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BBBC022_pipeline_200k.R
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BBBC022_pipeline_200k.R
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####################
### BBBC022 Data ###
####################
library(magrittr)
library(dplyr)
library(tidyverse)
library(stringr)
################################
# Loading data + preprocessing #
################################
#filename <- "../../../../input/BBBC022/Pf_Gustafsdottir.rds"
#Pf <- readRDS(filename)
#Pf <- Pf$data %>%
# dplyr::rename(Metadata_Plate = Plate,
# Metadata_Well = Well,
# Metadata_broad_sample = Image_Metadata_BROAD_ID,
# Metadata_cpd_name = Image_Metadata_SOURCE_COMPOUND_NAME)
filenames <- list.files(path = "../../input/BBBC022_2013/selected_single_cell_zoom/200000/feat_selected/") #BBBC022_dmso/Profile/feat_selected/SVD_entropy")
all <- 1:length(filenames)
for(n in all){
numfeat <- str_split(filenames[n], ".rds") %>% unlist
numfeat <- str_split(numfeat, "_") %>% unlist
numfeat <- numfeat[length(numfeat)-1]
Pf <- readRDS(paste("../../input/BBBC022_2013/selected_single_cell_zoom/200000/feat_selected/", #Profile/feat_selected/SVD_entropy/",
filenames[n],
sep = ""))
Pf <- Pf$data %>%
rename(Metadata_Plate = Plate,
Metadata_Well = Well,
Metadata_broad_sample = Image_Metadata_BROAD_ID,
Metadata_cpd_name = Image_Metadata_SOURCE_COMPOUND_NAME)
variables <-
colnames(Pf) %>% str_subset("^Cells_|^Cytoplasm_|^Nuclei_")
metadata <-
colnames(Pf) %>% str_subset("^Metadata_")
######################
# Features Selection #
######################
# remove features that are too correlated (threshold of .9)
#Pf %<>%
# cytominer::select(
# sample = Pf,
# variables = variables,
# operation = "correlation_threshold"
# )
#######################################################
### use feature selected from single cells
#features.filenames <- "../../input/BBBC022_2013/single_cells_findCorrelation/feat_names/selected_features_200000cells_findCorr.Rda"
#load(file.path(features.filenames))
#variables <- str_replace(variables, "Hoechst", "DNA")
#variables <- str_replace(variables, "Ph_golgi", "AGP")
#variables <- str_replace(variables, "Syto", "RNA")
#cl <- colnames(Pf)
#cl <- str_replace(cl, "Hoechst", "DNA")
#cl <- str_replace(cl, "Ph_golgi", "AGP")
#cl <- str_replace(cl, "Syto", "RNA")
#colnames(Pf) <- cl
# select the features
#Pf %<>% select(one_of(metadata, variables.fs.sc.fc))
#####################################################
#variables <-
# names(Pf) %>% str_subset("^Cells_|^Cytoplasm_|^Nuclei_")
#########################
# End of feat selection #
#########################
#################
# Hit Selection #
#################
# uplodaing functions
source("hit_selection_correlation_function.R")
source("hit_selection_jaccard_function.R")
start.time <- Sys.time()
# reproductibility
set.seed(42)
N <- 5 #10
seeds <- sample(1:10000, N, replace=F)
dir.save <- "BBBC022_2013/selected_single_cell_zoom/200000" #/single_cells_findCorrelation"
hit.ratio.p <- c()
i <- 0
for(s in seeds){
print(i)
i <- i + 1
hit <- hit_selection_correlation(Pf,
n.replicate = 4,
filename = filenames[n],
cor.method = "pearson",
seed = s,
