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150
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1 #!/usr/bin/env Rscript
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2
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3 suppressPackageStartupMessages(library("data.table"))
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4 suppressPackageStartupMessages(library("optparse"))
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5
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6 option_list <- list(
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175
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7 make_option(c("--burnin_num"), action="store", dest="burnin_num", type="integer", help="Number of burnin steps"),
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8 make_option(c("--bychr"), action="store_true", dest="bychr", default=FALSE, help="Output chromosomes in separate files"),
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9 make_option(c("--chrom_bed_input"), action="store", dest="chrom_bed_input", default=NULL, help="Chromosome windows positions file"),
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10 make_option(c("--chromosome_windows"), action="store", dest="chromosome_windows", default=NULL, help="Windows positions by chroms config file"),
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11 make_option(c("--hp"), action="store_true", dest="hp", default=FALSE, help="Discourage state transition across chromosomes"),
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12 make_option(c("--initial_states"), action="store", dest="initial_states", type="integer", default=NULL, help="Initial number of states"),
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13 make_option(c("--ideas_input_config"), action="store", dest="ideas_input_config", help="IDEAS_input_config file"),
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14 make_option(c("--log2"), action="store", dest="log2", type="double", default=NULL, help="log2 transformation"),
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15 make_option(c("--maxerr"), action="store", dest="maxerr", type="double", default=NULL, help="Maximum standard deviation for the emission Gaussian distribution"),
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16 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"),
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17 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"),
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18 make_option(c("--max_states"), action="store", dest="max_states", type="double", default=NULL, help="Maximum number of states to be inferred"),
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19 make_option(c("--mcmc_num"), action="store", dest="mcmc_num", type="integer", help="Number of maximization steps"),
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20 make_option(c("--minerr"), action="store", dest="minerr", type="double", default=NULL, help="Minimum standard deviation for the emission Gaussian distribution"),
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21 make_option(c("--output_dir"), action="store", dest="output_dir", help="Output directory, used only if job ends in error and process log needs saving"),
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22 make_option(c("--output_log"), action="store", dest="output_log", default=NULL, help="Output log file path"),
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23 make_option(c("--prior_concentration"), action="store", dest="prior_concentration", type="double", default=NULL, help="Prior concentration"),
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24 make_option(c("--project_name"), action="store", dest="project_name", help="Outputs will have this base name"),
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25 make_option(c("--rseed"), action="store", dest="rseed", type="integer", help="Seed for IDEAS model initialization"),
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26 make_option(c("--save_ideas_log"), action="store", dest="save_ideas_log", default=NULL, help="Flag to save IDEAS process log"),
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27 make_option(c("--standardize_datasets"), action="store_true", dest="standardize_datasets", default=FALSE, help="Standardize all datasets"),
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28 make_option(c("--thread"), action="store", dest="thread", type="integer", help="Process threads"),
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29 make_option(c("--training_iterations"), action="store", dest="training_iterations", type="integer", default=NULL, help="Number of training iterations"),
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30 make_option(c("--training_windows"), action="store", dest="training_windows", type="integer", default=NULL, help="Number of training iterations")
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150
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31 )
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32
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33 parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
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34 args <- parse_args(parser, positional_arguments=TRUE)
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35 opt <- args$options
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36
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174
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37 add_output_redirect <- function(cmd, output_log) {
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38 new_cmd = c(cmd, "&>>", output_log);
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166
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39 return(paste(new_cmd, collapse=" "));
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150
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40 }
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41
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42 combine_state <- function(parafiles, method="ward.D", mycut=0.9, pcut=1.0) {
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43 X = NULL;
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44 K = NULL;
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45 I = NULL;
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46 myheader = NULL;
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47 p = NULL;
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48 for(i in 1:length(parafiles)) {
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49 x = fread(parafiles[i]);
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50 t = max(which(is.na(x[1,])==F));
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51 x = as.matrix(x[,1:t]);
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52 if(i==1) {
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53 myheader = colnames(x);
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54 p = sqrt(9/4-2*(1-length(myheader))) - 3 / 2;
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55 }
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56 m = match(myheader[1:p+1], colnames(x)[1:p+1]);
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57 v = NULL;
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58 for(ii in 1:p) {
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59 for(jj in 1:ii) {
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60 a = max(m[ii],m[jj]);
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61 b = min(m[ii],m[jj]);
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62 v = c(v, a*(a+1)/2+b-a);
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63 }
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64 }
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65 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))));
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66 K = c(K, dim(x)[1]);
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67 I = c(I, rep(i, dim(x)[1]));
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68 }
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69 N = length(parafiles);
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70 p = (sqrt(1 + dim(X)[2] * 8) - 3) / 2;
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71 omycut = mycut;
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72 mycut = round(length(parafiles) * mycut);
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73 M = array(X[,1:p+1] / X[,1], dim=c(dim(X)[1], p));
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74 V = array(0, dim=c(dim(X)[1] * p, p));
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75 for(i in 1:dim(X)[1]) {
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76 t = (i - 1) * p;
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77 l = 1;
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78 for(j in 1:p) {
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79 for(k in 1:j) {
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80 V[t+j, k] = V[t+k, j] = X[i,1+p+l] / X[i,1] - M[i,j] * M[i,k];
