Mercurial > repos > greg > coral_multilocus_genotype

comparison coral_multilocus_genotype.R @ 0:adaf89535d2e draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
Uploaded
author greg
date Thu, 15 Aug 2019 10:02:15 -0400
parents
children a690e0382ce4
comparison
equal deleted inserted replaced
-1:000000000000 0:adaf89535d2e
1 #!/usr/bin/env Rscript
2
3 suppressPackageStartupMessages(library("adegenet"))
4 suppressPackageStartupMessages(library("ape"))
5 suppressPackageStartupMessages(library("data.table"))
6 suppressPackageStartupMessages(library("dbplyr"))
7 suppressPackageStartupMessages(library("dplyr"))
8 suppressPackageStartupMessages(library("ggplot2"))
9 suppressPackageStartupMessages(library("knitr"))
10 suppressPackageStartupMessages(library("maps"))
11 suppressPackageStartupMessages(library("mapproj"))
12 suppressPackageStartupMessages(library("optparse"))
13 suppressPackageStartupMessages(library("poppr"))
14 suppressPackageStartupMessages(library("RColorBrewer"))
15 suppressPackageStartupMessages(library("RPostgres"))
16 suppressPackageStartupMessages(library("SNPRelate"))
17 suppressPackageStartupMessages(library("tidyr"))
18 suppressPackageStartupMessages(library("vcfR"))
19 suppressPackageStartupMessages(library("vegan"))
20 suppressPackageStartupMessages(library("yarrr"))
21 theme_set(theme_bw())
22
23 DEFAULT_MISSING_NUMERIC_VALUE <- -9.000000;
24
25 option_list <- list(
26 make_option(c("--database_connection_string"), action="store", dest="database_connection_string", help="Corals (stag) database connection string"),
27 make_option(c("--input_affy_metadata"), action="store", dest="input_affy_metadata", help="Affymetrix 96 well plate input file"),
28 make_option(c("--input_pop_info"), action="store", dest="input_pop_info", help="Population information input file"),
29 make_option(c("--input_vcf"), action="store", dest="input_vcf", help="VCF input file"),
30 make_option(c("--output_nj_phylogeny_tree"), action="store", dest="output_nj_phylogeny_tree", default=NULL, help="Flag to plot neighbor-joining phylogeny tree"),
31 make_option(c("--output_stag_db_report"), action="store", dest="output_stag_db_report", help="Flag to output stag db report file")
32 )
33
34 parser <- OptionParser(usage="%prog [options] file", option_list=option_list);
35 args <- parse_args(parser, positional_arguments=TRUE);
36 opt <- args$options;
37
38 get_file_path = function(dir, file_name) {
39 file_path = paste(dir, file_name, sep="/");
40 return(file_path);
41 }
42
43 get_database_connection <- function(db_conn_string) {
44 # Instantiate database connection.
45 # The connection string has this format:
46 # postgresql://user:password@host/dbname
47 conn_items <- strsplit(db_conn_string, "://")[[1]];
48 string_needed <- conn_items[2];
49 items_needed <- strsplit(string_needed, "@")[[1]];
50 user_pass_string <- items_needed[1];
51 host_dbname_string <- items_needed[2];
52 user_pass_items <- strsplit(user_pass_string, ":")[[1]];
53 host_dbname_items <- strsplit(host_dbname_string, "/")[[1]];
54 user <- user_pass_items[1];
55 pass <- user_pass_items[2];
56 host <- host_dbname_items[1];
57 dbname <- host_dbname_items[2];
58 conn <- DBI::dbConnect(RPostgres::Postgres(), host=host, port="5432", dbname=dbname, user=user, password=pass);
59 return (conn);
60 }
61
62 time_elapsed <- function(start_time) {
63 cat("Elapsed time: ", proc.time() - start_time, "\n\n");
64 }
65
66 time_start <- function(msg) {
67 start_time <- proc.time();
68 cat(msg, "...\n");
69 return(start_time);
70 }
71
72 write_data_frame <- function(dir, file_name, data_frame) {
73 cat("\nWriting file: ", file_name, "\n");
74 file_path <- get_file_path(dir, file_name);
75 write.table(data_frame, file=file_path, quote=FALSE, row.names=FALSE, sep="\t");
76 }
77
78 # Prepare for processing.
79 output_data_dir = "output_data_dir";
80 output_plots_dir = "output_plots_dir";
81 # Read in VCF input file.
82 start_time <- time_start("Reading VCF input");
83 vcf <- read.vcfR(opt$input_vcf);
84 time_elapsed(start_time);
85
86 # Convert VCF file into a genind for the Poppr package.
87 start_time <- time_start("Converting VCF data to a genind object");
88 genind_obj <- vcfR2genind(vcf);
89 time_elapsed(start_time);
90
91 # Add population information to the genind object.
92 population_info_data_table <- read.table(opt$input_pop_info, check.names=FALSE, header=F, na.strings=c("", "NA"), stringsAsFactors=FALSE, sep="\t", quote="");
93 colnames(population_info_data_table) <- c("row_id", "affy_id", "user_specimen_id", "region");
94 #write_data_frame(output_data_dir, "population_info_data_table", population_info_data_table);
95 genind_obj@pop <- as.factor(population_info_data_table$region);
96 strata(genind_obj) <- data.frame(pop(genind_obj));
97
98 # Convert genind object to a genclone object.
99 start_time <- time_start("Converting the genind object to a genclone object");
100 genind_clone <- as.genclone(genind_obj);
101 time_elapsed(start_time);
102
103 # Calculate the bitwise distance between individuals.
104 start_time <- time_start("Calculating the bitwise distance between individuals");
105 bitwise_distance <- bitwise.dist(genind_clone);
106 time_elapsed(start_time);
107
108 # Multilocus genotypes (threshold of 3.2%).
109 mlg.filter(genind_clone, distance=bitwise_distance) <- 0.032;
110 m <- mlg.table(genind_clone, background=TRUE, color=TRUE);
111
112 # Create list of MLGs.
