1_Import Data.R
# Load the libraries
library("gplots")
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
library("RColorBrewer")
library("dplyr")
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library("matrixStats")
##
## Attaching package: 'matrixStats'
## The following object is masked from 'package:dplyr':
##
## count
B1_P1 <- read.csv("B1_P1.csv", fileEncoding = 'UTF-8-BOM')
B1_P2 <- read.csv("B1_P2.csv", fileEncoding = 'UTF-8-BOM')
B1_P3 <- read.csv("B1_P3.csv", fileEncoding = 'UTF-8-BOM')
B1_P4 <- read.csv("B1_P4.csv", fileEncoding = 'UTF-8-BOM')
B1_P5 <- read.csv("B1_P5.csv", fileEncoding = 'UTF-8-BOM')
B1_P6 <- read.csv("B1_P6.csv", fileEncoding = 'UTF-8-BOM')
B1_P7 <- read.csv("B1_P7.csv", fileEncoding = 'UTF-8-BOM')
B1_P8 <- read.csv("B1_P8.csv", fileEncoding = 'UTF-8-BOM')
B1_P9 <- read.csv("B1_P9.csv", fileEncoding = 'UTF-8-BOM')
B1_P10 <- read.csv("B1_P10.csv", fileEncoding = 'UTF-8-BOM')
B1_P11 <- read.csv("B1_P11.csv", fileEncoding = 'UTF-8-BOM')
B1_P12 <- read.csv("B1_P12.csv", fileEncoding = 'UTF-8-BOM')
B1_P13 <- read.csv("B1_P13.csv", fileEncoding = 'UTF-8-BOM')
B1_P14 <- read.csv("B1_P14.csv", fileEncoding = 'UTF-8-BOM')
B1_P15 <- read.csv("B1_P15.csv", fileEncoding = 'UTF-8-BOM')
B2_P1 <- read.csv("B2_P1.csv", fileEncoding = 'UTF-8-BOM')
B2_P2 <- read.csv("B2_P2.csv", fileEncoding = 'UTF-8-BOM')
B2_P3 <- read.csv("B2_P3.csv", fileEncoding = 'UTF-8-BOM')
B2_P4 <- read.csv("B2_P4.csv", fileEncoding = 'UTF-8-BOM')
B2_P5 <- read.csv("B2_P5.csv", fileEncoding = 'UTF-8-BOM')
B2_P6 <- read.csv("B2_P6.csv", fileEncoding = 'UTF-8-BOM')
B2_P7 <- read.csv("B2_P7.csv", fileEncoding = 'UTF-8-BOM')
B2_P8 <- read.csv("B2_P8.csv", fileEncoding = 'UTF-8-BOM')
B2_P9 <- read.csv("B2_P9.csv", fileEncoding = 'UTF-8-BOM')
B2_P10 <- read.csv("B2_P10.csv", fileEncoding = 'UTF-8-BOM')
B2_P11 <- read.csv("B2_P11.csv", fileEncoding = 'UTF-8-BOM')
B2_P12 <- read.csv("B2_P12.csv", fileEncoding = 'UTF-8-BOM')
B2_P13 <- read.csv("B2_P13.csv", fileEncoding = 'UTF-8-BOM')
B2_P14 <- read.csv("B2_P14.csv", fileEncoding = 'UTF-8-BOM')
B2_P15 <- read.csv("B2_P15.csv", fileEncoding = 'UTF-8-BOM')
2_Tidy Measures.R
# Column Number for Measures
Measure_Names <-read.csv("Measure_Names.csv", fileEncoding = 'UTF-8-BOM')
## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## incomplete final line found by readTableHeader on 'Measure_Names.csv'
Measure_Names <- colnames(Measure_Names)
B1_P1 <- B1_P1%>%
select(one_of(Measure_Names))
# List of data frame names
data_frame_names <- c("B1_P1", "B1_P2", "B1_P3", "B1_P4", "B1_P5", "B1_P6", "B1_P7", "B1_P8", "B1_P9", "B1_P10",
"B1_P11", "B1_P12", "B1_P13", "B1_P14", "B1_P15", "B2_P1", "B2_P2", "B2_P3", "B2_P4", "B2_P5",
"B2_P6", "B2_P7", "B2_P8", "B2_P9", "B2_P10", "B2_P11", "B2_P12", "B2_P13", "B2_P14", "B2_P15")
# Create a list of data frames
data_frames_list <- lapply(data_frame_names, function(df_name) {
read.csv(paste0(df_name, ".csv"), fileEncoding = 'UTF-8-BOM') %>%
