# This function prints the average of correlation parameters between real and simulated
# bin means, and real and simulated non-zero cell proportions (for each cell type).
# Three sets of simulation were perfomed for different sets of Gaussian noise parameters,
# and all simulations for a specific set were started from a specific seed value (first
# simulation set seed (version pattern = "simATACV1_mean_sd"): 60760, second simulation
# set seed (version pattern = "simATACV2_mean_sd"): 1000, third simulation set seed (version
# pattern = "simATACV3_mean_sd"): 10).
# This function reports the average of correlation of each parameter over all versions for
# simulations without Gaussian noise (mean:0, sd:0).
# Input:
# name: The name of the dataset ("Buenrostro2018", "Cusanovich2018", "PBMCs").
# version: The version of the input simulation.
# Output: -
analyze_correlation <- function(name, version){
data <- read.table(paste("/Users/zeinab/mnt/a5/simATAC_revision/", name, "/", name, "_correlation_revision.txt", sep = ""), header = FALSE, dec = ".")
data <- data[grep(version, data$V2),]
# get all existing cell type or cell lable in the dataset and report the average of
# correlation for each cell type separately.
type <- unique(data$V1)
for (t in type){
print(paste(t,
round(mean(data[which(data$V1 == t & data$V3 == "BinMeanPearCor<0.8"),]$V4), 2),
round(mean(data[which(data$V1 == t & data$V3 == "NZeroPropPearCor"),]$V4), 2)
)
)
}
}
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