--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/ideas.R	Fri Jan 12 11:24:43 2018 -0500
@@ -0,0 +1,345 @@
+#!/usr/bin/env Rscript
+
+suppressPackageStartupMessages(library("data.table"))
+suppressPackageStartupMessages(library("optparse"))
+
+option_list <- list(
+    make_option(c("--burnin_num"), action="store", dest="burnin_num", type="integer", help="Number of burnin steps"),
+    make_option(c("--bychr"), action="store_true", dest="bychr", default=FALSE, help="Output chromosomes in separate files"),
+    make_option(c("--hp"), action="store_true", dest="hp", default=FALSE, help="Discourage state transition across chromosomes"),
+    make_option(c("--initial_states"), action="store", dest="initial_states", type="integer", default=NULL, help="Initial number of states"),
+    make_option(c("--log2"), action="store", dest="log2", type="double", default=NULL, help="log2 transformation"),
+    make_option(c("--maxerr"), action="store", dest="maxerr", type="double", default=NULL, help="Maximum standard deviation for the emission Gaussian distribution"),
+    make_option(c("--max_cell_type_clusters"), action="store", dest="max_cell_type_clusters", type="integer", default=NULL, help="Maximum number of cell type clusters allowed"),
+    make_option(c("--max_position_classes"), action="store", dest="max_position_classes", type="integer", default=NULL, help="Maximum number of position classes to be inferred"),
+    make_option(c("--max_states"), action="store", dest="max_states", type="double", default=NULL, help="Maximum number of states to be inferred"),
+    make_option(c("--mcmc_num"), action="store", dest="mcmc_num", type="integer", help="Number of maximization steps"),
+    make_option(c("--minerr"), action="store", dest="minerr", type="double", default=NULL, help="Minimum standard deviation for the emission Gaussian distribution"),
+    make_option(c("--norm"), action="store_true", dest="norm", default=FALSE, help="Standardize all datasets"),
+    make_option(c("--output_log"), action="store", dest="output_log", default=NULL, help="Output log file path"),
+    make_option(c("--output_txt_dir"), action="store", dest="output_txt_dir", help="Directory for output txt files"),
+    make_option(c("--prep_output_config"), action="store", dest="prep_output_config", help="prepMat output config file"),
+    make_option(c("--prior_concentration"), action="store", dest="prior_concentration", type="double", default=NULL, help="Prior concentration"),
+    make_option(c("--project_name"), action="store", dest="project_name", help="Outputs will have this base name"),
+    make_option(c("--rseed"), action="store", dest="rseed", type="integer", help="Seed for IDEAS model initialization"),
+    make_option(c("--save_ideas_log"), action="store", dest="save_ideas_log", default=NULL, help="Flag to save IDEAS process log"),
+    make_option(c("--script_dir"), action="store", dest="script_dir", help="R script source directory"),
+    make_option(c("--thread"), action="store", dest="thread", type="integer", help="Process threads"),
+    make_option(c("--tmp_dir"), action="store", dest="tmp_dir", help="Directory of bed files"),
+    make_option(c("--training_iterations"), action="store", dest="training_iterations", type="integer", default=NULL, help="Number of training iterations"),
+    make_option(c("--training_windows"), action="store", dest="training_windows", type="integer", default=NULL, help="Number of training iterations"),
