library(E2Predictor)
splitCellLines = F
splitPerturbations = T
datafolder <- "/Users/napedro/CloudStation/protein_expression_data/perturbations/JY"
rest.file <- list.files(datafolder, "xlsx$")
setwd(datafolder)
df <- read_the_file(rest.file)
names(df) <- make.names(names(df))
cell.lines <- names(df)[11:length(names(df))]
dict.cell.lines <- make.names(c("HEK293",
"LCLC-103H",
"NIH-OVCAR-3",
"COLO-699-N",
"JAR",
"MEL-526",
"JY",
"MEL-HO",
"K562_A0201",
"SK-MEL-37"))
# "HEK293", "LCLC.103H", "NIH.OVCAR.3", "COLO.699.N", "JAR", "MEL.526", "JY", "MEL.HO", "K562A2", "SK.MEL.37"
dict.perturbations <- c("Control", "Bortezomib", "IFNg", "Rapamycin", "DMOG")
split_cell_line <- function(cell_line){
common_columns <- names(df)[1:9]
df_split_common <- df %>%
select(description:entry)
df_split <- df %>%
select_(cell_line)
df_split <- cbind(df_split_common, df_split)
write_tsv(df_split, paste("protein_TOP3", cell_line, "tsv", sep = "."))
}
if(splitCellLines){
nix <- sapply(cell.lines, split_cell_line)
}
split_perturbation <- function(perturbation){
common_columns <- names(df)[1:9]
df_split_common <- df %>%
select(description:entry)
df_split <- df %>%
select_(matches(perturbation))
df_split <- cbind(df_split_common, df_split)
write_tsv(df_split, paste("protein_TOP3", cell_line, "tsv", sep = "."))
}
if(splitPerturbations){
nix <- sapply(perturbations, split_perturbation)
}
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