113 mlg_ids <- mlg.id(genind_clone);
114
115 # Read user's Affymetrix 96 well plate tabular file.
116 affy_metadata_data_frame <- read.table(opt$input_affy_metadata, header=FALSE, stringsAsFactors=FALSE, sep="\t", na.strings=c("", "NA"), quote="");
117 colnames(affy_metadata_data_frame) <- c("user_specimen_id", "field_call", "bcoral_genet_id", "bsym_genet_id", "reef",
118 "region", "latitude", "longitude", "geographic_origin", "colony_location",
119 "depth", "disease_resist", "bleach_resist", "mortality","tle",
120 "spawning", "collector_last_name", "collector_first_name", "organization", "collection_date",
121 "email", "seq_facility", "array_version", "public", "public_after_date",
122 "sperm_motility", "healing_time", "dna_extraction_method", "dna_concentration", "registry_id",
123 "result_folder_name", "plate_barcode");
124 affy_metadata_data_frame$user_specimen_id <- as.character(affy_metadata_data_frame$user_specimen_id);
125 user_specimen_ids <- as.character(affy_metadata_data_frame$user_specimen_id);
126 # The specimen_id_field_call_data_table looks like this:
127 # user_specimen_ids V2
128 # 1090 prolifera
129 # 1091 prolifera
130 specimen_id_field_call_data_table <- data.table(user_specimen_ids, affy_metadata_data_frame$field_call);
131 # Rename the user_specimen_ids column.
132 setnames(specimen_id_field_call_data_table, c("user_specimen_ids"), c("user_specimen_id"));
133 # Rename the V2 column.
134 setnames(specimen_id_field_call_data_table, c("V2"), c("field_call"));
135
136 # Connect to database.
137 conn <- get_database_connection(opt$database_connection_string);
138 # Import the sample table.
139 sample_table <- tbl(conn, "sample");
140 # Import the genotype table.
141 genotype_table <- tbl(conn, "genotype");
142 # Select columns from the sample table and the
143 # genotype table joined by genotype_id.
144 sample_table_columns <- sample_table %>% select(user_specimen_id, affy_id, bcoral_genet_id, genotype_id);
145 smlg <- sample_table_columns %>%
146 left_join(genotype_table %>%
147 select("id", "coral_mlg_clonal_id", "coral_mlg_rep_sample_id", "genetic_coral_species_call"),
148 by=c("genotype_id"="id"));
149 # Name the columns.
150 smlg_data_frame <- as.data.frame(smlg);
151 colnames(smlg_data_frame) <- c("user_specimen_id", "affy_id", "bcoral_genet_id", "genotype_id",
152 "coral_mlg_clonal_id", "coral_mlg_rep_sample_id", "genetic_coral_species_call");
153 # Missing GT in samples submitted.
154 start_time <- time_start("Discovering missing GT in samples");
155 gt <- extract.gt(vcf, element="GT", as.numeric=FALSE);
156 missing_gt <- apply(gt, MARGIN=2, function(x){ sum(is.na(x))});
157 missing_gt <- (missing_gt / nrow(vcf)) * 100;
158 missing_gt_data_frame <- data.frame(missing_gt);
159 # The specimen_id_field_call_data_table looks like this:
160 # rn missing_gt
161 # a100000-4368120-060520-256_I07.CEL 0.06092608
162 # a100000-4368120-060520-256_K07.CEL 0.05077173
163 missing_gt_data_table <-setDT(missing_gt_data_frame, keep.rownames=TRUE)[];
164 # Rename the rn column.
165 setnames(missing_gt_data_table, c("rn"), c("affy_id"));
166 # Rename the missing_gt column.
167 setnames(missing_gt_data_table, c("missing_gt"), c("percent_missing_data_coral"));
168 # Round data to two digits.
169 missing_gt_data_table$percent_missing_data_coral <- round(missing_gt_data_table$percent_missing_data_coral, digits=2);
170 time_elapsed(start_time);
171
172 # Heterozygous alleles.
173 start_time <- time_start("Discovering heterozygous alleles");
174 heterozygous_alleles <- apply(gt, MARGIN=2, function(x) {sum(lengths(regmatches(x, gregexpr("0/1", x))))});
175 heterozygous_alleles <- (heterozygous_alleles / nrow(vcf)) * 100;
176 heterozygous_alleles_data_frame <- data.frame(heterozygous_alleles);
177 # The heterozygous_alleles_data_table looks like this:
178 # rn heterozygous_alleles
179 # a100000-4368120-060520-256_I07.CEL 73.94903
180 # a100000-4368120-060520-256_K07.CEL 74.40089
181 heterozygous_alleles_data_table <- setDT(heterozygous_alleles_data_frame, keep.rownames=TRUE)[];
182 # Rename the rn column.
183 setnames(heterozygous_alleles_data_table, c("rn"), c("affy_id"));
184 # Rename the heterozygous_alleles column.
185 setnames(heterozygous_alleles_data_table, c("heterozygous_alleles"), c("percent_heterozygous_coral"));
186 # Round data to two digits.
187 heterozygous_alleles_data_table$percent_heterozygous_coral <- round(heterozygous_alleles_data_table$percent_heterozygous_coral, digits=2);
188 time_elapsed(start_time);
189
190 # Reference alleles.
191 start_time <- time_start("Discovering reference alleles");
192 reference_alleles <- apply(gt, MARGIN=2, function(x) {sum(lengths(regmatches(x, gregexpr("0/0", x))))});
193 reference_alleles <- (reference_alleles / nrow(vcf)) * 100;
194 reference_alleles_data_frame <- data.frame(reference_alleles);
195 # The reference_alleles_data_table looks like this:
196 # rn reference_alleles
197 # a100000-4368120-060520-256_I07.CEL 11.60642
198 # a100000-4368120-060520-256_K07.CEL 11.45918
199 reference_alleles_data_table <- setDT(reference_alleles_data_frame, keep.rownames=TRUE)[];
200 # Rename the rn column.
201 setnames(reference_alleles_data_table, c("rn"), c("affy_id"));
202 # Rename the reference_alleles column.