+    make_option(c("--windows_bed"), action="store", dest="windows_bed", default=NULL, help="Bed file containing bed windows"),
+    make_option(c("--window_end"), action="store", dest="window_end", type="integer", default=NULL, help="Windows positions by chromosome end value"),
+    make_option(c("--window_start"), action="store", dest="window_start", type="integer", default=NULL, help="Windows positions by chromosome start value")
+)
+
+parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
+args <- parse_args(parser, positional_arguments=TRUE)
+opt <- args$options
+
+add_output_redirect <- function(cmd, save_ideas_log, output_log, default_log_name) {
+    if (is.null(save_ideas_log)) {
+        cmd = paste(cmd, "&>>", default_log_name, sep=" ");
+    }else {
+        cmd = paste(cmd, "&>>", output_log, sep=" ");
+    }
+    return(cmd);
+}
+
+combine_state <- function(parafiles, method="ward.D", mycut=0.9, pcut=1.0) {
+    X = NULL;
+    K = NULL;
+    I = NULL;
+    myheader = NULL;
+    p = NULL;
+    for(i in 1:length(parafiles)) {
+        x = fread(parafiles[i]);
+        t = max(which(is.na(x[1,])==F));
+        x = as.matrix(x[,1:t]);
+        if(i==1) {
+            myheader = colnames(x);
+            p = sqrt(9/4-2*(1-length(myheader))) - 3 / 2;
+        }
+        m = match(myheader[1:p+1], colnames(x)[1:p+1]);
+        v = NULL;
+        for(ii in 1:p) {
+            for(jj in 1:ii) {
+                a = max(m[ii],m[jj]);
+                b = min(m[ii],m[jj]);
+                v = c(v, a*(a+1)/2+b-a);
+            }
+        }
+        X = rbind(X, array(as.matrix(x[, c(1, 1+m, 1+p+v)]), dim=c(length(x) / (1+p+length(v)), 1 + p + length(v))));
+        K = c(K, dim(x)[1]);
+        I = c(I, rep(i, dim(x)[1]));
+    }
+    N = length(parafiles);
+    p = (sqrt(1 + dim(X)[2] * 8) - 3) / 2;
+    omycut = mycut;
+    mycut = round(length(parafiles) * mycut);
+    M = array(X[,1:p+1] / X[,1], dim=c(dim(X)[1], p));
+    V = array(0, dim=c(dim(X)[1] * p, p));
+    for(i in 1:dim(X)[1]) {
+        t = (i - 1) * p;
+        l = 1;
+        for(j in 1:p) {
+            for(k in 1:j) {
+                V[t+j, k] = V[t+k, j] = X[i,1+p+l] / X[i,1] - M[i,j] * M[i,k];
+                l = l + 1;
+            }
+        }
+        V[t+1:p,] = t(solve(chol(V[t+1:p,] + diag(1e-1,p))));
+    }
+    D = array(0, dim=rep(dim(X)[1],2));
+    for(i in 2:dim(X)[1]) {
+        for(j in 1:(i-1)) {
+            D[i,j] = D[j,i] = sqrt((sum((V[(i-1)*p+1:p,]%*%(M[i,]-M[j,]))^2) + sum((V[(j-1)*p+1:p,]%*%(M[i,]-M[j,]))^2)));
+        }
+    }
+    MM = NULL;
+    kk = NULL;
+    for(i in 1:N) {
+        t = 1:K[i];
+        if(i > 1) {
+            t = t + sum(K[1:(i-1)]);
+        }
+        t = (1:dim(D)[1])[-t];
+        h = hclust(as.dist(D[t,t]), method=method);
+        k = -1;
+        tM = NULL;
+        for(j in min(K):(min(length(t), max(K)*2))) {
+            m = cutree(h,k=j);
+            tt = NULL;
+            for(l in 1:j) {
+                tt[l] = length(unique(I[t[which(m==l)]]));
+            }
+            tk = length(which(tt>=mycut));
+            if(tk > k) {
+                k = tk;
+                tM = make_parameter(1:j, I[t], m, mycut, X[t,]);
+            } else if(tk < k) {
+                break;
+            }
+        }
+        kk[i] = k;
+        MM = rbind(MM, cbind(i, tM));
+    }
+    mysel = median(kk);
+    h = hclust(as.dist(D), method=method);
+    rt = rep(0, max(K)*2);
+    k = -1;
+    for(i in min(K):min(dim(D)[1], max(K)*2)) {
+        m = cutree(h,k=i);
+        tt = NULL;
+        for(j in 1:i) {
