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1 #!/usr/bin/env Rscript
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2
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3 suppressPackageStartupMessages(library("adegenet"))
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4 suppressPackageStartupMessages(library("ape"))
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5 suppressPackageStartupMessages(library("data.table"))
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6 #suppressPackageStartupMessages(library("dbplyr"))
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7 suppressPackageStartupMessages(library("dplyr"))
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8 suppressPackageStartupMessages(library("ggplot2"))
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9 suppressPackageStartupMessages(library("knitr"))
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10 suppressPackageStartupMessages(library("optparse"))
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11 suppressPackageStartupMessages(library("poppr"))
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12 suppressPackageStartupMessages(library("RColorBrewer"))
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13 suppressPackageStartupMessages(library("RPostgres"))
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14 #suppressPackageStartupMessages(library("tidyr"))
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15 suppressPackageStartupMessages(library("vcfR"))
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16 suppressPackageStartupMessages(library("vegan"))
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17
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18 option_list <- list(
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19 make_option(c("--database_connection_string"), action="store", dest="database_connection_string", help="Corals (stag) database connection string"),
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20 make_option(c("--input_affy_metadata"), action="store", dest="input_affy_metadata", help="Affymetrix 96 well plate input file"),
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21 make_option(c("--input_pop_info"), action="store", dest="input_pop_info", help="Population information input file"),
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22 make_option(c("--input_vcf"), action="store", dest="input_vcf", help="VCF input file"),
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23 make_option(c("--output_mlg_id"), action="store", dest="output_mlg_id", help="Mlg Id data output file"),
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24 make_option(c("--output_stag_db_report"), action="store", dest="output_stag_db_report", help="stag db report output file")
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25 )
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26
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27 parser <- OptionParser(usage="%prog [options] file", option_list=option_list);
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28 args <- parse_args(parser, positional_arguments=TRUE);
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29 opt <- args$options;
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30
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31 get_file_path = function(file_name) {
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32 file_path = paste("output_plots_dir", file_name, sep="/");
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33 return(file_path);
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34 }
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35
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36 # Read in VCF input file.
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37 vcf <- read.vcfR(opt$input_vcf);
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38
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39 #Missing GT in samples submitted
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40 gt <- extract.gt(vcf, element="GT", as.numeric=FALSE);
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41 missing_gt <- apply(gt, MARGIN=2, function(x){ sum(is.na(x))});
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42 missing_gt <- (missing_gt / nrow(vcf)) * 100;
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43 missing_gt_data_frame <- data.frame(missing_gt);
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44
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45 hets <- apply(gt, MARGIN=2, function(x) {sum(lengths(regmatches(x, gregexpr("0/1", x))))} );
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46 hets <- (hets / nrow(vcf)) * 100;
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47 ht <- data.frame(hets);
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48
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49 refA <- apply(gt, MARGIN=2, function(x) {sum(lengths(regmatches(x, gregexpr("0/0", x))))} );
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50 refA <- (refA / nrow(vcf)) * 100;
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51 rA <- data.frame(refA);
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52
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53 altB <- apply(gt, MARGIN=2, function(x) {sum(lengths(regmatches(x, gregexpr("1/1", x))))} );
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54 altB <- (altB / nrow(vcf)) * 100;
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55 aB <- data.frame(altB);
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56
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57 # Convert VCF file into a genind for the Poppr package.
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58 # TODO: probably should not hard-code 2 cores.
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59 gl <- vcfR2genlight(vcf, n.cores=2);
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60 genind <- new("genind", (as.matrix(gl)));
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61
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62 # Add population information to the genind object.
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63 poptab <- read.table(opt$input_pop_info, check.names=FALSE, header=T, na.strings=c("", "NA"));
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64 genind@pop <- as.factor(poptab$region);
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65
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66 # Convert genind object to a genclone object.
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67 gclo <- as.genclone(genind);
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68
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69 # Calculate the bitwise distance between individuals.
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70 xdis <- bitwise.dist(gclo);
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71
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72 # Multilocus genotypes (threshold of 1%).
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73 mlg.filter(gclo, distance=xdis) <- 0.01;
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74 m <- mlg.table(gclo, background=TRUE, color=TRUE);
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75
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76 # Create table of MLGs.
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77 id <- mlg.id(gclo);
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78 dt <- data.table(id, keep.rownames=TRUE);
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79 setnames(dt, c("id"), c("user_specimen_id"));
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80
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81 # Read user's Affymetrix 96 well plate csv file.