203 setnames(reference_alleles_data_table, c("reference_alleles"), c("percent_reference_coral"));
204 # Round data to two digits.
205 reference_alleles_data_table$percent_reference_coral <- round(reference_alleles_data_table$percent_reference_coral, digits=2);
206 time_elapsed(start_time);
207
208 # Alternative alleles
209 start_time <- time_start("Discovering alternative alleles");
210 alternative_alleles <- apply(gt, MARGIN=2, function(x) {sum(lengths(regmatches(x, gregexpr("1/1", x))))});
211 alternative_alleles <- (alternative_alleles / nrow(vcf)) * 100;
212 alternative_alleles_data_frame <- data.frame(alternative_alleles);
213 # The alternative_alleles_data_table looks like this:
214 # rn alternative_alleles
215 # a100000-4368120-060520-256_I07.CEL 14.38363
216 # a100000-4368120-060520-256_K07.CEL 14.08916
217 alternative_alleles_data_table <- setDT(alternative_alleles_data_frame, keep.rownames=TRUE)[];
218 # Rename the rn column.
219 setnames(alternative_alleles_data_table, c("rn"), c("affy_id"));
220 # Rename the alternative_alleles column.
221 setnames(alternative_alleles_data_table, c("alternative_alleles"), c("percent_alternative_coral"));
222 # Round data to two digits.
223 alternative_alleles_data_table$percent_alternative_coral <- round(alternative_alleles_data_table$percent_alternative_coral, digits=2);
224 time_elapsed(start_time);
225
226 # The mlg_ids_data_table looks like this:
227 # mlg_ids
228 # a550962-4368120-060520-500_M23.CEL
229 # a550962-4368120-060520-256_A19.CEL
230 mlg_ids_data_table <- data.table(mlg_ids, keep.rownames=TRUE);
231 # Rename the mlg_ids column.
232 setnames(mlg_ids_data_table, c("mlg_ids"), c("affy_id"));
233
234 # sample_mlg_tibble looks like this:
235 # A tibble: 262 x 3
236 # Groups: group [?]
237 # group affy_id coral_mlg_clonal_id coral_mlg_rep_sample_id
238 # <int> <chr> <chr> <chr>
239 # 1 a550962-4368.CEL NA 13905
240 sample_mlg_tibble <- mlg_ids_data_table %>%
241 group_by(row_number()) %>%
242 dplyr::rename(group="row_number()") %>%
243 unnest (affy_id) %>%
244 # Join with mlg table.
245 left_join(smlg_data_frame %>%
246 select("affy_id","coral_mlg_clonal_id", "coral_mlg_rep_sample_id"),
247 by="affy_id");
248
249 # If found in database, group members on previous mlg id.
250 uniques <- unique(sample_mlg_tibble[c("group", "coral_mlg_clonal_id")]);
251 uniques <- uniques[!is.na(uniques$coral_mlg_clonal_id),];
252 na.mlg <- which(is.na(sample_mlg_tibble$coral_mlg_clonal_id));
253 na.group <- sample_mlg_tibble$group[na.mlg];
254 sample_mlg_tibble$coral_mlg_clonal_id[na.mlg] <- uniques$coral_mlg_clonal_id[match(na.group, uniques$group)];
255
256 # Find out if the sample mlg matched a previous genotyped sample.
257 # sample_mlg_match_tibble looks like this:
258 # A tibble: 262 x 4
259 # Groups: group [230]
260 # group affy_id coral_mlg_clonal_id db_match
261 # <int> <chr> <chr> <chr>
262 # 1 a550962-436.CEL NA no_match
263 sample_mlg_match_tibble <- sample_mlg_tibble %>%
264 group_by(group) %>%
265 mutate(db_match = ifelse(is.na(coral_mlg_clonal_id), "no_match", "match"));
266
267 # Create new mlg id for samples with no matches in the database.
268 none <- unique(sample_mlg_match_tibble[c("group", "coral_mlg_clonal_id")]);
269 none <- none[is.na(none$coral_mlg_clonal_id),];
270 na.mlg2 <- which(is.na(sample_mlg_match_tibble$coral_mlg_clonal_id));
271 n.g <- sample_mlg_match_tibble$group[na.mlg2];
272 ct <- length(unique(n.g));
273
274 # List of new group ids, the sequence starts at the number of
275 # ids present in sample_mlg_match_tibble$coral_mlg_clonal_ids
276 # plus 1.
277 n.g_ids <- sprintf("HG%04d", seq((sum(!is.na(unique(sample_mlg_match_tibble["coral_mlg_clonal_id"]))) + 1), by=1, length=ct));
278
279 # Assign the new id iteratively for all that have NA.
280 for (i in 1:length(na.mlg2)) {
281 sample_mlg_match_tibble$coral_mlg_clonal_id[na.mlg2[i]] <- n.g_ids[match(sample_mlg_match_tibble$group[na.mlg2[i]], unique(n.g))];
282 }
283
284 # Subset population_info_data_table for all samples.
285 # affy_id_user_specimen_id_vector looks like this:
286 # affy_id user_specimen_id
287 # a100000-432.CEL 13704
288 affy_id_user_specimen_id_vector <- population_info_data_table[c(2, 3)];
289
290 # Merge data frames for final table.
