lag-SD_LSC_Lag, ymax=Mean_LSC_Lag+SD_LSC_Lag)) +
labs(title="Normalized Lag phase at different ATc concentration", x="ATc (ug/ml)", y = "LSC Lag phase")+
theme_classic()+
scale_color_manual(values=c('#999999','#E69F00', "green3", "red", "black"))+
scale_x_continuous(breaks = c(0, 1, 2, 3, 5, 7, 10, 15, 25),
labels = c("0", "1", "2", "3", "5", "7", "10", "15", "25"),
limits = c(0, 26))+
scale_y_continuous(breaks = c(-1, -0.5, 0, 0.5, 1, 1.5, 2),
labels = c("-1", "-0.5", "0", "0.5", "1", "1.5", "2"),
limits = c(-1, 2))+
theme(legend.position="none")
suppressWarnings(print(plt1))
plt2 <- ggplot(Atc_titer_subset, aes(x=Atc_concentration, y=Mean_LSC_GT, group=gRNA_name, color=gRNA_name)) +
geom_pointrange(aes(ymin=Mean_LSC_GT-SD_LSC_GT, ymax=Mean_LSC_GT+SD_LSC_GT)) +
labs(title="Normalized Generation time at different ATc concentration", x="ATc (ug/ml)", y = "LSC GT")+
theme_classic()+
scale_color_manual(values=c('#999999','#E69F00', "green3", "red", "black"))+
scale_x_continuous(breaks = c(0, 1, 2, 3, 5, 7, 10, 15, 25),
labels = c("0", "1", "2", "3", "5", "7", "10", "15", "25"),
limits = c(0, 26))+
scale_y_continuous(breaks = c(-1, -0.5, 0, 0.5, 1, 1.5, 2),
labels = c("-1", "-0.5", "0", "0.5", "1", "1.5", "2"),
limits = c(-1, 2))+
theme(legend.position="none")
suppressWarnings(print(plt2))