+            tt[j] = length(unique(I[which(m==j)]));
+        }
+        tk = length(which(tt>=mycut));
+        if(tk==mysel | tk<k) {
+            break;
+        }
+        k = tk;
+        rt[i] = length(which(tt>=mycut));
+    }
+    mysel = max(k,tk);
+    m = cutree(h, k=mysel);
+    nn = NULL;
+    for(i in 1:mysel) {
+        t = which(m==i);
+        nn[i] = sum(X[t,1]);
+    }
+    oo = order(nn, decreasing=T);
+    rt = make_parameter(oo, I, m, mycut, X);
+    onstate = max(rt[,1]) + 1;
+    ooo = NULL;
+    for(i in oo) {
+        t = which(m==i);
+        if(length(unique(I[t])) >= mycut) {
+            ooo = c(ooo, i);
+        }
+    }
+    d = NULL;
+    for(i in 1:N) {
+        d = rbind(d, compare_two(rt, MM[MM[,1]==i,-1])[1:onstate]);
+    }
+    dd = array(cutree(hclust(dist(c(d))), k=2), dim=dim(d));
+    kk = table(c(dd));
+    kk = which(as.integer(kk)==max(as.integer(kk)))[1];
+    pp = apply(dd, 2, function(x){length(which(x!=kk))/length(x)});
+    pp0 = apply(d, 2, function(x){length(which(x>0.5))/length(x)});
+    pp[pp0<pp] = pp0[pp0<pp];
+    t = which(pp > pcut);
+    if(length(t) > 0) {
+        j = 0;
+        nrt = NULL;
+        for(i in (1:onstate-1)[-t]) {
+            nrt = rbind(nrt, cbind(j, rt[rt[,1]==i,-1]));
+            j = j + 1;
+        }
+        rt = nrt;
+        ooo = ooo[-t];
+    }
+    nrt = NULL;
+    for(i in 0:max(rt[,1])) {
+        t = which(rt[,1]==i);
+        nrt = rbind(nrt, apply(array(rt[t,], dim=c(length(t), dim(rt)[2])), 2, sum)[-1]);
+    }
+    rt = nrt;
+    colnames(rt) = myheader;
+    O = NULL;
+    Ip = NULL;
+    Xp = NULL;
+    k = 0;
+    for(i in 1:length(parafiles)) {
+        str = gsub(".para", ".profile", parafiles[i]);
+        p = as.matrix(read.table(str));
+        u = array(0, dim=c(dim(p)[1], length(ooo)));
+        for(j in 1:length(ooo)) {
+            t = which(m[k+1:K[i]] == ooo[j]);
+            u[,j] = apply(array(p[,1+t], dim=c(dim(p)[1], length(t))), 1, sum);
+        }
+        k = k + K[i];
+        u = u / (apply(u, 1, sum) + 1e-10);
+        Xp = rbind(Xp, cbind(p[,1], u));
+        Ip = c(Ip, rep(i,dim(u)[1]));
+    }
+    hp = hclust(dist(((Xp[,-1]+min(1e-3, min(Xp[,-1][Xp[,-1]>0]))))), method=method);
+    ocut = min(mycut/2, length(parafiles)/2);
+    t = range(as.integer(table(Ip)));
+    Kp = NULL;
+    for(i in t[1]:(t[2]*2)) {
+        m = cutree(hp, k=i);
+        tt = table(Ip,m);
+        ll = apply(tt, 2, function(x){length(which(x>0))});
+        Kp = c(Kp, length(which(ll>=ocut)));
+    }
+    oN = (t[1]:(t[2]*2))[which(Kp==max(Kp))[1]];
+    m = cutree(hp, k=oN);
+    tt = table(Ip,m);
+    ll = apply(tt, 2, function(x){length(which(x>0))});
+    tt = which(ll>=ocut);
+    for(i in tt) {
+        t = which(m==i);
+        O = rbind(O, c(sum(Xp[t, 1]), apply(array(Xp[t,-1]*Xp[t,1], dim=c(length(t), dim(Xp)[2]-1)), 2, sum)/sum(Xp[t, 1])));
+    }
+    nrt = NULL;
+    nrt$para = rt;
+    nrt$profile = O;
+    return(nrt);
+}
+
+compare_two <- function(n, m) {
+    NN = get_mean(n);
+    MM = get_mean(m);
+    p = (-3 + sqrt(9 + 8 * (dim(n)[2] - 2))) / 2;
+    dd = NULL;
+    for (i in 1:dim(NN)[1]) {
+        dd[i] = min(apply(array(MM[,1:p], dim=c(dim(MM)[1],p)), 1, function(x){sqrt(sum((x-NN[i,1:p])^2))}));
+    }
+    for (i in 1:dim(MM)[1]) {
+        dd[i+dim(NN)[1]] = min(apply(array(NN[,1:p], dim=c(dim(NN)[1],p)), 1, function(x){sqrt(sum((x-MM[i,1:p])^2))}));
+    }
+    return(dd);
+}
+
+get_mean <- function(n) {
+    N = NULL;
+    for(i in sort(unique(n[,1]))) {