291 start_time <- time_start("Merging data frames");
292 stag_db_report <- specimen_id_field_call_data_table %>%
293 left_join(affy_id_user_specimen_id_vector %>%
294 select("affy_id", "user_specimen_id"),
295 by="user_specimen_id") %>%
296 mutate(db_record = ifelse(affy_id %in% smlg_data_frame$affy_id, "genotyped", "new")) %>%
297 filter(db_record=="new") %>%
298 left_join(sample_mlg_match_tibble %>%
299 select("affy_id", "coral_mlg_clonal_id", "db_match"),
300 by="affy_id") %>%
301 left_join(missing_gt_data_table %>%
302 select("affy_id", "percent_missing_data_coral"),
303 by="affy_id") %>%
304 left_join(heterozygous_alleles_data_table %>%
305 select("affy_id", "percent_heterozygous_coral"),
306 by="affy_id") %>%
307 left_join(reference_alleles_data_table %>%
308 select("affy_id", "percent_reference_coral"),
309 by="affy_id") %>%
310 left_join(alternative_alleles_data_table %>%
311 select("affy_id", "percent_alternative_coral"),
312 by="affy_id") %>%
313 mutate(db_match = ifelse(is.na(db_match), "failed", db_match))%>%
314 mutate(coral_mlg_clonal_id = ifelse(is.na(coral_mlg_clonal_id), "failed", coral_mlg_clonal_id)) %>%
315 mutate(genetic_coral_species_call = ifelse(percent_alternative_coral >= 40 & percent_alternative_coral <= 44.99, "A.palmata","other")) %>%
316 mutate(genetic_coral_species_call = ifelse(percent_alternative_coral >= 45 & percent_alternative_coral <= 51, "A.cervicornis", genetic_coral_species_call)) %>%
317 mutate(genetic_coral_species_call = ifelse(percent_heterozygous_coral > 40, "A.prolifera", genetic_coral_species_call)) %>%
318 ungroup() %>%
319 select(-group,-db_record);
320 time_elapsed(start_time);
321
322 start_time <- time_start("Writing csv output");
323 write.csv(stag_db_report, file=opt$output_stag_db_report, quote=FALSE);
324 time_elapsed(start_time);
325
326 # Representative clone for genotype table.
327 start_time <- time_start("Creating representative clone for genotype table");
328 no_dup_genotypes_genind <- clonecorrect(genind_clone, strata = ~pop.genind_obj.);
329 id_rep <- mlg.id(no_dup_genotypes_genind);
330 id_data_table <- data.table(id_rep, keep.rownames=TRUE);
331 # Rename the id_rep column.
332 setnames(id_data_table, c("id_rep"), c("affy_id"));
333 time_elapsed(start_time);
334
335 # Table of alleles for the new samples subset to new plate data.
336 # Create vector indicating number of individuals desired from
337 # affy_id column of stag_db_report data table.
338 i <- ifelse(is.na(stag_db_report[3]), "", stag_db_report[[3]]);
339 i <- i[!apply(i== "", 1, all),];
340
341 # Subset VCF to the user samples.
342 start_time <- time_start("Subsetting vcf to the user samples");
343 l <- length(i)+1;
344 #n <- ncol(vcf@gt);
345 #s <- n - l;
346 svcf <- vcf[, 1:l];
347 write.vcf(svcf, "subset.vcf.gz");
348 vcf.fn <- "subset.vcf.gz";
349 snpgdsVCF2GDS(vcf.fn, "test3.gds", method="biallelic.only");
350 genofile <- snpgdsOpen(filename="test3.gds", readonly=FALSE);
351 gds_array <- read.gdsn(index.gdsn(genofile, "sample.id"));
352 # gds_array looks like this:
353 # [1] "a550962-4368120-060520-500_A03.CEL" "a550962-4368120-060520-500_A05.CEL"
354 gds_data_frame <- data.frame(gds_array);
355 # gds_data_frame looks like this:
356 # gds_array
357 # a550962-4368120-060520-500_A03.CEL
358 # a550962-4368120-060520-500_A05.CEL
359 gds_data_table <- setDT(gds_data_frame, keep.rownames=FALSE)[];
360 # Rename the gds_array column.
361 setnames(gds_data_table, c("gds_array"), c("affy_id"));
362 # affy_id_region_list looks like this:
363 # affy_id region
364 # a100000-4368120-060520-256_I07.CEL USVI
365 # a100000-4368120-060520-256_K07.CEL USVI
366 affy_id_region_list <- population_info_data_table[c(2,3,4)];
367 gds_data_table_join <- gds_data_table %>%
368 left_join(affy_id_region_list %>%
369 select("affy_id", "user_specimen_id","region"),
370 by='affy_id')%>%
371 drop_na();
372 samp.annot <- data.frame(pop.group=c(gds_data_table_join$region));
373 add.gdsn(genofile, "sample.annot", samp.annot);
374 # population_code looks like this:
375 # [1] 18.361733 18.361733 18.361733 18.361733 18.361733 18.361733
376 # [7] 25.11844009 25.11844009 25.11844009 25.11844009 25.11844009 25.11844009
377 population_code <- read.gdsn(index.gdsn(genofile, path="sample.annot/pop.group"));
378 pop.group <- as.factor(read.gdsn(index.gdsn(genofile, "sample.annot/pop.group")));
379 # pop.group looks like this:
380 # [1] 18.361733 18.361733 18.361733 18.361733 18.361733 18.361733
381 # [7] 25.11844009 25.11844009 25.11844009 25.11844009 25.11844009 25.11844009
382 time_elapsed(start_time);
383
384 # Distance matrix calculation and sample labels change to user specimen ids.
385 start_time <- time_start("Calculating distance matrix");
386 ibs <- snpgdsIBS(genofile, num.thread=2, autosome.only=FALSE);
387 ibs$sample.id <-gds_data_table_join$user_specimen_id;
388 time_elapsed(start_time);
389
390 # Cluster analysis on the genome-wide IBS pairwise distance matrix.
391 start_time <- time_start("Clustering the genome-wide IBS pairwise distance matrix");
392 set.seed(100);
393 par(cex=0.6, cex.lab=1, cex.axis=1.5,cex.main=2);
394 ibs.hc <- snpgdsHCluster(ibs);
395 time_elapsed(start_time);
396
397 # cols looks like this:
398 # blue1 red green pink orange blue2
399 # "#0C5BB0FF" "#EE0011FF" "#15983DFF" "#EC579AFF" "#FA6B09FF" "#149BEDFF"
400 # green2 yellow turquoise poop
401 # "#A1C720FF" "#FEC10BFF" "#16A08CFF" "#9A703EFF"
402 cols <- piratepal("basel");
403 set.seed(999);
404
405 # Generate plots.
406 # Default clustering.
407 start_time <- time_start("Creating ibs_default.pdf");
408 # Start PDF device driver.
