Mercurial > repos > petr-novak > dante_ltr

annotate do_not_track_info/te_analysis.R @ 7:c33d6583e548 draft

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"planemo upload commit 50884f7f0269a0bbde078f24fe5020975693bcd9"
author petr-novak
date Fri, 24 Jun 2022 14:19:48 +0000
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7
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1 #!/usr/bin/env Rscript
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2 library(ggplot2)
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3 lf <- read.table("/mnt/raid/users/petr/workspace/dante_ltr/do_not_track_info/ltr_finder_13100_2018_144_MOESM1_ESM.csv",
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4 as.is=TRUE, header=TRUE, sep="\t", quote = "")
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5 cls <- read.table("/mnt/raid/users/petr/workspace/dante_ltr/do_not_track_info/classification__domain_features_13100_2018_144_MOESM1_ESM.csv",
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6 as.is=TRUE, header=TRUE, sep="\t", quote='', comment.char = "")
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7
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8 # add classification to ltr
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9 rexdb <- cls$Classification..new03.
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10 names(rexdb) <- cls$REXdb.name
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12
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13 lf$classification <- cls$Classification..new03.[match(lf$REXdb.name, cls$REXdb.name)]
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18 length_quantiles = sapply(split(lf$size..including.TSD., lf$classification), quantile, seq(0,1,by=0.025))
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19 length95min = length_quantiles[2,]
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20 length95max = length_quantiles[40,]
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21
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22 ltr_length_quantiles = sapply(split(lf$X3.LTR.size, lf$classification), quantile, seq(0,1,by=0.025))
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23 ltr_length95min = ltr_length_quantiles[2,]
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24 ltr_length95max = ltr_length_quantiles[40,]
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25
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26 write.table(length_quantiles, file = "/mnt/raid/users/petr/workspace/dante_ltr/do_not_track_info/TE_length_quantiles.csv", sep="\t")
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27 write.table(ltr_length_quantiles, file = "/mnt/raid/users/petr/workspace/dante_ltr/do_not_track_info/LTR_length_quantiles.csv", sep="\t")
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28
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29 library(rtracklayer)
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30 d = import("/mnt/raid/users/petr/workspace/dante_ltr/do_not_track_info/dante_on_rexdb.gff3")
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31
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32 # some elements have duplicated domains - remove it
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33 dom_dup = sapply(split(d$Name, seqnames(d)), function (x)any(duplicated(x)))
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34 d2 = d[seqnames(d) %in% names(dom_dup)[!dom_dup]]
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35 dparts = split(d2, d2$Final_Classification)
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36
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37 dparts_rex = sapply(dparts, function(x) split(x, seqnames(x), drop = TRUE))
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38
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39
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40 get_dom_pos <- function(g){
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41 if (length(g) == 0){
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42 return(NULL)
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43 }
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44 gdf <- data.frame(rexdb = as.character(seqnames(g)),
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45 domain = g$Name,
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46 S = start(g),
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47 E = end(g),
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48 stringsAsFactors = FALSE)
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49 gSmat <- xtabs(S ~ rexdb + domain, data = gdf)
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50 colnames(gSmat) <- paste0(colnames(gSmat),"_S")
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51 gEmat <- xtabs(E ~ rexdb + domain, data = gdf)
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52 colnames(gEmat) <- paste0(colnames(gEmat),"_E")
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53 SEmat <- cbind(gSmat,gEmat)
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54 # reorder
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55 dom_position <- colMeans(SEmat)
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56 SEmat_sorted <- SEmat[,order(dom_position)]
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57 return(SEmat_sorted)
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58 }
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60
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61 domains_SE = lapply(dparts, get_dom_pos)
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62
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63 # add ltr5_E, ltr5_S, ..