+        t = which(n[,1]==i);
+        N = rbind(N, apply(array(n[t,], dim=c(length(t), dim(n)[2])), 2, sum)[-1]);
+    }
+    NN = N[,-1] / N[,1];
+    return(array(NN, dim=c(length(NN)/(dim(n)[2]-2), dim(n)[2]-2)));
+}
+
+make_parameter <- function(myorder, id, mem, mycut, para) {
+    rt = NULL;
+    j = 0;
+    for(i in myorder) {
+        t = which(mem==i);
+        if (length(unique(id[t])) >= mycut) {
+            rt = rbind(rt, cbind(j, array(para[t,], dim=c(length(t), dim(para)[2]))));
+            j = j + 1;
+        }
+    }
+    return(rt);
+}
+
+run_cmd <- function(cmd, save_ideas_log, output_log, default_log_name) {
+    cat("\n\n >>>>> cmd:\n", cmd, "\n\n");
+    rc = system(cmd);
+    if (rc != 0) {
+        if (is.null(save_ideas_log)) {
+            file.rename(default_log_name, output_log);
+        }
+        quit(rc);
+    }
+}
+
+default_log_name = "ideas_log.txt";
+output_base_name = opt$project_name;
+cmd = paste("ideas", opt$prep_output_config, sep=" ");
+if (!is.null(opt$windows_bed)) {
+    cmd = paste(cmd, opt$windows_bed, sep=" ");
+}
+if (!is.null(opt$training_iterations)) {
+    cmd = paste(cmd, "-impute none", sep=" ");
+}
+if (opt$bychr) {
+    cmd = paste(cmd, "-bychr", sep=" ");
+}
+if (opt$hp) {
+    cmd = paste(cmd, "-hp", sep=" ");
+}
+if (opt$norm) {
+    cmd = paste(cmd, "-norm", sep=" ");
+}
+if (!is.null(opt$window_start) && !is.null(opt$window_end)) {
+    cmd = paste(cmd, "-inv", opt$window_start, opt$window_end, sep=" ");
+}
+if (!is.null(opt$log2)) {
+    cmd = paste(cmd, "-log2", opt$log2, sep=" ");
+}
+if (!is.null(opt$max_states)) {
+    cmd = paste(cmd, "-G", opt$max_states, sep=" ");
+}
+if (!is.null(opt$initial_states)) {
+    cmd = paste(cmd, "-C", opt$initial_states, sep=" ");
+}
+if (!is.null(opt$max_position_classes)) {
+    cmd = paste(cmd, "-P", opt$max_position_classes, sep=" ");
+}
+if (!is.null(opt$max_cell_type_clusters)) {
+    cmd = paste(cmd, "-K", opt$max_cell_type_clusters, sep=" ");
+}
+if (!is.null(opt$prior_concentration)) {
+    cmd = paste(cmd, "-A", opt$prior_concentration, sep=" ");
+}
+cmd = paste(cmd, "-sample", opt$burnin_num, opt$mcmc_num, sep=" ");
+if (!is.null(opt$minerr)) {
+    cmd = paste(cmd, "-minerr", opt$minerr, sep=" ");
+}
+if (!is.null(opt$maxerr)) {
+    cmd = paste(cmd, "-maxerr", opt$maxerr, sep=" ");
+}
+cmd = paste(cmd, "-rseed", opt$rseed, sep=" ");
+cmd = paste(cmd, "-thread", opt$thread, sep=" ");
+
+if (is.null(opt$training_iterations)) {
+    cmd = paste(cmd, "-o", output_base_name, sep=" ");
+    cmd = add_output_redirect(cmd, opt$save_ideas_log, opt$output_log, default_log_name);
+    run_cmd(cmd, opt$save_ideas_log, opt$output_log, default_log_name);
+} else {
+    for (i in 1:opt$training_iterations) {
+        cmd = paste(cmd, "-o", paste(output_base_name, ".tmp.", i, sep=""), sep=" ");
+        cmd = add_output_redirect(cmd, opt$save_ideas_log, opt$output_log, default_log_name);
+        run_cmd(cmd, opt$save_ideas_log, opt$output_log, default_log_name);
+    }
+    tpara = combine_state(paste(output_base_name, ".tmp.", (1:opt$training_iterations), ".para", sep=""), mycut=0.5);
+    para = tpara$para;
+    write.table(tpara$profile, paste(output_base_name, ".profile0", sep=""), quote=F, row.names=F, col.names=F);
+    para = apply(para, 1, function(x){paste(x, collapse=" ")});
+    para = c(readLines(paste(output_base_name, ".tmp.1.para", sep=""), n=1), para);
+    output_para0 = paste(output_base_name, ".para0", sep="");
+    writeLines(para, output_para0);
+}