409 dev.new(width=40, height=20);
410 file_path = get_file_path(output_plots_dir, "ibs_default.pdf");
411 pdf(file=file_path, width=40, height=20);
412 rv <- snpgdsCutTree(ibs.hc, col.list=cols, pch.list=15);
413 snpgdsDrawTree(rv, main="Color by Cluster", leaflab="perpendicular", yaxis.kinship=FALSE);
414 abline(h = 0.032, lty = 2);
415 legend("topleft", legend=levels(rv$samp.group), xpd=T, col=cols[1:nlevels(rv$samp.group)], pch=15, ncol=4, cex=1.2);
416 dev.off()
417 time_elapsed(start_time);
418
419 # Color cluster by region.
420 start_time <- time_start("Creating ibs_region.pdf");
421 # Start PDF device driver.
422 dev.new(width=40, height=20);
423 file_path = get_file_path(output_plots_dir, "ibs_region.pdf");
424 pdf(file=file_path, width=40, height=20);
425 race <- as.factor(population_code);
426 rv2 <- snpgdsCutTree(ibs.hc, samp.group=race,col.list=cols, pch.list=15);
427 snpgdsDrawTree(rv2, main="Color by Region", leaflab="perpendicular", yaxis.kinship=FALSE);
428 legend("topleft", legend=levels(race), xpd=T, col=cols[1:nlevels(race)], pch=15, ncol=4, cex=1.2);
429 dev.off()
430 time_elapsed(start_time);
431
432 # Missing data barplot.
433 start_time <- time_start("Creating missing_data.pdf");
434 population_info_data_table$miss <- stag_db_report$percent_missing_data_coral[match(missing_gt_data_frame$affy_id, stag_db_report$affy_id)];
435 test2 <- which(!is.na(population_info_data_table$miss));
436 miss96 <- population_info_data_table$miss[test2];
437 name96 <- population_info_data_table$user_specimen_id[test2];
438 # Start PDF device driver.
439 dev.new(width=20, height=10);
440 file_path = get_file_path(output_plots_dir, "missing_data.pdf");
441 pdf(file=file_path, width=20, height=10);
442 par(mar = c(8, 4, 4, 2));
443 x <- barplot(miss96, las=2, col=cols, ylim=c(0, 3), cex.axis=0.8, space=0.8, ylab="Missingness (%)", xaxt="n");
444 text(cex=0.8, x=x-0.25, y=-.05, name96, xpd=TRUE, srt=60, adj=1);
445 dev.off()
446 time_elapsed(start_time);
447
448 # Sample MLG on a map.
449 start_time <- time_start("Creating mlg_map.pdf");
450 # Get the lattitude and longitude boundaries for rendering
451 # the map. Tese boundaries will restrict the map to focus
452 # (i.e., zoom) on the region of the world map from which
453 # the samples were taken.
454 max_latitude <- max(affy_metadata_data_frame$latitude, na.rm=TRUE);
455 min_latitude <- min(affy_metadata_data_frame$latitude, na.rm=TRUE);
456 latitude_range_vector <- c(min_latitude-3, max_latitude+3);
457 max_longitude <- max(affy_metadata_data_frame$longitude, na.rm=TRUE);
458 min_longitude <- min(affy_metadata_data_frame$longitude, na.rm=TRUE);
459 longitude_range_vector <- c(min_longitude-3, max_longitude+3);
460 # Get the palette colors for rendering plots.
461 colors <- length(unique(stag_db_report$coral_mlg_clonal_id));
462 # Get a color palette.
463 palette <- colorRampPalette(piratepal("basel"));
464 # Start PDF device driver.
465 dev.new(width=20, height=20);
466 file_path = get_file_path(output_plots_dir, "mlg_map.pdf");
467 pdf(file=file_path, width=20, height=20);
468 world_data = map_data("world");
469 # Add the coral_mlg_clonal_id column from the stag_db_report
470 # data fram to the affy_metadata_data_frame.
471 affy_metadata_data_frame$mlg <- stag_db_report$coral_mlg_clonal_id;
472 # Get the number of colors needed from the palette for plotting
473 # the sample locations on the world map.
474 num_colors = length(unique(affy_metadata_data_frame$mlg));
475 # Get a color palette.
476 palette = colorRampPalette(piratepal("basel"));
477 ggplot() +
478 geom_map(data=world_data, map=world_data, aes(x=long, y=lat, group=group, map_id=region), fill="white", colour="#7f7f7f") +
479 coord_quickmap(xlim=longitude_range_vector, ylim=latitude_range_vector) +
480 geom_point(data=affy_metadata_data_frame, aes(x=longitude, y=latitude, group=mlg, colour=mlg), alpha=.7, size=3) +
481 scale_color_manual(values=palette(num_colors)) +
482 theme(legend.position="bottom") +
483 guides(color=guide_legend(nrow=8, byrow=F));
484
485 # Sample MLG on a map for each region.
486 for (i in unique(affy_metadata_data_frame$region)) {
487 m <- i;
488 num_colors_2 = length(unique(affy_metadata_data_frame$mlg[which(affy_metadata_data_frame$region == m)]));
489 max_latitude_region <- max(affy_metadata_data_frame$latitude[which(affy_metadata_data_frame$region == m)],na.rm=TRUE);
490 min_latitude_region <- min(affy_metadata_data_frame$latitude[which(affy_metadata_data_frame$region == m)], na.rm=TRUE);
491 latitude_range_vector_region <- c(min_latitude_region-0.5, max_latitude_region+0.5);
492 max_longitude_region <- max(affy_metadata_data_frame$longitude[which(affy_metadata_data_frame$region == m)], na.rm=TRUE);
493 min_longitude_region <- min(affy_metadata_data_frame$longitude[which(affy_metadata_data_frame$region == m)], na.rm=TRUE);
494 longitude_range_vector_region <- c(min_longitude_region-0.5, max_longitude_region+0.5);
495 print(ggplot() +
496 geom_map(data=world_data, map=world_data, aes(x=long, y=lat, group=group, map_id=region),
497 fill="grey", colour="#7f7f7f") +
498 coord_quickmap(xlim=longitude_range_vector_region, ylim=latitude_range_vector_region, clip = "on") +
499 geom_point(data=affy_metadata_data_frame[which(affy_metadata_data_frame$region == m),], aes(x=longitude, y=latitude,
500 group=mlg, colour=mlg), alpha=.5, size=3) +
501 scale_color_manual(values=palette(num_colors_2)) +
502 theme(legend.position="bottom") + labs(title=paste("MLG assignments for", m)) +
503 guides(color=guide_legend(nrow=8, byrow=F)));
504 }
505 dev.off()
506 time_elapsed(start_time);
507
508 if (!is.null(opt$output_nj_phylogeny_tree)) {
509 # Create a phylogeny tree of samples based on distance matrices.
510 # Start PDF device driver.
511 start_time <- time_start("Creating nj_phylogeny_tree.pdf");
512 # Table of alleles for the new samples subset to new plate data.
513 # Create vector indicating number of individuals desired from
514 # affy_id column of stag_db_report data table.
515 i <- ifelse(is.na(stag_db_report[1]), "", stag_db_report[[1]]);
516 i <- i[!apply(i== "", 1, all),];
517 sample_alleles_vector <- genind_clone[i, mlg.reset=FALSE, drop=FALSE];
518 dev.new(width=40, height=80);
519 file_path = get_file_path(output_plots_dir, "nj_phylogeny_tree.pdf");
520 pdf(file=file_path, width=40, height=80);
521 # Organize branches by clade.
522 nj_phylogeny_tree <- sample_alleles_vector %>%
523 aboot(dist=provesti.dist, sample=100, tree="nj", cutoff=50, quiet=TRUE, showtree = FALSE) %>%
524 ladderize();
525 nj_phylogeny_tree$tip.label <- stag_db_report$user_specimen_id[match(nj_phylogeny_tree$tip.label, stag_db_report$affy_id)];
526 plot.phylo(nj_phylogeny_tree, tip.color=cols[sample_alleles_vector$pop], label.offset=0.0025, cex=0.6, font=2, lwd=4, align.tip.label=F, no.margin=T);
527 # Add a scale bar showing 5% difference.
528 add.scale.bar(0, 0.95, length=0.05, cex=0.65, lwd=2);
529 nodelabels(nj_phylogeny_tree$node.label, cex=.5, adj=c(1.5, -0.1), frame="n", font=3, xpd=TRUE);
530 legend("topright", legend=c(levels(sample_alleles_vector$pop)), text.col=cols, xpd=T, cex=0.8);
531 dev.off()
532 time_elapsed(start_time);
533 }
534
535 # Generate a pie chart for each sample with a genotype.
536 # Store the numerical and user_specimen_id values from
537 # stag_db_report for the charts (user_specimen_id names
538 # will be used to label each chart).
539 start_time <- time_start("Creating percent_breakdown.pdf");
540 stag_db_report_data_table <- stag_db_report[c(-2, -3, -4)];
541 # Remove NA and NaN values.
542 stag_db_report_data_table <- na.omit(stag_db_report_data_table);
543 # Translate to N (i.e., number of samples with a genotype)
544 # columns and 5 rows.
545 translated_stag_db_report_data_table <- t(stag_db_report_data_table);
546 translated_stag_db_report_matrix <- as.matrix(translated_stag_db_report_data_table[-1,]);
547 # Set the storage mode of the matrix to numeric. In some
548 # cases this could result in the following:
549 # Warning message:
550 # In mde(x) : NAs introduced by coercion
551 mode(translated_stag_db_report_matrix) <- "numeric";
552 # Remove NA and NaN values that may have been introduced
553 # by coercion.
554 translated_stag_db_report_matrix <- na.omit(translated_stag_db_report_matrix);
555 tsdbrm_row_means <- rowMeans(translated_stag_db_report_matrix, na.rm=TRUE);
556 dev.new(width=10, height=7);
557 file_path = get_file_path(output_plots_dir, "percent_breakdown.pdf");
558 pdf(file=file_path, width=10, height=7);
559 # Average pie of all samples.
560 labels <- paste(c("missing data", "mixed", "reference", "alternative"), " (", round(tsdbrm_row_means, 1), "%)", sep="");
561 col <- c("GREY", "#006DDB", "#24FF24", "#920000");
562 main <- "Average breakdown of SNP assignments across all samples";
563 pie(tsdbrm_row_means, labels=labels, radius=0.60, col=col, main=main, cex.main=.75);
564 par(mfrow=c(3, 2));
565 col <- c("GREY", "#006DDB", "#24FF24", "#920000");
566 # Generate a pie chart for each sample with genotypes.
567 for (i in 1:ncol(translated_stag_db_report_matrix)) {
568 tmp_labels <- paste(c("missing data", "mixed", "reference", "alternative"), " (", round(translated_stag_db_report_matrix[,i], 1), "%)", sep="");
569 main <- paste("Breakdown of SNP assignments for", translated_stag_db_report_data_table[1, i]);
570 pie(translated_stag_db_report_matrix[,i], labels=tmp_labels, radius=0.90, col=col, main=main, cex.main=.85, cex=0.75);
571 }
572 dev.off()
573 time_elapsed(start_time);
574
575 # close GDS file.
576 snpgdsClose(genofile);
577
578 # Prepare to output data frames for input to a downstream
579 # tool that will use them to update the stag database.