select(one_of(Measure_Names))
})
# Optionally, if you want to assign the modified data frames back to the original variables
# Note: This will overwrite the original data frames
for (i in seq_along(data_frame_names)) {
assign(data_frame_names[i], data_frames_list[[i]])
}
4_Create Batches.R
# Update Batch_1
Batch_1_siGlo <- bind_rows(B1_P1_siGlo, B1_P2_siGlo, B1_P3_siGlo, B1_P4_siGlo, B1_P5_siGlo,
B1_P6_siGlo, B1_P7_siGlo, B1_P8_siGlo, B1_P9_siGlo, B1_P10_siGlo,
B1_P11_siGlo, B1_P12_siGlo, B1_P13_siGlo, B1_P14_siGlo, B1_P15_siGlo)
# Update Batch_2
Batch_2_siGlo <- bind_rows(B2_P1_siGlo, B2_P2_siGlo, B2_P3_siGlo, B2_P4_siGlo, B2_P5_siGlo,
B2_P6_siGlo, B2_P7_siGlo, B2_P8_siGlo, B2_P9_siGlo, B2_P10_siGlo,
B2_P11_siGlo, B2_P12_siGlo, B2_P13_siGlo, B2_P14_siGlo, B2_P15_siGlo)
5_Calculate Batch Means.R
#Batch1
Batch_1_Means <- colMeans(Batch_1_siGlo[,-c(1,2)])
Batch_1_Means <- c("Mean","Mean", Batch_1_Means)
Batch_1_Means_DF <- rbind(Batch_1_siGlo,Batch_1_Means, stringsAsFactors = F)
Batch_1_SD <- Batch_1_siGlo %>%
select_if(is.numeric) %>%
summarise_all(sd, na.rm = TRUE) %>%
unlist(use.names = FALSE) %>%
c("SD","SD", .)
Batch_1_siGlo_Complete <- rbind(Batch_1_Means_DF,Batch_1_SD, stringsAsFactors = F)
#Batch2
Batch_2_Means <- colMeans(Batch_2_siGlo[,-c(1,2)])
Batch_2_Means <- c("Mean","Mean", Batch_2_Means)
Batch_2_Means_DF <- rbind(Batch_2_siGlo,Batch_2_Means, stringsAsFactors = F)
Batch_2_SD <- Batch_2_siGlo %>%
select_if(is.numeric) %>%
summarise_all(sd, na.rm = TRUE) %>%
unlist(use.names = FALSE) %>%
c("SD","SD", .)
Batch_2_siGlo_Complete <- rbind(Batch_2_Means_DF,Batch_2_SD, stringsAsFactors = F)
6_Write Batch siGlo Controls.R
write.csv(Batch_1_siGlo_Complete,"Batch_1_Complete.csv", row.names = FALSE)
write.csv(Batch_2_siGlo_Complete,"Batch_2_Complete.csv", row.names = FALSE)
7_Add Batch Means.R
#Add Batch 1 Mean/SD
B1_P1 <- rbind(B1_P1, Batch_1_Means,Batch_1_SD)
B1_P2 <- rbind(B1_P2, Batch_1_Means,Batch_1_SD)
B1_P3 <- rbind(B1_P3, Batch_1_Means,Batch_1_SD)
B1_P4 <- rbind(B1_P4, Batch_1_Means,Batch_1_SD)
B1_P5 <- rbind(B1_P5, Batch_1_Means,Batch_1_SD)
B1_P6 <- rbind(B1_P6, Batch_1_Means,Batch_1_SD)
B1_P7 <- rbind(B1_P7, Batch_1_Means,Batch_1_SD)
B1_P8 <- rbind(B1_P8, Batch_1_Means,Batch_1_SD)
B1_P9 <- rbind(B1_P9, Batch_1_Means,Batch_1_SD)
B1_P10 <- rbind(B1_P10, Batch_1_Means,Batch_1_SD)
B1_P11 <- rbind(B1_P11, Batch_1_Means,Batch_1_SD)
B1_P12 <- rbind(B1_P12, Batch_1_Means,Batch_1_SD)
B1_P13 <- rbind(B1_P13, Batch_1_Means,Batch_1_SD)
B1_P14 <- rbind(B1_P14, Batch_1_Means,Batch_1_SD)
B1_P15 <- rbind(B1_P15, Batch_1_Means,Batch_1_SD)
#Add Batch 2 Mean/SD
B2_P1 <- rbind(B2_P1, Batch_1_Means,Batch_1_SD)
B2_P2 <- rbind(B2_P2, Batch_1_Means,Batch_1_SD)
B2_P3 <- rbind(B2_P3, Batch_1_Means,Batch_1_SD)
B2_P4 <- rbind(B2_P4, Batch_1_Means,Batch_1_SD)
B2_P5 <- rbind(B2_P5, Batch_1_Means,Batch_1_SD)
B2_P6 <- rbind(B2_P6, Batch_1_Means,Batch_1_SD)
B2_P7 <- rbind(B2_P7, Batch_1_Means,Batch_1_SD)
B2_P8 <- rbind(B2_P8, Batch_1_Means,Batch_1_SD)
