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.Rapp.history
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load("/Users/mrincredible/Box Sync/Home Folder csh36/Sharing/Data_Plus/R_Data/saved_sample_factors.r")
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "bl_s", "bl_as", "v_s")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_lists = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
miRNA_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
miRNA_list[[j]] = ct_vec#
}#
miRNA_df[[i]] = miRNA_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_lists[[1]])
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "bl_s", "bl_as", "v_s")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_lists = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
miRNA_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
miRNA_list[[j]] = ct_vec#
}#
miRNA_lists[[i]] = miRNA_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_lists[[1]])
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_lists = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
miRNA_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
miRNA_list[[j]] = ct_vec#
}#
miRNA_lists[[i]] = miRNA_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_lists[[1]])
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_lists = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
miRNA_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
miRNA_list[[j]] = ct_vec#
}#
miRNA_lists[[i]] = miRNA_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_lists[[1]], names=sub_grps)
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
sub_grp_names = c("Bacteria", "Viral Symp", "Baseline Symp", "Baseline Asymp")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_lists = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
miRNA_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
miRNA_list[[j]] = ct_vec#
}#
miRNA_lists[[i]] = miRNA_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_lists[[1]], names=sub_grps)
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
sub_grp_names = c("Bacteria", "Viral Symp", "Baseline Symp", "Baseline Asymp")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_lists = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
miRNA_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
miRNA_list[[j]] = ct_vec#
}#
miRNA_lists[[i]] = miRNA_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_lists[[1]], names=sub_grp_names)
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
sub_grp_names = c("Bacteria", "Viral Symp", "Baseline Symp", "Baseline Asymp")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_list = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
grp_list_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
grp_list[[j]] = ct_vec#
}#
miRNA_list[[i]] = grp_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_list[[1]], names=sub_grp_names, main="Logged Counts")
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
sub_grp_names = c("Bacteria", "Viral Symp", "Baseline Symp", "Baseline Asymp")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_list = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
grp_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
grp_list[[j]] = ct_vec#
}#
miRNA_list[[i]] = grp_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_list[[1]], names=sub_grp_names, main="Logged Counts")
# Load the following...#
# processed_data#
# sample_factors#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
sub_grp_names = c("Bacteria", "Viral Symp", "Baseline Symp", "Baseline Asymp")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_list = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
grp_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
grp_list[[j]] = ct_vec#
}#
miRNA_list[[i]] = grp_list#
}#
# select only certain miRNA indices#
# make boxplot#
boxplot(miRNA_list[[1]], miRNA_list[[2]], miRNA_list[[3]], miRNA_list[[4]], names=sub_grp_names, main="Logged Counts")
x = matrix(c(1,2,3,4), nrow=2, ncol=2)
new_x = apply(x, c(1,2), log)
new_x
# select only certain miRNA indices#
# make boxplot#
col_scheme = c("red", "green", "blue", "orange")#
boxplot(miRNA_list[[1]], names=sub_grp_names, main="Logged Counts")#
boxplot(miRNA_list[[2]] names=sub_grp_names, add=TRUE)
# select only certain miRNA indices#
# make boxplot#
col_scheme = c("red", "green", "blue", "orange")#
boxplot(miRNA_list[[1]], names=sub_grp_names, main="Logged Counts")#
boxplot(miRNA_list[[2]], names=sub_grp_names, add=TRUE)
errors()
# select only certain miRNA indices#
# make boxplot#
col_scheme = c("red", "green", "blue", "orange")#
boxplot(miRNA_list[[1]], names=sub_grp_names, main="Logged Counts")#
boxplot(miRNA_list[[2]], names=sub_grp_names, add=TRUE)
# select only certain miRNA indices#
# make boxplot#
col_scheme = c("red", "green", "blue", "orange")#
boxplot(miRNA_list[[1]], main="Logged Counts")#
boxplot(miRNA_list[[2]], add=TRUE)
# select only certain miRNA indices#
# make boxplot#
col_scheme = c("red", "green", "blue", "orange")#
boxplot(miRNA_list[[1]], main="Logged Counts")#
boxplot(miRNA_list[[2]], names=5:8, add=TRUE)
# Load the following...#
# processed_data#
# sample_factors#
#
# function to log transform the data#
log_transform = function(x) {#
return log(x + 1)#
}#
#
# log the data, transform it, make it into data frame#
logged_data = apply(processed_data, c(1,2), log_transform)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
sub_grp_names = c("Bacteria", "Viral Symp", "Baseline Symp", "Baseline Asymp")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_list = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
grp_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
grp_list[[j]] = ct_vec#
}#
miRNA_list[[i]] = grp_list#
}#
# select only certain miRNA indices#
# make boxplot#
col_scheme = c("red", "green", "blue", "orange")#
boxplot(miRNA_list[[1]], main="Logged Counts")#
boxplot(miRNA_list[[2]], names=5:8, add=TRUE)
# select only certain miRNA indices#
# make boxplots#
# divide figure into 6 cells#
par(mfrow=c(3,2))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_num = miRNA_indices[i]#
plot_title = paste("miRNA #", miRNA_num, sep="")#
boxplot(miRNA_list[[i]], main=plot_title, col=col_scheme)#
}
# select only certain miRNA indices#
# make boxplots#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_num = miRNA_indices[i]#
plot_title = paste("miRNA #", miRNA_num, sep="")#
boxplot(miRNA_list[[i]], main=plot_title, col=col_scheme)#
}
################
# Retrieve Data#
################
#
# insert data set#
known_data <-read.delim("Data_in/known_miRNAs_expressed_all_samples.txt", header=TRUE)#
#
# extract raw data into matrix (just #s), exclude "norm" data#
data_raw <- known_data[,5:56]#
data_raw <- data.matrix(data_raw)#
#
# save miRNA names#
miRNA_names <- known_data[,1]#
# save precursor names#
prec_names <- known_data[,3]#
#
# function to count zeros, return proportions#
count_zeros <- function(v) {#
len <- length(v)#
zero_bool <- v==0#
zero_count <- length(which(zero_bool == "TRUE"))#
prop <- zero_count / len#
return(prop)#
}#
################
# Eliminate >50% zero proportions#
################
#
# find zero proportions for each miRNA#
zero_prop_miRNA <- apply(data_raw, 1, count_zeros)#
#
# delete miRNAs that have >50% zeros#
over_50 <- zero_prop_miRNA > 0.5#
data_proc <- data_raw[!over_50,]#
#
# get miRNA names for data_proc#
miRNA_names_proc <- miRNA_names[!over_50]#
# get precursor names for data_proc#
prec_names_proc <- prec_names[!over_50]#
################
# Normalize Data#
################
#
# UQ normalization function#
uq_normalize <- function(v) {#
nonzero_data <- subset(v, v > 0)#
quartiles <- quantile(nonzero_data)#
uq <- quartiles[4]#
return(uq)#
}#
#
# vector of Us needed to normalize data#
Us <- apply(data_raw, 2, uq_normalize)#
# finding mean of the of the uq samples#
U <- mean(Us)#
#
# do UQ normalization#
uqnorm_data <- matrix(, nrow=nrow(data_proc), ncol=ncol(data_proc))#
# doing a for loop to normalize on each sample#
for (i in 1:ncol(data_proc)) {#
col <- data_proc[,i]#
uqnorm_data[,i] <- col/Us[i]*U#
}#
#
# save upper quartilze normalized data#
save(uqnorm_data, file="Data_out/saved_uqnorm_data.r")
load("/Users/mrincredible/Box Sync/Home Folder csh36/Sharing/Data_Plus/R_Data/data_frames.r")
load("/Users/mrincredible/Box Sync/Home Folder csh36/Sharing/Data_Plus/R_Data/saved_uqnorm_data.r")
# Load the following...#
# uqnorm_data#
# sample_factors#
# feat_df (from data_frames.r)#
#
# function to log transform the data#
log_transform = function(x) {#
return log(x + 1)#
}#
#
# log the data, transform it, make it into data frame#
logged_data = apply(uqnorm_data, c(1,2), log_transform)#
logged_data = t(logged_data)#
logged_df = as.data.frame(logged_data)#
#
# important miRNA indices:#
# - 200#
# - 86#
# - 195#
# - 211#
# - 51#
#
# extract miRNAs#
miRNA_indices = c(200, 86, 195, 211, 51)#
# extract miRNA names from feat_df#
miRNA_names = feat_df$miRNA[miRNA_indices]#
select_logged_df = logged_df[ , miRNA_indices]#
#
# important subgroups:#
# - bacteria#
# - baseline symptomatic#
# - baseline asymptomatic#
# - viral symptomatic#
#
# For reference:#
# Viral Symptomatic - v_s#
# Viral Asymptomatic - v_as#
# Baseline Symptomatic - bl_s#
# Basline Asymptomatic - bl_as#
# Bacteria - bc#
#
# subgroups#
sub_grps = c("bc", "v_s", "bl_s", "bl_as")#
sub_grp_names = c("Bacteria", "Viral Symp", "Baseline Symp", "Baseline Asymp")#
num_grps = length(sub_grps)#
#
# aggregate data for miRNA for use in box plots#
# define miRNA_df as list of lists; for each miRNA, then for each group, vector of counts#
miRNA_list = list()#
num_miRNAs = length(miRNA_indices)#
for (i in 1:num_miRNAs) {#
miRNA_index = miRNA_indices[i]#
index_data = select_logged_df[ , i]#
grp_list = list()#
for (j in 1:num_grps) {#
ct_vec = index_data[sample_factors == sub_grps[j]]#
grp_list[[j]] = ct_vec#
}#
miRNA_list[[i]] = grp_list#
}#
# select only certain miRNA indices#
# make boxplots#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme)#
}
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps")#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=10, height=10)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=10, height=10)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
warnings()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=10, height=10)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11, height=8.5)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=13, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=18, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=30, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11.5, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names, las=2)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11.5, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
sub_grp_names = c("BC", "V Symp", "BL Symp", "BL Asymp")
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11.5, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=12, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=14, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11.5, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3), oma=c(1,1,1,1))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11.5, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3), oma=c(1,0,0,0))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11.5, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3), oma=c(0,1,0,0))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
setEPS("Data_out/miRNA_distributions.eps")#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
setEPS()#
postscript("Data_out/miRNA_distributions.eps")#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11.5, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
boxplot(miRNA_list[[i]], main=miRNA_name, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
# select only certain miRNA indices#
# make boxplots#
postscript("Data_out/miRNA_distributions.eps", width=11.5, height=8)#
# divide figure into 6 cells#
par(mfrow=c(2,3))#
col_scheme = c("red", "green", "blue", "orange")#
for (i in 1:num_miRNAs) {#
miRNA_name = miRNA_names[i]#
miRNA_num = miRNA_indices[i]#
plot_title = paste(miRNA_name, " (index #", miRNA_num, ")", sep="")#
boxplot(miRNA_list[[i]], main=plot_title, col=col_scheme, names=sub_grp_names)#
}#
dev.off()
load("/Users/mrincredible/Box Sync/Home Folder csh36/Sharing/Data_Plus/R_Data/saved_bin_comp_dfs.r")
load("/Users/mrincredible/Box Sync/Home Folder csh36/Sharing/Data_Plus/R_Data/saved_bin_comp_names.r")
# Load the following...#
# bin_comp_dfs#
# bin_comp_names#
#
# Loading do_log_reg func#
do_log_reg = dget("R_Code/Classifiers/do_log_reg.r")#
#
# create main directory#
dir.create("Data_out/logistic_regression_out")#
main_dir = "Data_out/logistic_regression_out/"#
#
# Obtain data for roc curves#
roc_data = list()#
#
# Do logistic regression for each binary comparison#
for (i in 1:7) {#
# get df_sub#
df_sub = bin_comp_dfs[[i]]#
# create directory#
dir.create(paste(main_dir, bin_comp_names[i], sep=""))#
# perform logistic regression#
sub_dir = paste(bin_comp_names[i], "/", sep="")#
roc_data[[i]] = do_log_reg(df_sub, 1, paste(main_dir, sub_dir, sep=""))#
}
load("/Users/mrincredible/Box Sync/Home Folder csh36/Sharing/Data_Plus/R_Data/data_frames.r")
# Load the following...#
# bin_comp_dfs#
# bin_comp_names#
# feat_df (from data_frames.r)#
#
# Loading do_log_reg func#
do_log_reg = dget("R_Code/Classifiers/do_log_reg.r")#
#
# create main directory#
dir.create("Data_out/logistic_regression_out")#