580 start_time <- time_start("Building data frames for insertion into database tables");
581 # sample_prep_data_frame looks like this (split across comment lines):
582 # user_specimen_id field_call bcoral_genet_id bsym_genet_id reef
583 # test_002 prolifera NA NA JohnsonsReef
584 # region latitude longitude geographic_origin colony_location
585 # Bahamas 18.36173 -64.77430 Reef NA
586 # depth disease_resist bleach_resist
587 # 5 NA N
588 # mortality tle spawning collector_last_name collector_first_name organization
589 # NA NA False Kitchen Sheila Penn State
590 # collection_date email seq_facility array_version public
591 # 2018-11-08 k89@psu.edu Affymetrix 1 True
592 # public_after_date sperm_motility healing_time dna_extraction_method
593 # NA -9 -9 NA
594 # dna_concentration registry_id result_folder_name plate_barcode mlg
595 # NA NA PRO100175_PSU175_SAX_b02 P9SR10074 HG0227
596 # affy_id percent_missing_data_coral percent_heterozygous_coral
597 # a550962-436.CEL 1.06 19.10
598 # percent_reference_coral percent_alternative_coral
599 # 40.10459 39.73396
600 sample_prep_data_frame <- affy_metadata_data_frame %>%
601 left_join(stag_db_report %>%
602 select("user_specimen_id", "affy_id", "percent_missing_data_coral", "percent_heterozygous_coral",
603 "percent_reference_coral", "percent_alternative_coral"),
604 by='user_specimen_id');
605 # Get the number of rows for all data frames.
606 num_rows <- nrow(sample_prep_data_frame);
607 # Set the column names so that we can extract only those columns
608 # needed for the sample table.
609 colnames(sample_prep_data_frame) <- c("user_specimen_id", "field_call", "bcoral_genet_id", "bsym_genet_id", "reef",
610 "region", "latitude", "longitude", "geographic_origin", "colony_location",
611 "depth", "disease_resist", "bleach_resist", "mortality", "tle",
612 "spawning", "collector_last_name", "collector_first_name", "organization",
613 "collection_date", "email", "seq_facility", "array_version", "public",
614 "public_after_date", "sperm_motility", "healing_time", "dna_extraction_method",
615 "dna_concentration", "registry_id", "result_folder_name", "plate_barcode",
616 "mlg", "affy_id", "percent_missing_data_coral", "percent_heterozygous_coral",
617 "percent_reference_coral", "percent_alternative_coral");
618
619 # Output the data frame for updating the alleles table.
620 # Subset to only the new plate data.
621 i <- ifelse(is.na(stag_db_report[3]), "", stag_db_report[[3]]);
622 # Create a vector indicating the number of individuals desired
623 # from the affy_id collumn in the report_user data table.
624 i <- i[!apply(i=="", 1, all),];
625 # Subset the genclone object to the user data.
626 allele_vector <- genind_clone[i, mlg.reset=FALSE, drop=FALSE];
627 # Convert the subset genclone to a data frame.
628 allele_data_frame <- genind2df(allele_vector, sep="");
629 allele_data_frame <- allele_data_frame %>%
630 select(-pop);
631 # Allele string for Allele.table in database.
632 allele_table_data_frame <- unite(allele_data_frame, alleles, 1:19696, sep=" ", remove=TRUE);
633 allele_table_data_frame <- setDT(allele_table_data_frame, keep.rownames=TRUE)[];
634 setnames(allele_table_data_frame, c("rn"), c("affy_id"));
635 # write.csv(concat_sample_alleles,file=paste("Seed_genotype_alleles.csv",sep = ""),quote=FALSE,row.names=FALSE);
636 write_data_frame(output_data_dir, "allele.tabular", allele_table_data_frame);
637
638 # Output the data frame for updating the experiment table.
639 experiment_table_data_frame <- data.frame(matrix(ncol=4, nrow=num_rows));
640 colnames(experiment_table_data_frame) <- c("seq_facility", "array_version", "result_folder_name", "plate_barcode");
641 for (i in 1:num_rows) {
642 experiment_table_data_frame$seq_facility[i] <- sample_prep_data_frame$seq_facility[i];
643 experiment_table_data_frame$array_version[i] <- sample_prep_data_frame$array_version[i];
644 experiment_table_data_frame$result_folder_name[i] <- sample_prep_data_frame$result_folder_name[i];
645 experiment_table_data_frame$plate_barcode[i] <- sample_prep_data_frame$plate_barcode[i];
646 }
647 write_data_frame(output_data_dir, "experiment.tabular", experiment_table_data_frame);
648
649 # Output the data frame for updating the colony table.
650 # The geographic_origin value is used for deciding into which table
651 # to insert the latitude and longitude values. If the geographic_origin
652 # is "reef", the values will be inserted into the reef table, and if it is
653 # "colony", the values will be inserted into the colony table. We insert
654 # these values in both data frames so that the downstream tool that parses
655 # them can determine the appropriate table.
656 colony_table_data_frame <- data.frame(matrix(ncol=4, nrow=num_rows));
657 colnames(colony_table_data_frame) <- c("latitude", "longitude", "depth", "geographic_origin");
658 for (i in 1:num_rows) {
659 colony_table_data_frame$latitude[i] <- sample_prep_data_frame$latitude[i];
660 colony_table_data_frame$longitude[i] <- sample_prep_data_frame$longitude[i];
661 colony_table_data_frame$depth[i] <- sample_prep_data_frame$depth[i];
662 colony_table_data_frame$geographic_origin[i] <- sample_prep_data_frame$geographic_origin[i];
663 }
664 write_data_frame(output_data_dir, "colony.tabular", colony_table_data_frame);
665
666 # Output the data frame for populating the genotype table.
667 # Combine with previously genotyped samples.
668 # prep_genotype_tibble looks like this:
669 # A tibble: 220 x 7
670 # Groups: group [?]
671 # group affy_id coral_mlg_clona… user_specimen_id db_match
672 # <int> <chr> <chr> <chr> <chr>
673 # 1 a10000… 13905 HG0048 match
674 # genetic_coral_species_call coral_mlg_rep_sample_id
675 # <chr> <chr>
676 # A.palmata 1104
677 prep_genotype_tibble <- id_data_table %>%
678 group_by(row_number()) %>%
679 dplyr::rename(group='row_number()') %>%
680 unnest(affy_id) %>%
681 left_join(smlg_data_frame %>%
682 select("affy_id", "coral_mlg_rep_sample_id", "coral_mlg_clonal_id", "user_specimen_id",
683 "genetic_coral_species_call", "bcoral_genet_id"),
684 by='affy_id');
685 # Confirm that the representative mlg is the same between runs.