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82 pinfo <- read.csv(opt$input_affy_metadata, stringsAsFactors=FALSE);
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83 pinfo <- pinfo$user_specimen_id;
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84 pi <- data.table(pinfo);
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85 setnames(pi, c("pinfo"), c("user_specimen_id"));
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86
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87 # Instantiate database connection.
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88 # The connection string has this format:
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89 # postgresql://user:password@host/dbname
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90 conn_string <- opt$database_connection_string;
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91 conn_items <- strsplit(conn_string, "://")[[1]];
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92 string_needed <- conn_items[1];
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93 items_needed <- strsplit(string_needed, "@")[[1]];
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94 user_pass_string <- items_needed[1];
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95 host_dbname_string <- items_needed[2];
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96 user_pass_items <- strsplit(user_pass_string, ":")[[1]];
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97 host_dbname_items <- strsplit(host_dbname_string, "/")[[1]];
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98 user <- user_pass_items[1];
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99 pass <- user_pass_items[2];
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100 host <- host_dbname_items[1];
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101 dbname <- host_dbname_items[2];
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102 # FIXME: is there a way to not hard-code the port?
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103 conn <- DBI::dbConnect(RPostgres::Postgres(), host=host, port='5432', dbname=dbname, user=user, password=pass);
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104
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105 # Import the sample table.
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106 sample_table <- tbl(conn, "sample");
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107
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108 # Select user_specimen_id and mlg columns.
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109 smlg <- sample_table %>% select(user_specimen_id, coral_mlg_clonal_id, symbio_mlg_clonal_id);
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110
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111 # Convert to dataframe.
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112 sm <- data.frame(smlg);
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113 sm[sm==""] <- NA;
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114
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115 # Convert missing data into data table.
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116 mi <-setDT(missing_gt_data_frame, keep.rownames=TRUE)[];
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117 # Change names to match db.
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118 setnames(mi, c("rn"), c("user_specimen_id"));
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119 setnames(mi, c("myMiss"), c("percent_missing_data_coral"));
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120 # Round missing data to two digits.
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121 mi$percent_missing <- round(mi$percent_missing, digits=2);
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122
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123 # Convert heterozygosity data into data table.
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124 ht <-setDT(ht, keep.rownames=TRUE)[];
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125 # Change names to match db.
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126 setnames(ht, c("rn"), c("user_specimen_id"));
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127 setnames(ht, c("hets"), c("percent_mixed_coral"));
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128 # Round missing data to two digits.
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129 ht$percent_mixed<-round(ht$percent_mixed, digits=2);
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130
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131 # Convert refA data into data.table.
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132 rA <-setDT(rA, keep.rownames=TRUE)[];
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133 # Change names to match db.
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134 setnames(rA, c("rn"), c("user_specimen_id"));
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135 setnames(rA, c("refA"), c("percent_reference_coral"));
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136 # round missing data to two digits.
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137 rA$percent_reference<-round(rA$percent_reference, digits=2);
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138
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139 # Convert altB data into data table.
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140 aB <-setDT(aB, keep.rownames=TRUE)[];
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141 # Change names to match db.
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142 setnames(aB, c("rn"), c("user_specimen_id"));
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143 setnames(aB, c("altB"), c("percent_alternative_coral"));
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144 # Round missing data to two digits.
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145 aB$percent_alternative<-round(aB$percent_alternative, digits=2);
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146
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147 #convert mlg id to data.table format
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148 dt <- data.table(id, keep.rownames=TRUE);
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149 # Change name to match db.
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150 setnames(dt, c("id"), c("user_specimen_id"));
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151
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152 # Transform.
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153 df3 <- dt %>%
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154 group_by(row_number()) %>%
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155 dplyr::rename(group='row_number()') %>%
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156 unnest (user_specimen_id) %>%
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157 # Join with mlg table.
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158 left_join(sm %>%
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159 select("user_specimen_id","coral_mlg_clonal_id"), by='user_specimen_id');
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160
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161 # If found in database, group members on previous mlg id.
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162 uniques <- unique(df3[c("group", "coral_mlg_clonal_id")]);
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163 uniques <- uniques[!is.na(uniques$coral_mlg_clonal_id),];
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164 na.mlg <- which(is.na(df3$coral_mlg_clonal_id));
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165 na.group <- df3$group[na.mlg];
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166 df3$coral_mlg_clonal_id[na.mlg] <- uniques$coral_mlg_clonal_id[match(na.group, uniques$group)];
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167
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168 # Determine if the sample mlg matched previous genotyped sample.