N = 3000,
dir.save = dir.save,
dir.save.plus = "/hit_selected/Pearson/")
hit.ratio.p <- cbind(hit.ratio.p, hit)
}
print("mean hit ratio: ")
print(mean(hit.ratio.p))
print("standard deviation hit ratio: ")
print(sd(hit.ratio.p)/N)
hit.ratio.j <- c()
num.feat <- round(0.05*length(variables))
i <- 0
for(s in seeds){
print(i)
i <- i + 1
hit <- hit_selection_jaccard(Pf,
n.replicate = 4,
filename = filenames[n],
n.feat = num.feat,
seed = s,
N = 3000,
dir.save = dir.save,
dir.save.plus = "/hit_selected/Jaccard/")
hit.ratio.j <- cbind(hit.ratio.j, hit)
}
print("mean hit ratio: ")
print(mean(hit.ratio.j))
print("standard deviation hit ratio: ")
print(sd(hit.ratio.j)/N)
end.time <- Sys.time()
time.taken <- end.time - start.time
time.taken
}
####################
# Enrichment ratio #
####################
source("enrichment_ratio_function.R")
data.filenames.p <- list.files(path = "../../input/BBBC022_2013/selected_single_cell_zoom/200000/hit_selected/Pearson/")
data.filenames.j <- list.files(path = "../../input/BBBC022_2013/selected_single_cell_zoom/200000/hit_selected/Jaccard")
###### TO REMOVE
tmp <- paste("BBC022_fs_SVD_", as.character(numfeat), sep = "")
data.filenames.p <- data.filenames.p[grep(tmp,data.filenames.p)]
data.filenames.j <- data.filenames.j[grep(tmp,data.filenames.j)]
#data.filenames.p <- data.filenames.p[2:length(data.filenames.p)]
#data.filenames.j <- data.filenames.j[2:length(data.filenames.j)]
######
# dataframe of result
enrichment.ratio <- data.frame(mean = numeric(0), quant = numeric(0), percent = numeric(0), filename = character(0), method = character(0))
for(i in 1:length(data.filenames.p)){
print(i)
seed <- str_split(data.filenames.p[i], "_") %>% unlist
seed <- str_split(seed, ".rds") %>% unlist
seed <- seed[length(seed)-1]
pf.p <- readRDS(file.path("..", "..", "input", "BBBC022_2013", "selected_single_cell_zoom", "200000","hit_selected", "Pearson", data.filenames.p[i]))
enrichment.ratio <- bind_rows(enrichment.ratio,
enrichment_ratio(pf.p, top.x = 0.02, seed = seed,
nCPU = detectCores(), N = 1000, filename = data.filenames.p[i], method = "Pearson"))
pf.j <- readRDS(file.path("..", "..", "input", "BBBC022_2013", "selected_single_cell_zoom", "200000","hit_selected", "Jaccard", data.filenames.j[i]))
enrichment.ratio <- bind_rows(enrichment.ratio,
enrichment_ratio(pf.j, top.x = 0.02, seed = seed,
nCPU = detectCores(), N = 1000, filename = data.filenames.j[i], method = "Jaccard"))
}
###### result
enr.ratio <- enrichment.ratio %>% mutate(ratio = percent/mean)
enr.ratio %<>%
group_by(method) %>%
summarise(ratio.mean = mean(ratio),
ratio.sd = sd(ratio)/sqrt(n()))
enr.ratio
filename.enr.ratio <- paste("../../input/BBBC022_2013/selected_single_cell_zoom/200000/enrichment_ratio/enr_ratio_nfeat_",
as.character(numfeat),
#data.filenames.p[i],
".Rda",
sep = "")
enr.ratio[enr.ratio=="Jaccard"] <- "Jaccard_SVD_single_cell_200000"
enr.ratio[enr.ratio=="Pearson"] <- "Pearson_SVD_single_cell_200000"
enr.ratio %<>% mutate(n.feat = numfeat)#dim(Pf)[2])
save(enr.ratio, file=filename.enr.ratio)
#Whole dataset:
# method ratio.mean ratio.sd
#1 Jaccard 1.824503 0.015004642
#2 Pearson 1.874721 0.004085244
#With feature selection: