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sim_roc_classes.R
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sim_roc_classes.R
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library(genefilter)
library(MASS)
library(pROC)
library(docopt)
###############################
# steps to perform in the program
test = FALSE
# harcode the parameters during a test
if(test){
n_samples = 30
seed = 50 #42
ddcor_test = TRUE
EBcoexpress_test = TRUE
discordant_test = TRUE
compare_ddcor_eb_discordant = TRUE
corr_classes = "medium" #"all" "medium" "strong"
compare_ddcor_classes = FALSE
compare_eb_classes = FALSE
compare_discordant_classes = FALSE
}
#get in parameters from the command line when in "production" use
if(!test){
args = commandArgs(trailingOnly = TRUE)
print(args)
n_samples = as.numeric(args[1])
if(args[2] == "random"){
seed = runif(1, 0, 100000)
print("seed")
print(seed)
} else {
seed = as.numeric(args[2])
}
ddcor_test = as.logical(args[3])
EBcoexpress_test = as.logical(args[4])
discordant_test = as.logical(args[5])
compare_ddcor_eb_discordant = as.logical(args[6])
corr_classes = as.character(args[7]) #"all" "medium" "strong"
compare_ddcor_classes = as.logical(args[8])
compare_eb_classes = as.logical(args[9])
compare_discordant_classes = as.logical(args[10])
}
#reproducibility
set.seed(seed)
#color palette
#http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
cbPalette = c("#000000", "darkgrey", "#E69F00", "#56B4E9", "#009E73", "#F0E442",
"#0072B2", "#CC79A7")
#################################
# functions
#Sets the superdiagonal and subdiagonal of a matrix to the number x.
subsup_diag <- function(matrix, x){
diag(matrix[,-1]) = x
diag(matrix[-1,]) = x
return(matrix)
}
#changes the superdiagonal and subdiagonal of a particular submatrix of a larger matrix
add_sigma_to_matrix <- function(total_matrix, row_start, nrows, x){
extract_mat = total_matrix[row_start:(row_start + nrows),
row_start:(row_start + nrows)]
mat = subsup_diag(extract_mat, x)
total_matrix[row_start:(row_start + nrows),
row_start:(row_start + nrows)] = mat
return(total_matrix)
}
#updates the corrrelation matrix and adds the gene pairs that were modified to a class for subsequent extraction
update_corr_structure <- function(sigma, n_genes, variance, corr_factor, dcor = NULL,
dcor_specific = NULL){
total_dc_genes = sigma[["total_dc_genes"]]
sigma[["sigma_tot"]] = add_sigma_to_matrix(sigma[["sigma_tot"]],
row_start = total_dc_genes + 1,
nrows = n_genes, x = variance * corr_factor)
if(!is.null(dcor)){
if(dcor == "goc"){
sigma[["goc_gene_pairs"]] = c(sigma[["goc_gene_pairs"]],
paste(letters_unique[(total_dc_genes + 1):(total_dc_genes + n_genes - 1)],
letters_unique[(total_dc_genes + 2):(total_dc_genes + n_genes)], sep = " "))
}
if(dcor == "loc"){
sigma[["loc_gene_pairs"]] = c(sigma[["loc_gene_pairs"]],
paste(letters_unique[(total_dc_genes + 1):(total_dc_genes + n_genes - 1)],
letters_unique[(total_dc_genes + 2):(total_dc_genes + n_genes)], sep = " "))
}
}
if(!is.null(dcor_specific)){
if(dcor_specific == "+/0"){
sigma[["pos_none_gene_pairs"]] = c(sigma[["pos_none_gene_pairs"]],
paste(letters_unique[(total_dc_genes + 1):(total_dc_genes + n_genes - 1)],
letters_unique[(total_dc_genes + 2):(total_dc_genes + n_genes)], sep = " "))
}
if(dcor_specific == "+/-"){
sigma[["pos_neg_gene_pairs"]] = c(sigma[["pos_neg_gene_pairs"]],
paste(letters_unique[(total_dc_genes + 1):(total_dc_genes + n_genes - 1)],
letters_unique[(total_dc_genes + 2):(total_dc_genes + n_genes)], sep = " "))
}
if(dcor_specific == "0/+"){
sigma[["none_pos_gene_pairs"]] = c(sigma[["none_pos_gene_pairs"]],
paste(letters_unique[(total_dc_genes + 1):(total_dc_genes + n_genes - 1)],
letters_unique[(total_dc_genes + 2):(total_dc_genes + n_genes)], sep = " "))
}
if(dcor_specific == "0/-"){
sigma[["none_neg_gene_pairs"]] = c(sigma[["none_neg_gene_pairs"]],
paste(letters_unique[(total_dc_genes + 1):(total_dc_genes + n_genes - 1)],
letters_unique[(total_dc_genes + 2):(total_dc_genes + n_genes)], sep = " "))
}
if(dcor_specific == "-/+"){
sigma[["neg_pos_gene_pairs"]] = c(sigma[["neg_pos_gene_pairs"]],
paste(letters_unique[(total_dc_genes + 1):(total_dc_genes + n_genes - 1)],
letters_unique[(total_dc_genes + 2):(total_dc_genes + n_genes)], sep = " "))
}
if(dcor_specific == "-/0"){
sigma[["neg_none_gene_pairs"]] = c(sigma[["neg_none_gene_pairs"]],
paste(letters_unique[(total_dc_genes + 1):(total_dc_genes + n_genes - 1)],
letters_unique[(total_dc_genes + 2):(total_dc_genes + n_genes)], sep = " "))
}
}
sigma[["total_dc_genes"]] = total_dc_genes + n_genes
return(sigma)
}
#for gene name generation, i.e. "aa", "ab", "ac", etc
combine <- function (vecs, number){
combn(vecs, number, paste, collapse = "")
}
letters_unique = combine(c(letters[1:26], letters[1:26]), 2)
letters_unique = sort(unique(letters_unique))
#################################
# parameters for simulation data
n_non_expr_genes = 200
n_housekeeping_genes = 100
n_activated_genes = 300
n_tot_genes = n_non_expr_genes + n_housekeeping_genes + n_activated_genes
low_mean = 2
high_mean = 50
low_var = 50
high_var = 100
positive_correlation = 0.5
negative_correlation = -0.5 #if this is set too low, then the matrix will be non-positive definite
######################################
#create overall correlation matrices and classes to store them
sigma_tot_a <- structure(list(
sigma_tot = matrix(0, nrow = n_tot_genes, ncol = n_tot_genes),
total_dc_genes = 0
), class = "sigma")
sigma_tot_b <- structure(list(
sigma_tot = matrix(0, nrow = n_tot_genes, ncol = n_tot_genes),
total_dc_genes = 0,
loc_gene_pairs = vector(),
goc_gene_pairs = vector(),
pos_none_gene_pairs = vector(),
pos_neg_gene_pairs = vector(),
none_pos_gene_pairs = vector(),
none_neg_gene_pairs = vector(),
neg_pos_gene_pairs = vector(),
neg_none_gene_pairs = vector()
), class = "sigma")
if(corr_classes == "all"){
#A+, B+ = no differential correlation
n_apbp = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_apbp,
variance = high_var, corr_factor = positive_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_apbp,
variance = high_var, corr_factor = positive_correlation, dcor = NULL)
#A+, B= = loss of correlation
n_apbe = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_apbe,
variance = high_var, corr_factor = positive_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_apbe,
variance = high_var, corr_factor = 0, dcor = "loc", dcor_specific = "+/0")
#A+, B- = loss of correlation
n_apbm = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_apbm,
variance = high_var, corr_factor = positive_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_apbm,
variance = high_var, corr_factor = negative_correlation, dcor = "loc",
dcor_specific = "+/-")
#A=, B+ = gain of correlation
n_aebp = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_aebp,
variance = high_var, corr_factor = 0, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_aebp,
variance = high_var, corr_factor = positive_correlation, dcor = "goc",
dcor_specific = "0/+")
#A=, B- = loss of correlation
n_aebm = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_aebm,
variance = high_var, corr_factor = 0, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_aebm,
variance = high_var, corr_factor = negative_correlation, dcor = "loc",
dcor_specific = "0/-")
#A-, B+ = gain of correlation
n_ambp = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_ambp,
variance = high_var, corr_factor = negative_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_ambp,
variance = high_var, corr_factor = positive_correlation, dcor = "goc",
dcor_specific = "-/+")
#A-, B= = gain of correlation
n_ambe = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_aebm,
variance = high_var, corr_factor = negative_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_aebm,
variance = high_var, corr_factor = 0, dcor = "goc",
dcor_specific = "-/0")
#A-, B- = no differential correlation
n_ambm = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_aebm,
variance = high_var, corr_factor = negative_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_aebm,
variance = high_var, corr_factor = negative_correlation, dcor = NULL)
#can potential skip this one...