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64 LSE = list()
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65 for (i in seq_along(domains_SE)){
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66 ltr3 = lf$X3.LTR.position[match(rownames(domains_SE[[i]]), lf$REXdb.name)]
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67 ltr5 = lf$X5.LTR..position[match(rownames(domains_SE[[i]]), lf$REXdb.name)]
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68 if (all(is.na(ltr3))){
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69 # not ltr retrotransposon
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70 LSE[[i]] = domains_SE[[i]]
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71 next
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72 }
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73 ltr5_S = sapply(strsplit(ltr5, "-"), function(x) if(is.na(x[[1]])){NA} else{as.numeric(x[1])})
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74 ltr5_E = sapply(strsplit(ltr5, "-"), function(x) if(is.na(x[[1]])){NA} else{as.numeric(x[2])})
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75 ltr3_S = sapply(strsplit(ltr3, "-"), function(x) if(is.na(x[[1]])){NA} else{as.numeric(x[1])})
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76 ltr3_E = sapply(strsplit(ltr3, "-"), function(x) if(is.na(x[[1]])){NA} else{as.numeric(x[2])})
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77 LSE[[i]] = cbind(ltr5_S, ltr5_E, domains_SE[[i]], ltr3_S, ltr3_E)
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78 }
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79
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80 names(LSE) = names(domains_SE)
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81 newN = function(a,b){
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82 ap = gsub("_.+","", a)
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83 bp = gsub("_.+","", b)
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84 ifelse(ap == bp, ap, paste0(ap,"_", bp))
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85 }
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86
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87 # positions to distances:
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88 LSED = lapply(LSE, function(x){
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89 x[x==0]=NA
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90 N = ncol(x)
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91 if (is.null(N)){
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92 return(NULL)
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93 }
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94 NM2 = colnames(x[,2:N])
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95 NM1 = colnames(x[,1:(N - 1)])
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96 d = x[,2:N, drop = FALSE] - x[,1:(N-1), drop=FALSE]
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97 colnames(d) = newN(NM1, NM2)
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98 return(d)
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99 }
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100 )
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101
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102
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103 # create empirical cumulative distribution function
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104 get_ecdf <- function(dd, s = 1) {
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105 ecdf_list = list()
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106 for (i1 in colnames(dd)){
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107 for (i2 in colnames(dd)){
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108 if (i1 == i2){
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109 next
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110 }else{
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111 ii = sort(c(i1,i2))
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112 ecdf_list[[ii[1]]][[ii[2]]]=ecdf(s * abs(dd[,i2]-dd[,i1]))
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113
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114 }
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115 }
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116 }
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117 return(ecdf_list)
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118 }
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119
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120
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121 ecdf_list = lapply(LSE, get_ecdf)
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122 ecdf_list_m = lapply(LSE, get_ecdf, -1)
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123
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124 ecdf_model = list(plus = ecdf_list, minus = ecdf_list_m)
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125
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126 saveRDS(ecdf_model, "databases/feature_distances_model.RDS")
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127
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128 dante2dist <- function(dc){
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129 # dc - cluster of domains, granges
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130 dpos <- get_dom_pos(dc)
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131 D <- c(dist(dpos))
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132 NN <- combn(names(dpos), 2)
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133 # order feature in pair alphabeticaly
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134 N1 <- ifelse(NN[1,]<NN[2,], NN[1,], NN[2, ])
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135 N2 <- ifelse(NN[1,]>NN[2,], NN[1,], NN[2, ])
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136 dfd <- data.frame(F1 = N1, F2 = N2, distance = D)
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137 }
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138
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139
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140
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141 dante_to_quantiles <- function(dc, model_function, lineage=NULL){
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142 dfd <- dante2dist(dc)
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143 if (is.null(lineage)){
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144 lineage <- dc$Final_Classification[1]
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145 }
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146 fn <- model_function$plus[[lineage]]
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147 fnm <- model_function$minus[[lineage]]
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148 dstat1 <- mapply(mapply(dfd$F1, dfd$F2, FUN=function(a, b)fn[[a]][[b]]), dfd$distance, FUN=function(f, x)f(x))
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149 dstat2 <- mapply(mapply(dfd$F1, dfd$F2, FUN=function(a, b)fnm[[a]][[b]]), dfd$distance, FUN=function(f, x)f(-x))
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150 dout <- cbind(dfd, dstat1, dstat2, dstat12 = ifelse(dstat1>dstat2, dstat2, dstat1))
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151 }
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152
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153
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154
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155 q=dante_to_quantiles(dparts_rex[[29]][[500]], ecdf_model)
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156
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157 qq = lapply(dparts_rex[[29]], dante_to_quantiles, ecdf_model, lineage = dparts_rex[[43]][[1]]$Final_Classification[1])
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158 qq = lapply(dparts_rex[[29]], dante_to_quantiles, ecdf_model, lineage = dparts_rex[[29]][[1]]$Final_Classification[1])
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159
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160 ll = sapply(qq, function(x)(sum(x$dstat12<0.01))/nrow(x))
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161 lln = sapply(qq, function(x)(sum(x$dstat12<0.01)))
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162
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163
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164 table(ll > 0.1) # more that 10% dist are outliers
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165
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166 table(ll)
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167 table(lln)
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168 lll = sapply(ll, min)
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169 hist(lll, breaks= 500)
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170 dc = dparts_rex[[30]][[30]]
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171
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172 print(min(dout[4:5]))
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173 plot(dout[4:5], xlim=c(0,1), ylim=c(0,1))
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174
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175 hist(dout[,6], breaks=50, xlim=c(0,1))
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176 mtext(min(dout[,6]))
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177
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178 apply(LSED[[30]],2, median, na.rm = TRUE)