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81 l = l + 1;
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82 }
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83 }
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84 V[t+1:p,] = t(solve(chol(V[t+1:p,] + diag(1e-1,p))));
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85 }
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86 D = array(0, dim=rep(dim(X)[1],2));
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87 for(i in 2:dim(X)[1]) {
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88 for(j in 1:(i-1)) {
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89 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)));
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90 }
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91 }
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92 MM = NULL;
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93 kk = NULL;
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94 for(i in 1:N) {
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95 t = 1:K[i];
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96 if(i > 1) {
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97 t = t + sum(K[1:(i-1)]);
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98 }
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99 t = (1:dim(D)[1])[-t];
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100 h = hclust(as.dist(D[t,t]), method=method);
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101 k = -1;
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102 tM = NULL;
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103 for(j in min(K):(min(length(t), max(K)*2))) {
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104 m = cutree(h,k=j);
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105 tt = NULL;
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106 for(l in 1:j) {
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107 tt[l] = length(unique(I[t[which(m==l)]]));
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108 }
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109 tk = length(which(tt>=mycut));
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110 if(tk > k) {
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111 k = tk;
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112 tM = make_parameter(1:j, I[t], m, mycut, X[t,]);
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113 } else if(tk < k) {
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114 break;
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115 }
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116 }
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117 kk[i] = k;
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118 MM = rbind(MM, cbind(i, tM));
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119 }
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120 mysel = median(kk);
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121 h = hclust(as.dist(D), method=method);
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122 rt = rep(0, max(K)*2);
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123 k = -1;
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124 for(i in min(K):min(dim(D)[1], max(K)*2)) {
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125 m = cutree(h,k=i);
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126 tt = NULL;
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127 for(j in 1:i) {
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128 tt[j] = length(unique(I[which(m==j)]));
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129 }
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130 tk = length(which(tt>=mycut));
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131 if(tk==mysel | tk<k) {
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132 break;
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133 }
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134 k = tk;
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135 rt[i] = length(which(tt>=mycut));
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136 }
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137 mysel = max(k,tk);
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138 m = cutree(h, k=mysel);
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139 nn = NULL;
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140 for(i in 1:mysel) {
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141 t = which(m==i);
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142 nn[i] = sum(X[t,1]);
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143 }
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144 oo = order(nn, decreasing=T);
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145 rt = make_parameter(oo, I, m, mycut, X);
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146 onstate = max(rt[,1]) + 1;
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147 ooo = NULL;
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148 for(i in oo) {
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149 t = which(m==i);
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150 if(length(unique(I[t])) >= mycut) {
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151 ooo = c(ooo, i);
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152 }
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153 }
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154 d = NULL;
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155 for(i in 1:N) {
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156 d = rbind(d, compare_two(rt, MM[MM[,1]==i,-1])[1:onstate]);
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157 }
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158 dd = array(cutree(hclust(dist(c(d))), k=2), dim=dim(d));
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159 kk = table(c(dd));
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160 kk = which(as.integer(kk)==max(as.integer(kk)))[1];
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161 pp = apply(dd, 2, function(x){length(which(x!=kk))/length(x)});
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162 pp0 = apply(d, 2, function(x){length(which(x>0.5))/length(x)});
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163 pp[pp0<pp] = pp0[pp0<pp];
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164 t = which(pp > pcut);
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165 if(length(t) > 0) {
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166 j = 0;
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167 nrt = NULL;
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168 for(i in (1:onstate-1)[-t]) {
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169 nrt = rbind(nrt, cbind(j, rt[rt[,1]==i,-1]));
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170 j = j + 1;
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171 }
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172 rt = nrt;
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173 ooo = ooo[-t];
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174 }
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175 nrt = NULL;
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176 for(i in 0:max(rt[,1])) {
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177 t = which(rt[,1]==i);
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178 nrt = rbind(nrt, apply(array(rt[t,], dim=c(length(t), dim(rt)[2])), 2, sum)[-1]);
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179 }
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180 rt = nrt;
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181 colnames(rt) = myheader;
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182 O = NULL;
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183 Ip = NULL;
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184 Xp = NULL;
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185 k = 0;
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186 for(i in 1:length(parafiles)) {
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187 str = gsub(".para", ".profile", parafiles[i]);
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188 p = as.matrix(read.table(str));
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189 u = array(0, dim=c(dim(p)[1], length(ooo)));
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190 for(j in 1:length(ooo)) {
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191 t = which(m[k+1:K[i]] == ooo[j]);
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192 u[,j] = apply(array(p[,1+t], dim=c(dim(p)[1], length(t))), 1, sum);
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193 }
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194 k = k + K[i];
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195 u = u / (apply(u, 1, sum) + 1e-10);
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196 Xp = rbind(Xp, cbind(p[,1], u));
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197 Ip = c(Ip, rep(i,dim(u)[1]));