#A=, B= = no differential correlation
n_aebe = n_activated_genes - sigma_tot_a[["total_dc_genes"]]
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_aebm,
variance = high_var, corr_factor = 0, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_aebm,
variance = high_var, corr_factor = 0, dcor = NULL)
}
if(corr_classes == "medium"){
#A+, B= = loss of correlation
n_apbe = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_apbe,
variance = high_var, corr_factor = positive_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_apbe,
variance = high_var, corr_factor = 0, dcor = "loc", dcor_specific = "+/0")
#A=, B+ = gain of correlation
n_aebp = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_aebp,
variance = high_var, corr_factor = 0, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_aebp,
variance = high_var, corr_factor = positive_correlation, dcor = "goc",
dcor_specific = "0/+")
#A=, B- = loss of correlation
n_aebm = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_aebm,
variance = high_var, corr_factor = 0, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_aebm,
variance = high_var, corr_factor = negative_correlation, dcor = "loc",
dcor_specific = "0/-")
#A-, B= = gain of correlation
n_ambe = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_aebm,
variance = high_var, corr_factor = negative_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_aebm,
variance = high_var, corr_factor = 0, dcor = "goc",
dcor_specific = "-/0")
}
if(corr_classes == "strong"){
#A+, B- = loss of correlation
n_apbm = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_apbm,
variance = high_var, corr_factor = positive_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_apbm,
variance = high_var, corr_factor = negative_correlation, dcor = "loc",
dcor_specific = "+/-")
#A-, B+ = gain of correlation
n_ambp = 20
sigma_tot_a = update_corr_structure(sigma = sigma_tot_a, n_genes = n_ambp,
variance = high_var, corr_factor = negative_correlation, dcor = NULL)
sigma_tot_b = update_corr_structure(sigma = sigma_tot_b, n_genes = n_ambp,
variance = high_var, corr_factor = positive_correlation, dcor = "goc",
dcor_specific = "-/+")
}
sigma_tot_a_mat = sigma_tot_a[["sigma_tot"]]
sigma_tot_b_mat = sigma_tot_b[["sigma_tot"]]
########################################
#set the means and variances of the genes
#set the variance
var_diag = c(rep(high_var, n_activated_genes),
rep(low_var, n_housekeeping_genes),
rep(high_var, n_non_expr_genes))
#set the mean for the high expression genes
mu = rnbinom(n = (n_activated_genes + n_housekeeping_genes), mu = high_mean, size = 0.5)
#set the mean for the low expression genes