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169 df4<- df3 %>%
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170 group_by(group) %>%
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171 mutate(DB_match = ifelse(is.na(coral_mlg_clonal_id),"no_match","match"));
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172
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173 # Create new mlg id for samples that did not match those in the database.
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174 none <- unique(df4[c("group", "coral_mlg_clonal_id")]);
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175 none <- none[is.na(none$coral_mlg_clonal_id),];
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176 na.mlg2 <- which(is.na(df4$coral_mlg_clonal_id));
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177 n.g <- df4$group[na.mlg2];
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178 ct <- length(unique(n.g));
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179
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180 # List of new group ids, the sequence starts at the number of
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181 # ids present in df4$coral_mlg_clonal_ids plus 1. Not sure if
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182 # the df4 file contains all ids. If it doesn't then look below
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183 # to change the seq() function.
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184 n.g_ids <- sprintf("HG%04d", seq((sum(!is.na(unique(df4["coral_mlg_clonal_id"]))) + 1), by=1, length=ct));
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185 # This is a key for pairing group with new ids.
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186 rat <- cbind(unique(n.g), n.g_ids);
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187 # this for loop assigns the new id iteratively for all that have NA.
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188 for (i in 1:length(na.mlg2)) {
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189 df4$coral_mlg_clonal_id[na.mlg2[i]] <- n.g_ids[match(df4$group[na.mlg2[i]], unique(n.g))];
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190 }
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191
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192 # Merge data frames for final table.
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193 report_user <- pi %>%
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194 # Join with the second file (only the first and third column).
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195 left_join(df4 %>%
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196 select("user_specimen_id","coral_mlg_clonal_id","DB_match"),
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197 by='user_specimen_id') %>%
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198 # Join with the second file (only the first and third column).
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199 left_join(mi %>%
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200 select("user_specimen_id","percent_missing_coral"),
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201 by='user_specimen_id') %>%
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202 # Join with the second file (only the first and third column).
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203 left_join(ht %>%
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204 select("user_specimen_id","percent_mixed_coral"),
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205 by='user_specimen_id') %>%
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206 # Join with the second file (only the first and third column);
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207 left_join(rA %>%
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208 select("user_specimen_id","percent_reference_coral"),
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209 by='user_specimen_id') %>%
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210 # Join with the second file (only the first and third column).
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211 left_join(aB %>%
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212 select("user_specimen_id","percent_alternative_coral"),
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213 by='user_specimen_id') %>%
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214 mutate(DB_match = ifelse(is.na(DB_match), "failed", DB_match))%>%
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215 mutate(coral_mlg_clonal_id=ifelse(is.na(coral_mlg_clonal_id), "failed", coral_mlg_clonal_id))%>%
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216 ungroup() %>%
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217 select(-group);
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218
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219 write.csv(report_user, file=paste(opt$output_stag_db_report), quote=FALSE);
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220
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221 # Rarifaction curve.
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222 # Start PDF device driver.
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223 dev.new(width=10, height=7);
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224 file_path = get_file_path("geno_rarifaction_curve.pdf");
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225 pdf(file=file_path, width=10, height=7);
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226 rarecurve(m, ylab="Number of expected MLGs", sample=min(rowSums(m)), border=NA, fill=NA, font=2, cex=1, col="blue");
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227 dev.off();
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228
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229 # Genotype accumulation curve, sample is number of
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230 # loci randomly selected for to make the curve.
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231 dev.new(width=10, height=7);
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232 file_path = get_file_path("geno_accumulation_curve.pdf");
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233 pdf(file=file_path, width=10, height=7);
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234 genotype_curve(gind, sample=5, quiet=TRUE);
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235 dev.off();
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236
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237 # Create a phylogeny of samples based on distance matrices.
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238 cols <- palette(brewer.pal(n=12, name='Set3'));
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239 set.seed(999);
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240 # Start PDF device driver.
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241 dev.new(width=10, height=7);
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242 file_path = get_file_path("nj_phylogeny.pdf");
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243 pdf(file=file_path, width=10, height=7);
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244 # Organize branches by clade.
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245 tree <- gclo %>%
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246 aboot(dist=provesti.dist, sample=10, tree="nj", cutoff=50, quiet=TRUE) %>%
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247 ladderize();
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248 plot.phylo(tree, tip.color=cols[obj2$pop],label.offset=0.0125, cex=0.7, font=2, lwd=4);
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249 # Add a scale bar showing 5% difference..
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250 add.scale.bar(length=0.05, cex=0.65);
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251 nodelabels(tree$node.label, cex=.5, adj=c(1.5, -0.1), frame="n", font=3, xpd=TRUE);
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252 dev.off();
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253
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