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198 }
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199 hp = hclust(dist(((Xp[,-1]+min(1e-3, min(Xp[,-1][Xp[,-1]>0]))))), method=method);
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200 ocut = min(mycut/2, length(parafiles)/2);
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201 t = range(as.integer(table(Ip)));
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202 Kp = NULL;
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203 for(i in t[1]:(t[2]*2)) {
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204 m = cutree(hp, k=i);
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205 tt = table(Ip,m);
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206 ll = apply(tt, 2, function(x){length(which(x>0))});
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207 Kp = c(Kp, length(which(ll>=ocut)));
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208 }
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209 oN = (t[1]:(t[2]*2))[which(Kp==max(Kp))[1]];
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210 m = cutree(hp, k=oN);
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211 tt = table(Ip,m);
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212 ll = apply(tt, 2, function(x){length(which(x>0))});
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213 tt = which(ll>=ocut);
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214 for(i in tt) {
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215 t = which(m==i);
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216 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])));
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217 }
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218 nrt = NULL;
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219 nrt$para = rt;
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220 nrt$profile = O;
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221 return(nrt);
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222 }
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223
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224 compare_two <- function(n, m) {
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225 NN = get_mean(n);
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226 MM = get_mean(m);
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227 p = (-3 + sqrt(9 + 8 * (dim(n)[2] - 2))) / 2;
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228 dd = NULL;
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229 for (i in 1:dim(NN)[1]) {
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230 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))}));
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231 }
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232 for (i in 1:dim(MM)[1]) {
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233 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))}));
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234 }
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235 return(dd);
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236 }
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237
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170
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238 get_base_cmd <- function(ideas_input_config, chrom_bed_input, training_iterations, bychr, hp, standardize_datasets, log2,
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166
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239 max_states, initial_states, max_position_classes, max_cell_type_clusters, prior_concentration,
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240 burnin_num, mcmc_num, minerr, maxerr, rseed, thread) {
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170
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241 base_cmd = paste("ideas", ideas_input_config, sep=" ");
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242 if (!is.null(chrom_bed_input)) {
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243 base_cmd = paste(base_cmd, chrom_bed_input, sep=" ");
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166
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244 }
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245 if (!is.null(training_iterations)) {
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246 base_cmd = paste(base_cmd, "-impute none", sep=" ");
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247 }
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248 if (bychr) {
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249 base_cmd = paste(base_cmd, "-bychr", sep=" ");
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250 }
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251 if (hp) {
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252 base_cmd = paste(base_cmd, "-hp", sep=" ");
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253 }
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170
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254 if (standardize_datasets) {
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166
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255 base_cmd = paste(base_cmd, "-norm", sep=" ");
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256 }
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257 if (!is.null(log2)) {
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258 base_cmd = paste(base_cmd, "-log2", log2, sep=" ");
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259 }
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260 if (!is.null(max_states)) {
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261 base_cmd = paste(base_cmd, "-G", max_states, sep=" ");
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262 }
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263 if (!is.null(initial_states)) {
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264 base_cmd = paste(base_cmd, "-C", initial_states, sep=" ");
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265 }
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266 if (!is.null(max_position_classes)) {
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267 base_cmd = paste(base_cmd, "-P", max_position_classes, sep=" ");
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268 }
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269 if (!is.null(max_cell_type_clusters)) {
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270 base_cmd = paste(base_cmd, "-K", max_cell_type_clusters, sep=" ");
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271 }
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272 if (!is.null(prior_concentration)) {
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273 base_cmd = paste(base_cmd, "-A", prior_concentration, sep=" ");
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274 }
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275 base_cmd = paste(base_cmd, "-sample", burnin_num, mcmc_num, sep=" ");
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276 if (!is.null(minerr)) {
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277 base_cmd = paste(base_cmd, "-minerr", minerr, sep=" ");
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278 }
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279 if (!is.null(maxerr)) {
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280 base_cmd = paste(base_cmd, "-maxerr", maxerr, sep=" ");
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281 }
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282 base_cmd = paste(base_cmd, "-rseed", rseed, sep=" ");
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283 base_cmd = paste(base_cmd, "-thread", thread, sep=" ");
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284 return(base_cmd);
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285 }
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286
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150
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287 get_mean <- function(n) {
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288 N = NULL;
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289 for(i in sort(unique(n[,1]))) {
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290 t = which(n[,1]==i);
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291 N = rbind(N, apply(array(n[t,], dim=c(length(t), dim(n)[2])), 2, sum)[-1]);
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292 }
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293 NN = N[,-1] / N[,1];
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294 return(array(NN, dim=c(length(NN)/(dim(n)[2]-2), dim(n)[2]-2)));
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295 }
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296
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166
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297 get_post_training_base_cmd <- function(base_cmd, para) {
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298 # Change base_cmd due to training mode.