mu = c(mu, rnbinom(n = n_non_expr_genes, mu = low_mean, size = 0.5))
#if mu is less than one, set it to one
mu[mu < 1] = 1
diag(sigma_tot_a_mat) = var_diag
diag(sigma_tot_b_mat) = var_diag
#generate the simulation matrix
sim_data_a = as.matrix(mvrnorm(n_samples, mu = mu, Sigma = sigma_tot_a_mat))
sim_data_b = as.matrix(mvrnorm(n_samples, mu = mu, Sigma = sigma_tot_b_mat))
true_cases = c(sigma_tot_b$loc_gene_pairs, sigma_tot_b$goc_gene_pairs)
###########################################
# quality control of the resulting simulation data
test_for_unintended_correlations = FALSE
if(test_for_unintended_correlations){
cor_sims_a_pearson = cor(sim_data_a)
cor_sims_a_rem_pearson = subsup_diag(cor_sims_a_pearson, 0)
diag(cor_sims_a_rem_pearson) = 0
corrplot(cor_sims_a_rem_pearson)
cor_sims_a_spearman = cor(sim_data_a, method = "spearman")
cor_sims_a_rem_spearman = subsup_diag(cor_sims_a_spearman, 0)
diag(cor_sims_a_rem_spearman) = 0
corrplot(cor_sims_a_rem_spearman)
}
####################################
#run the ddcor differential correlation pipeline on the simulated data
if(ddcor_test){
library(DGCA)
print("starting the ddcor pipeline")
sim_data_a = t(sim_data_a)
sim_data_b = t(sim_data_b)
sim_data_merge = cbind(sim_data_a, sim_data_b)
rownames(sim_data_merge) = letters_unique[1:nrow(sim_data_merge)]
conditions = c(rep("cond_a", ncol(sim_data_a)),
rep("cond_b", ncol(sim_data_b)))
design_mat = model.matrix(~ conditions + 0)
labels = c("cond_a", "cond_b")
colnames(design_mat) = labels
npairs = (nrow(sim_data_merge)^2)/2 - nrow(sim_data_merge)
ddcor_start = Sys.time()
ddcor_res = ddcorAll(inputMat = sim_data_merge, design = design_mat, compare = labels, corr_cutoff = 0.95, adjust = "perm", corrType = "pearson", nPerm = 10, heatmapPlot = FALSE, nPairs = "all")
print(paste0("time taken to run the ddcor pipeline with ", corr_classes, n_samples, " samples..."))
ddcor_time = difftime(ddcor_start, Sys.time(), units = "secs")
print(difftime(ddcor_start, Sys.time()))
ddcor_res$combos = paste(ddcor_res$Gene1, ddcor_res$Gene2, sep = " ")
ddcor_res$true = ddcor_res$combos %in% true_cases
plot_groupwise_ROC_ddcor <- function(res_pval = ddcor_res$pValDiff,
pair_names = ddcor_res$combos, group_true, all_true = true_cases){
group_true_vec = pair_names %in% group_true
to_remove = pair_names %in% setdiff(all_true, group_true)
tmp_df = data.frame(pvals = res_pval, true = group_true_vec)
tmp_df = tmp_df[!to_remove, ]
roc_list = roc(tmp_df$true, tmp_df$pvals, smooth = FALSE)
return(roc_list)
}
print("starting ddcor ROC curve calculation")
full_roc = plot_groupwise_ROC_ddcor(group_true = true_cases)
print("full AUC for ddcor with n_samples...")
