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test_hist_match_pdx_gcsi.R
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test_hist_match_pdx_gcsi.R
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# rm(list = ls())
## Set-up system path...
PATH <- if (Sys.getenv("USERNAME") == "SRDhruba") {
"\\Users\\SRDhruba\\Dropbox (Personal)\\ResearchWork\\Rtest\\"
} else {
sprintf("%s\\Dropbox\\ResearchWork\\Rtest\\", Sys.getenv("HOMEPATH"))
}
setwd(PATH); cat("Current system path = ", getwd(), "\n")
## Packages...
library(progress)
library(DMTL)
#### Functions...
printf <- function(..., end = "\n") {
if ((nargs() > 1) & (grepl(list(...)[1], pattern = "%")))
cat(sprintf(...), end)
else
cat(..., end)
}
norm01 <- function(z) { z <- if (min(z)) z - min(z) else z; z <- z / max(z); z }
norm.data <- function(df) as.data.frame(apply(df, MARGIN = 2, norm01))
### Pick top genes...
get.top.genes <- function(ranks, m.top = 150, verbose = FALSE) {
## Initialization...
nI <- 0; nGN <- 300
gene.rank <- intersect(ranks[1:nGN, 1], ranks[1:nGN, 2])
m <- length(gene.rank); m0 <- if (verbose) m
## Run iterations...
while(m < m.top) {
nI <- nI + 1; nGN <- nGN + 100
gene.rank <- intersect(ranks[1:nGN, 1], ranks[1:nGN, 2])
m <- length(gene.rank)
}
gene.rank <- sort(gene.rank, decreasing = FALSE) # Sort ranks
## Print results...
if (verbose)
printf("#top genes chosen = %d (nGN = %d, nI = %d, m0 = %d)", m, nGN, nI, m0)
gene.rank
}
### Get model performance...
calc.perf <- function(y, y.pred, measures = c("NRMSE", "NMAE", "SCC")) {
## Initialize results array...
perf.vals <- c("sq.err" = NA, "abs.err" = NA, "cor.coef" = NA)
for (mes in measures) {
## Calculate squared error...
if (grepl(pattern = "MSE", mes, ignore.case = TRUE)) {
num <- mean((y - y.pred)^2)
den <- if (mes == "NRMSE") mean((y - mean(y))^2) else 1
pow <- if (mes == "MSE") 1 else 0.5
perf.vals["sq.err"] <- (num / den)^pow
}
## Calculate absolute error...
else if (grepl(pattern = "MAE", mes, ignore.case = TRUE)) {
num <- mean(abs(y - y.pred))
den <- if (mes == "NMAE") mean(abs(y - mean(y))) else 1
perf.vals["abs.err"] <- num / den
}
## Calculate similarity measures...
else if (grepl(pattern = "CC", mes, ignore.case = TRUE)) {
alg <- if (mes == "SCC") "spearman" else "pearson"
perf.vals["cor.coef"] <- cor(y, y.pred, method = alg)
}
## Doesn't match any...
else
stop("Invalid performance measure! Please use common variants of MSE, MAE or CC (correlation coefficient).")
}
perf.vals
}
#### Read tumor-cell line data...
Xdata1 <- read.table("Data/LUAD_gene_expression_TCGA_06_Dec_2020.txt", sep = "\t", header = TRUE)
Xdata2 <- read.table("Data/LUSC_gene_expression_TCGA_06_Dec_2020.txt", sep = "\t", header = TRUE)
Xdata3 <- read.table("Data/NSCLC_gene_expression_CCLE_06_Dec_2020.txt", sep = "\t", header = TRUE)
Xdata4 <- read.table("Data/NSCLC_gene_expression_GDSC_06_Dec_2020.txt", sep = "\t", header = TRUE)
Ydata1 <- read.table("Data/LUAD_biomarker_expression_TCGA_06_Dec_2020.txt", sep = "\t", header = TRUE)
Ydata2 <- read.table("Data/LUSC_biomarker_expression_TCGA_06_Dec_2020.txt", sep = "\t", header = TRUE)
Ydata3 <- read.table("Data/NSCLC_biomarker_expression_CCLE_06_Dec_2020.txt", sep = "\t", header = TRUE)
Ydata4 <- read.table("Data/NSCLC_biomarker_expression_GDSC_06_Dec_2020.txt", sep = "\t", header = TRUE)
rank1 <- read.table("Data/LUAD_biomarker_ranks_TCGA_06_Dec_2020.txt", sep = "\t", header = TRUE)
rank2 <- read.table("Data/LUSC_biomarker_ranks_TCGA_06_Dec_2020.txt", sep = "\t", header = TRUE)
rank3 <- read.table("Data/NSCLC_biomarker_ranks_CCLE_06_Dec_2020.txt", sep = "\t", header = TRUE)
rank4 <- read.table("Data/NSCLC_biomarker_ranks_GDSC_06_Dec_2020.txt", sep = "\t", header = TRUE)
biomarkers <- colnames(Ydata1); q <- length(biomarkers)