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299 base_cmd_items = as.list(strsplit(base_cmd[1], split=" ", fixed=TRUE))[[1]];
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300 if (length(which(base_cmd_items == "-G")) == 0) {
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301 base_cmd_items = c(base_cmd_items, "-G", length(para)-1);
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302 } else {
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303 tt = which(base_cmd_items == "-G");
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304 base_cmd_items[tt + 1] = length(para)-1;
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305 }
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306 tt = which(base_cmd_items == '-C');
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307 if(length(tt) > 0) {
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308 base_cmd_items = base_cmd_items[-c(tt, tt+1)];
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309 }
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310 base_cmd = paste(base_cmd_items, collapse=" ");
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311 return(base_cmd);
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312 }
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313
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170
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314 get_windows_by_chrom <- function(chromosome_windows) {
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315 fh = file(chromosome_windows, "r");
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316 windows_by_chrom = readLines(fh);
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317 close(fh);
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166
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318 return(windows_by_chrom);
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319 }
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320
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150
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321 make_parameter <- function(myorder, id, mem, mycut, para) {
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322 rt = NULL;
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323 j = 0;
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324 for(i in myorder) {
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325 t = which(mem==i);
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326 if (length(unique(id[t])) >= mycut) {
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327 rt = rbind(rt, cbind(j, array(para[t,], dim=c(length(t), dim(para)[2]))));
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328 j = j + 1;
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329 }
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330 }
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331 return(rt);
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332 }
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333
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166
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334 remove_files <- function(path, pattern) {
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335 files = list.files(path=path, pattern=pattern);
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336 for (f in files) {
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337 unlink(f);
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338 }
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339 }
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340
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174
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341 run_cmd <- function(cmd, save_ideas_log, output_log, output_dir) {
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150
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342 rc = system(cmd);
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343 if (rc != 0) {
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344 if (is.null(save_ideas_log)) {
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174
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345 to_path = paste(output_dir, output_log, sep="/");
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346 file.rename(output_log, to_path);
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150
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347 }
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167
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348 quit(save="no", status=rc);
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150
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349 }
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350 }
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351
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170
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352 # Initialize values.
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174
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353 if (is.null(opt$save_ideas_log)) {
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354 output_log = "ideas_log.txt";
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355 } else {
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356 output_log = opt$output_log;
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357 }
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175
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358 if (is.null(opt$chromosome_windows)) {
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170
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359 windows_by_chrom = NULL;
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360 } else {
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361 # Read chromosome_windows.txt into memory.
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179
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362 windows_by_chrom = get_windows_by_chrom(opt$chromosome_windows);
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170
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363 }
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175
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364 base_cmd = get_base_cmd(opt$ideas_input_config, opt$chrom_bed_input, opt$training_iterations, opt$bychr, opt$hp,
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174
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365 opt$standardize_datasets, opt$log2, opt$max_states, opt$initial_states, opt$max_position_classes,
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366 opt$max_cell_type_clusters, opt$prior_concentration, opt$burnin_num, opt$mcmc_num, opt$minerr,
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367 opt$maxerr, opt$rseed, opt$thread);
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150
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368 output_base_name = opt$project_name;
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175
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369 # Perform analysis.
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150
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370 if (is.null(opt$training_iterations)) {
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166
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371 # Not performing training.
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372 if (is.null(windows_by_chrom)) {
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373 # Not performing windows by chromosome.