B2_P9 <- rbind(B2_P9, Batch_1_Means,Batch_1_SD)
B2_P10 <- rbind(B2_P10, Batch_1_Means,Batch_1_SD)
B2_P11 <- rbind(B2_P11, Batch_1_Means,Batch_1_SD)
B2_P12 <- rbind(B2_P12, Batch_1_Means,Batch_1_SD)
B2_P13 <- rbind(B2_P13, Batch_1_Means,Batch_1_SD)
B2_P14 <- rbind(B2_P14, Batch_1_Means,Batch_1_SD)
B2_P15 <- rbind(B2_P15, Batch_1_Means,Batch_1_SD)
8_Create_Z_Function.R
transform_dataframe <- function(input_df) {
# Remove the first column and convert the remaining columns to numeric
B1_P1_New <- input_df[,-c(1,2)]
B1_P1_New <- B1_P1_New %>% mutate_all(as.numeric)
# Create a new dataframe for the transformed values
B1_P1_Normalized <- B1_P1_New # Create a copy of the original dataframe
# Divide each column by its corresponding value in row 385
for (i in 1:ncol(B1_P1_Normalized)) {
B1_P1_Normalized[, i] <- B1_P1_Normalized[, i] - B1_P1_New[385, i]
}
for (i in 1:ncol(B1_P1_Normalized)) {
B1_P1_Normalized[, i] <- B1_P1_Normalized[, i] / B1_P1_New[386, i]
}
# Add the first column back to the transformed dataframe with the name "WELL.LABEL"
B1_P1_Normalized <- cbind("Plate Name" = deparse(substitute(input_df)), WELL.LABEL = input_df[, 2], B1_P1_Normalized)
return(B1_P1_Normalized)
}
# Example usage:
# Assuming you have a dataframe named B1_P1
# B1_P1_PhenoMeasures <- transform_dataframe(B1_P1)
10_Export_All_Data.R
# List of PhenoMeasures dataframe names
df_names <- c("B1_P1_PhenoMeasures", "B1_P2_PhenoMeasures", "B1_P3_PhenoMeasures", "B1_P4_PhenoMeasures", "B1_P5_PhenoMeasures",
"B1_P6_PhenoMeasures", "B1_P7_PhenoMeasures", "B1_P8_PhenoMeasures", "B1_P9_PhenoMeasures", "B1_P10_PhenoMeasures",
"B1_P11_PhenoMeasures", "B1_P12_PhenoMeasures", "B1_P13_PhenoMeasures", "B1_P14_PhenoMeasures", "B1_P15_PhenoMeasures",
"B2_P1_PhenoMeasures", "B2_P2_PhenoMeasures", "B2_P3_PhenoMeasures", "B2_P4_PhenoMeasures", "B2_P5_PhenoMeasures",
"B2_P6_PhenoMeasures", "B2_P7_PhenoMeasures", "B2_P8_PhenoMeasures", "B2_P9_PhenoMeasures", "B2_P10_PhenoMeasures",
"B2_P11_PhenoMeasures", "B2_P12_PhenoMeasures", "B2_P13_PhenoMeasures", "B2_P14_PhenoMeasures", "B2_P15_PhenoMeasures")
# Combine the dataframes into one large dataframe
All_Z_Scores <- do.call(rbind, lapply(df_names, get))
library(stringr)
filtered_data <- All_Z_Scores %>%
filter(!str_detect(WELL.LABEL, "- 11|- 12|- 23|- 24"))
filtered_data <- filtered_data[-c(9660,9659),]
filtered_data <- filtered_data %>%
filter(!str_detect(WELL.LABEL, "Mean|SD"))
Compounds <- read.csv("DDU_Locations.csv", fileEncoding = 'UTF-8-BOM')
Compounds<- rbind(Compounds,Compounds)
All_Compound_Scores <- cbind(Compounds , filtered_data)
All_Compound_Scores <-All_Compound_Scores %>%
filter(!PlateBarcode == "X")
All_Compound_Scores <- All_Compound_Scores[,-c(1,3,5:8)]
All_Compound_Scores <- All_Compound_Scores %>%
group_by(DDDNumber, Concentration) %>%
summarize_all(mean, na.rm = TRUE)
# View the resulting dataframe
# Save All_Z_Scores to CSV
write.csv(All_Compound_Scores , "All_Compound_Scores.csv", row.names = FALSE)