print(n_samples)
print(full_roc)
ddcor_line = c("ddcor", corr_classes, n_samples, seed, ddcor_time, as.numeric(full_roc$auc))
write.table(ddcor_line, "auc_results_classes.txt", sep = "\t", quote = FALSE, append = TRUE, col.names = FALSE,
row.names = FALSE)
if(compare_ddcor_classes){
roc_loc_only = plot_groupwise_ROC_ddcor(group_true = sigma_tot_b$loc_gene_pairs)
roc_goc_only = plot_groupwise_ROC_ddcor(group_true = sigma_tot_b$goc_gene_pairs)
roc_p0_only = plot_groupwise_ROC_ddcor(group_true = sigma_tot_b$pos_none_gene_pairs)
roc_pn_only = plot_groupwise_ROC_ddcor(group_true = sigma_tot_b$pos_neg_gene_pairs)
roc_0p_only = plot_groupwise_ROC_ddcor(group_true = sigma_tot_b$none_pos_gene_pairs)
roc_0n_only = plot_groupwise_ROC_ddcor(group_true = sigma_tot_b$none_neg_gene_pairs)
roc_np_only = plot_groupwise_ROC_ddcor(group_true = sigma_tot_b$neg_pos_gene_pairs)
roc_n0_only = plot_groupwise_ROC_ddcor(group_true = sigma_tot_b$neg_none_gene_pairs)
tiff(file = paste0("ddcor_classes_", corr_classes, n_samples, "_samples", seed, ".tiff"),
width = 4, height = 4, units = 'in', res = 300)
plot(roc_goc_only, col = cbPalette[1])
plot(roc_loc_only, add = TRUE, col = cbPalette[2])
plot(roc_p0_only, add = TRUE, col = cbPalette[3])
plot(roc_pn_only, add = TRUE, col = cbPalette[4])
plot(roc_0p_only, add = TRUE, col = cbPalette[5])
plot(roc_0n_only, add = TRUE, col = cbPalette[6])
plot(roc_np_only, add = TRUE, col = cbPalette[7])
plot(roc_n0_only, add = TRUE, col = cbPalette[8])
dev.off()
}
}
###################################
#run the EBcoexpress differential correlation pipeline on the simulated data
if(EBcoexpress_test){
print("starting the EBcoexpress pipeline")
library(EBcoexpress)
conditions_eb = c(rep(1, n_samples), rep(2, n_samples))
eb_start = Sys.time()
tinyPat = ebPatterns(c("1,1","1,2"))
D = makeMyD(sim_data_merge, conditions_eb)
initHP = initializeHP(D, conditions_eb)
oout = ebCoexpressOneStep(D, conditions_eb, tinyPat, initHP)
print(paste0("time taken to run the EBcoexpress pipeline with ", corr_classes, n_samples, " samples..."))
eb_time = difftime(eb_start, Sys.time(), units = "secs")
print(difftime(eb_start, Sys.time()))
eb_res = data.frame(probs = as.numeric(oout$POSTPROBS[ , "DC1"]), genes = rownames(oout$POSTPROBS))
eb_res$genes_split = gsub("~", " ", eb_res$genes)
eb_res$true = eb_res$genes_split %in% true_cases
eb_res_true = eb_res[eb_res$true == TRUE, ]
eb_res_order = eb_res[order(eb_res$probs, decreasing = TRUE), ]
plot_groupwise_ROC_eb <- function(res_pval = eb_res_order$probs,
pair_names = eb_res_order$genes_split, group_true,
all_true = true_cases){
group_true_vec = pair_names %in% group_true
to_remove = pair_names %in% setdiff(all_true, group_true)
tmp_df = data.frame(pvals = res_pval, true = group_true_vec)
tmp_df = tmp_df[!to_remove, ]
roc_list = roc(tmp_df$true, tmp_df$pvals, smooth = FALSE)
return(roc_list)
}
print("starting EB coexpress ROC curve calculation")
full_roc_eb = plot_groupwise_ROC_eb(group_true = true_cases)
print("full AUC for EBcoexpress with n_samples...")