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374 output_name = output_base_name;
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375 cmd = paste(base_cmd, "-o", output_name, sep=" ");
|
|
174
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376 cmd = add_output_redirect(cmd, output_log);
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377 run_cmd(cmd, opt$save_ideas_log, output_log, opt$output_dir);
|
|
166
|
378 } else {
|
|
|
379 # Performing windows by chromosome.
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380 for (i in 1:length(windows_by_chrom)) {
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381 line = windows_by_chrom[i];
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|
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382 items = strsplit(line, " ")[[1]];
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|
|
383 chrom = items[1];
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|
|
384 window_start = items[2];
|
|
|
385 window_end = items[3];
|
|
167
|
386 output_name = paste(output_base_name, chrom, sep=".");
|
|
166
|
387 cmd = paste(base_cmd, "-inv", window_start, window_end, sep=" ");
|
|
|
388 cmd = paste(cmd, "-o", output_name, sep=" ");
|
|
174
|
389 cmd = add_output_redirect(cmd, output_log);
|
|
|
390 run_cmd(cmd, opt$save_ideas_log, output_log, opt$output_dir);
|
|
166
|
391 }
|
|
|
392 }
|
|
150
|
393 } else {
|
|
175
|
394 # Performing training.
|
|
166
|
395 output_para0 = paste(output_base_name, "para0", sep=".");
|
|
|
396 output_profile0 = paste(output_base_name, "profile0", sep=".");
|
|
150
|
397 for (i in 1:opt$training_iterations) {
|
|
166
|
398 cmd = paste(base_cmd, "-o", paste(output_base_name, ".tmp.", i, sep=""), sep=" ");
|
|
174
|
399 cmd = add_output_redirect(cmd, output_log);
|
|
|
400 run_cmd(cmd, opt$save_ideas_log, output_log, opt$output_dir);
|
|
150
|
401 }
|
|
166
|
402 tpara = combine_state(paste(output_base_name, "tmp", (1:opt$training_iterations), "para", sep="."), mycut=0.5);
|
|
|
403 write.table(tpara$profile, output_profile0, quote=F, row.names=F, col.names=F);
|
|
150
|
404 para = tpara$para;
|
|
|
405 para = apply(para, 1, function(x){paste(x, collapse=" ")});
|
|
166
|
406 para = c(readLines(paste(output_base_name, "tmp", "1", "para", sep="."), n=1), para);
|
|
150
|
407 writeLines(para, output_para0);
|
|
166
|
408 # Now run IDEAS based on the files produced during training.
|
|
|
409 base_cmd = get_post_training_base_cmd(base_cmd, para);
|
|
|
410 base_cmd = paste(base_cmd, "-otherpara", output_para0[[1]], output_profile0[[1]], sep=" ");
|
|
|
411 if (is.null(windows_by_chrom)) {
|
|
|
412 cmd = c(base_cmd, "-o", output_base_name);
|
|
174
|
413 cmd = add_output_redirect(cmd, output_log);
|
|
|
414 run_cmd(cmd, opt$save_ideas_log, output_log, opt$output_dir);
|
|
152
|
415 } else {
|
|
166
|
416 # Performing windows by chromosome.
|
|
|
417 if (length(windows_by_chrom) == 1) {
|
|
|
418 output_name = paste(output_base_name, i, sep=".");
|
|
|
419 cmd = c(base_cmd, "-o", output_name);
|
|
174
|
420 cmd = add_output_redirect(cmd, output_log);
|
|
|
421 run_cmd(cmd, opt$save_ideas_log, output_log, opt$output_dir);
|
|
166
|
422 } else {
|
|
|
423 for (i in 1:length(windows_by_chrom)) {
|
|
|
424 line = windows_by_chrom[i];
|
|
|
425 items = strsplit(line, " ")[[1]];
|
|
|
426 chrom = items[[1]];
|
|
|
427 window_start = items[[2]];
|
|
|
428 window_end = items[[3]];
|
|
|
429 cmd = paste(base_cmd, "-inv", window_start, window_end, sep=" ");
|
|
|
430 output_name = paste(output_base_name, chrom, sep=".");
|
|
|
431 cmd = paste(cmd, "-o", output_name, sep=" ");
|
|
174
|
432 cmd = add_output_redirect(cmd, output_log);
|
|
|
433 run_cmd(cmd, opt$save_ideas_log, output_log, opt$output_dir);
|
|
166
|
434 }
|
|
|
435 }
|
|
152
|
436 }
|
|
166
|
437 # Remove temporary outputs.
|
|
|
438 remove_files(path=".", pattern="tmp");
|
|
150
|
439 }
|