## Get results for all biomarkers...
# source("dist_match_trans_learn.R") ## Load function
run <- function(q.run, n.feat, random.seed, density.opt) {
# q.run <- 1 # drug idx
# random.seed <- 4321 # 0, 654321, 4321
# method.opt <- "dens" # hist, dens
## Save performance measures...
perf.mes <- c("NRMSE", "NMAE", "SCC")
results.all <- list(data.frame("DMTL" = double(), "DMTL_SS" = double(), "BL" = double()),
data.frame("DMTL" = double(), "DMTL_SS" = double(), "BL" = double()),
data.frame("DMTL" = double(), "DMTL_SS" = double(), "BL" = double()),
"genes" = data.frame("num.genes" = double()))
names(results.all)[1:3] <- perf.mes
pb <- progress_bar$new(format = " running [:bar] :percent eta: :eta", total = length(q.run), clear = FALSE, width = 64)
pb$tick(0)
for (k in q.run) {
pb$tick()
## Select biomarker...
bmChosen <- biomarkers[k]; #printf("\nChosen biomarker = %s", bmChosen)
ranks <- cbind(rank1[, bmChosen], rank2[, bmChosen], rank3[, bmChosen], rank4[, bmChosen])
gnRank <- get.top.genes(ranks[, 3:4], m.top = n.feat, verbose = FALSE); m <- length(gnRank)
## Prepare datasets...
X1 <- rbind(Xdata1[, gnRank], Xdata2[, gnRank]); X2 <- rbind(Xdata3[, gnRank], Xdata4[, gnRank])
Y1 <- norm01(c(Ydata1[, bmChosen], Ydata2[, bmChosen])); Y2 <- norm01(c(Ydata3[, bmChosen], Ydata4[, bmChosen]))
## DMTL model...
prediction <- DMTL(target_set = list("X" = X1, "y" = Y1), source_set = list("X" = X2, "y" = Y2),
use_density = density.opt, random_seed = random.seed, all_pred = TRUE)
Y1.pred <- prediction$target; Y1.pred.src <- prediction$source
## Baseline model...
# source("RF_predict.R") # Random forest modeling
Y1.pred.base <- RF_predict(x_train = norm.data(X2), y_train = Y2, x_test = norm.data(X1),
lims = c(0, 1), n_tree = 200, m_try = 0.4, random_seed = random.seed)
## Generate & save results...
results <- data.frame("DMTL" = calc.perf(Y1, Y1.pred, measures = perf.mes),
"DMTL_SS" = calc.perf(Y1, Y1.pred.src, measures = perf.mes),
"BL" = calc.perf(Y1, Y1.pred.base, measures = perf.mes), row.names = perf.mes)
## Print option...
if (length(q.run) == 1) { printf("\nResults for %s using top %d features = ", bmChosen, n.feat); print(results) }
results.all[[perf.mes[1]]][bmChosen, ] <- results[perf.mes[1], ]
results.all[[perf.mes[2]]][bmChosen, ] <- results[perf.mes[2], ]
results.all[[perf.mes[3]]][bmChosen, ] <- results[perf.mes[3], ]
results.all$genes[bmChosen, ] <- m
}
## Calculate mean performance...
results.all[[perf.mes[1]]]["Mean", ] <- colMeans(results.all[[perf.mes[1]]][biomarkers, ], na.rm = TRUE)
results.all[[perf.mes[2]]]["Mean", ] <- colMeans(results.all[[perf.mes[2]]][biomarkers, ], na.rm = TRUE)
results.all[[perf.mes[3]]]["Mean", ] <- colMeans(results.all[[perf.mes[3]]][biomarkers, ], na.rm = TRUE)
results.all$genes["Mean", ] <- mean(results.all$genes[biomarkers, ], na.rm = TRUE)
results.all[["table"]] <- rbind(results.all[[perf.mes[1]]]["Mean", ], results.all[[perf.mes[2]]]["Mean", ],
results.all[[perf.mes[3]]]["Mean", ])
rownames(results.all$table) <- perf.mes
## Print options...
if (length(q.run) > 1) { printf("\nResults summary for top %d features = ", n.feat); print(results.all$table) }
results.all
}
# source("dist_match_trans_learn.R") ## Load function
results.all <- run(q.run = 1:q, n.feat = 50, random.seed = 97531, density.opt = FALSE)
c(sum(results.all$NRMSE$DMTL >= 1), sum(results.all$NMAE$DMTL >= 1), sum(abs(results.all$SCC$DMTL) <= 0.2))