print(n_samples)
print(full_roc_eb)
ddcor_line = c("ebcoexpress", corr_classes, n_samples, seed, eb_time, as.numeric(full_roc_eb$auc))
write.table(ddcor_line, "auc_results_classes.txt", sep = "\t", quote = FALSE, append = TRUE, col.names = FALSE,
row.names = FALSE)
if(compare_eb_classes){
print("starting EB coexpress ROC curve calculation for individual classes")
loc_roc_eb = plot_groupwise_ROC_eb(group_true = sigma_tot_b$loc_gene_pairs)
goc_roc_eb = plot_groupwise_ROC_eb(group_true = sigma_tot_b$goc_gene_pairs)
pos_none_roc_eb = plot_groupwise_ROC_eb(group_true = sigma_tot_b$pos_none_gene_pairs)
pos_neg_roc_eb = plot_groupwise_ROC_eb(group_true = sigma_tot_b$pos_neg_gene_pairs)
none_pos_roc_eb = plot_groupwise_ROC_eb(group_true = sigma_tot_b$none_pos_gene_pairs)
none_neg_roc_eb = plot_groupwise_ROC_eb(group_true = sigma_tot_b$none_neg_gene_pairs)
neg_pos_roc_eb = plot_groupwise_ROC_eb(group_true = sigma_tot_b$neg_pos_gene_pairs)
neg_none_roc_eb = plot_groupwise_ROC_eb(group_true = sigma_tot_b$neg_none_gene_pairs)
tiff(file = paste0("ebcoexpress_classes_", corr_classes, n_samples, "_samples_seed", seed, ".tiff"),
width = 4, height = 4, units = 'in', res = 300)
plot(loc_roc_eb, col = cbPalette[1]) #lty = 2
plot(goc_roc_eb, add = TRUE, col = cbPalette[2])
plot(pos_none_roc_eb, add = TRUE, col = cbPalette[3])
plot(pos_neg_roc_eb, add = TRUE, col = cbPalette[4])
plot(none_pos_roc_eb, add = TRUE, col = cbPalette[5])
plot(none_neg_roc_eb, add = TRUE, col = cbPalette[6])
plot(neg_pos_roc_eb, add = TRUE, col = cbPalette[7])
plot(neg_none_roc_eb, add = TRUE, col = cbPalette[8])
dev.off()
}
}
###########################################
if(discordant_test){
print("starting the discordant pipeline")
library(discordant)
if(!ddcor_test){
sim_data_a = t(sim_data_a)
sim_data_b = t(sim_data_b)
sim_data_merge = cbind(sim_data_a, sim_data_b)
rownames(sim_data_merge) = letters_unique[1:nrow(sim_data_merge)]
}
discordant_conditions = c(rep(1, ncol(sim_data_a)), rep(2, ncol(sim_data_b)))
discordant_start = Sys.time()
vectors = createVectors(sim_data_merge, groups = discordant_conditions)
result = discordantRun(vectors$v1, vectors$v2, sim_data_merge, transform = FALSE)
resultTable = makeTable(result$discordPPMatrix, sim_data_merge)
print(paste0("time taken to run the discordant pipeline with ", corr_classes, n_samples, " samples..."))
discordant_time = difftime(discordant_start, Sys.time(), units = "secs")
print(difftime(discordant_start, Sys.time()))
resultTable = as.data.frame(resultTable)
resultTable$genes = paste(resultTable$feature2, resultTable$feature1, sep = " ")
resultTable$prob = as.numeric(resultTable$`posterior probability`)
plot_groupwise_ROC_discordant <- function(res_pval = resultTable$prob,
pair_names = resultTable$genes, group_true,
all_true = true_cases){
group_true_vec = pair_names %in% group_true
to_remove = pair_names %in% setdiff(all_true, group_true)
tmp_df = data.frame(pvals = res_pval, true = group_true_vec)
tmp_df = tmp_df[!to_remove, ]
roc_list = roc(tmp_df$true, tmp_df$pvals, smooth = FALSE)
return(roc_list)
}
print("starting Discordant ROC curve calculation")
full_roc_discordant = plot_groupwise_ROC_discordant(group_true = true_cases)
print("full AUC for Discordant with n_samples...")
print(n_samples)
print(full_roc_discordant)
ddcor_line = c("discordant", corr_classes, n_samples, seed, discordant_time, as.numeric(full_roc_discordant$auc))
write.table(ddcor_line, "auc_results_classes.txt", sep = "\t", quote = FALSE, append = TRUE, col.names = FALSE,
row.names = FALSE)
result_transform_v1 = fishersTrans(vectors$v1)
result_transform_v2 = fishersTrans(vectors$v2)
result_transform = discordantRun(result_transform_v1, result_transform_v2, sim_data_merge, transform = FALSE)
resultTable_t = makeTable(result_transform$discordPPMatrix, sim_data_merge)
resultTable_t = as.data.frame(resultTable_t)
resultTable_t$genes = paste(resultTable_t$feature2, resultTable_t$feature1, sep = " ")
resultTable_t$prob = as.numeric(resultTable_t$`posterior probability`)
plot_groupwise_ROC_discordant_t <- function(res_pval = resultTable_t$prob,
pair_names = resultTable_t$genes, group_true,
all_true = true_cases){
group_true_vec = pair_names %in% group_true
to_remove = pair_names %in% setdiff(all_true, group_true)
tmp_df = data.frame(pvals = res_pval, true = group_true_vec)
tmp_df = tmp_df[!to_remove, ]
roc_list = roc(tmp_df$true, tmp_df$pvals, smooth = FALSE)
return(roc_list)
}
print("starting Discordant Fisher transformed ROC curve calculation")
full_roc_discordant_t = plot_groupwise_ROC_discordant_t(group_true = true_cases)
print("full AUC for Discordant Fisher transformed with n_samples...")
print(n_samples)
print(full_roc_discordant_t)
ddcor_line = c("discordant_t", corr_classes, n_samples, seed, discordant_time, as.numeric(full_roc_discordant_t$auc))
write.table(ddcor_line, "auc_results_classes.txt", sep = "\t", quote = FALSE, append = TRUE, col.names = FALSE,
row.names = FALSE)
if(compare_discordant_classes){
print("starting discordant ROC curve calculation for individual classes")
loc_roc_discordant = plot_groupwise_ROC_discordant_t(group_true = sigma_tot_b$loc_gene_pairs)
goc_roc_discordant = plot_groupwise_ROC_discordant_t(group_true = sigma_tot_b$goc_gene_pairs)
pos_none_roc_discordant = plot_groupwise_ROC_discordant_t(group_true = sigma_tot_b$pos_none_gene_pairs)
pos_neg_roc_discordant = plot_groupwise_ROC_discordant_t(group_true = sigma_tot_b$pos_neg_gene_pairs)
none_pos_roc_discordant = plot_groupwise_ROC_discordant_t(group_true = sigma_tot_b$none_pos_gene_pairs)
none_neg_roc_discordant = plot_groupwise_ROC_discordant_t(group_true = sigma_tot_b$none_neg_gene_pairs)
neg_pos_roc_discordant = plot_groupwise_ROC_discordant_t(group_true = sigma_tot_b$neg_pos_gene_pairs)
neg_none_roc_discordant = plot_groupwise_ROC_discordant_t(group_true = sigma_tot_b$neg_none_gene_pairs)
tiff(file = paste0("discordant_classes_", corr_classes, n_samples, "_samples.tiff"),
width = 4, height = 4, units = 'in', res = 300)
plot(loc_roc_discordant, col = cbPalette[1]) #lty = 2
plot(goc_roc_discordant, add = TRUE, col = cbPalette[2])
plot(pos_none_roc_discordant, add = TRUE, col = cbPalette[3])
plot(pos_neg_roc_discordant, add = TRUE, col = cbPalette[4])
plot(none_pos_roc_discordant, add = TRUE, col = cbPalette[5])
plot(none_neg_roc_discordant, add = TRUE, col = cbPalette[6])
plot(neg_pos_roc_discordant, add = TRUE, col = cbPalette[7])
plot(neg_none_roc_discordant, add = TRUE, col = cbPalette[8])
dev.off()
}
}
#############################################
#plot comparing the methods
if(compare_ddcor_eb_discordant){
tiff(file = paste0("ddcor_vs_ebcoexpress_vs_discordant_", corr_classes, n_samples, "_samples_seed", seed, ".tiff"),
width = 4, height = 4, units = 'in', res = 300)
plot(full_roc, col = "black")
plot(full_roc_eb, add = TRUE, col = "blue")
plot(full_roc_discordant, add = TRUE, col = "red")
plot(full_roc_discordant_t, add = TRUE, col = "orange")
dev.off()
}