686 uniques2 <- unique(prep_genotype_tibble[c("group", "coral_mlg_rep_sample_id")]);
687 uniques2 <- uniques2[!is.na(uniques2$coral_mlg_rep_sample_id),];
688 na.mlg3 <- which(is.na(prep_genotype_tibble$coral_mlg_rep_sample_id));
689 na.group2 <- prep_genotype_tibble$group[na.mlg3];
690 prep_genotype_tibble$coral_mlg_rep_sample_id[na.mlg3] <- uniques2$coral_mlg_rep_sample_id[match(na.group2, uniques2$group)];
691 # Transform the representative mlg column with new genotyped samples.
692 # representative_mlg_tibble looks like this:
693 # A tibble: 220 x 5
694 # affy_id coral_mlg_rep_sa… coral_mlg_clona… user_specimen_id
695 # <chr> <chr> <chr> <chr>
696 # a100000-… 13905 HG0048 13905
697 # genetic_coral_species_call bcoral_genet_id
698 # <chr> <chr>
699 # A.palmata C1651
700 representative_mlg_tibble <- prep_genotype_tibble %>%
701 mutate(coral_mlg_rep_sample_id=ifelse(is.na(coral_mlg_rep_sample_id), affy_id, coral_mlg_rep_sample_id)) %>%
702 ungroup() %>%
703 select(-group);
704 # prep_genotype_table_tibble looks like this:
705 # affy_id coral_mlg_clonal_id user_specimen_id db_match
706 # a550962...CEL HG0120 1090 match
707 # genetic_coral_species_call coral_mlg_rep_sample_id
708 # A.palmata 1104
709 prep_genotype_table_tibble <- stag_db_report %>%
710 select("affy_id", "coral_mlg_clonal_id", "user_specimen_id", "db_match", "genetic_coral_species_call") %>%
711 left_join(representative_mlg_tibble %>%
712 select("affy_id", "coral_mlg_rep_sample_id"),
713 by='affy_id');
714 # genotype_table_tibble looks like this:
715 # affy_id coral_mlg_clonal_id user_specimen_id db_match
716 # a550962-436.CEL HG0120 1090 match
717 # genetic_coral_species_call coral_mlg_rep_sample_id bcoral_genet_id
718 # A.palmata 1104 <NA>
719 genotype_table_tibble <- prep_genotype_table_tibble %>%
720 left_join(affy_metadata_data_frame %>%
721 select("user_specimen_id", "bcoral_genet_id"),
722 by='user_specimen_id');
723 write_data_frame(output_data_dir, "genotype.tabular", genotype_table_tibble);
724
725 # Output the file needed for populating the person table.
726 person_table_data_frame <- data.frame(matrix(ncol=4, nrow=num_rows));
727 colnames(person_table_data_frame) <- c("last_name", "first_name", "organization", "email");
728 # person_table_data_frame looks like this:
729 # last_name first_name organization email
730 # Kitchen Sheila Penn State s89@psu.edu
731 for (i in 1:num_rows) {
732 person_table_data_frame$last_name[i] <- sample_prep_data_frame$collector_last_name[i];
733 person_table_data_frame$first_name[i] <- sample_prep_data_frame$collector_first_name[i];
734 person_table_data_frame$organization[i] <- sample_prep_data_frame$organization[i];
735 person_table_data_frame$email[i] <- sample_prep_data_frame$email[i];
736 }
737 write_data_frame(output_data_dir, "person.tabular", person_table_data_frame);
738
739 # Output the file needed for populating the phenotype table.
740 phenotype_table_data_frame <- data.frame(matrix(ncol=7, nrow=num_rows));
741 colnames(phenotype_table_data_frame) <- c("disease_resist", "bleach_resist", "mortality", "tle",
742 "spawning", "sperm_motility", "healing_time");
743 # phenotype_table_data_frame looks like this:
744 # disease_resist bleach_resist mortality tle spawning sperm_motility healing_time
745 # NA NA NA NA False NA NA
746 for (i in 1:num_rows) {
747 phenotype_table_data_frame$disease_resist[i] <- sample_prep_data_frame$disease_resist[i];
748 phenotype_table_data_frame$bleach_resist[i] <- sample_prep_data_frame$bleach_resist[i];
749 phenotype_table_data_frame$mortality[i] <- sample_prep_data_frame$mortality[i];
750 phenotype_table_data_frame$tle[i] <- sample_prep_data_frame$tle[i];
751 phenotype_table_data_frame$spawning[i] <- sample_prep_data_frame$spawning[i];
752 phenotype_table_data_frame$sperm_motility[i] <- sample_prep_data_frame$sperm_motility[i];
753 phenotype_table_data_frame$healing_time[i] <- sample_prep_data_frame$healing_time[i];
754 }
755 write_data_frame(output_data_dir, "phenotype.tabular", phenotype_table_data_frame);
756
757 # Output the file needed for populating the reef table.
758 reef_table_data_frame <- data.frame(matrix(ncol=5, nrow=num_rows));
759 colnames(reef_table_data_frame) <- c("name", "region", "latitude", "longitude", "geographic_origin");
760 # The geographic_origin value is used for deciding into which table
761 # to insert the latitude and longitude values. If the geographic_origin
762 # is "reef", the values will be inserted into the reef table, and if it is
763 # "colony", the values will be inserted into the colony table. We insert
764 # these values in both data frames so that the downstream tool that parses
765 # them can determine the appropriate table.
766 # reef_table_data_frame looks like this:
767 # name region latitude longitude geographic_origin
768 # JohnsonsReef Bahamas 18.361733 -64.7743 Reef
769 for (i in 1:num_rows) {
770 reef_table_data_frame$name[i] <- sample_prep_data_frame$reef[i];
771 reef_table_data_frame$region[i] <- sample_prep_data_frame$region[i];
772 reef_table_data_frame$latitude[i] <- sample_prep_data_frame$latitude[i];
773 reef_table_data_frame$longitude[i] <- sample_prep_data_frame$longitude[i];
774 reef_table_data_frame$geographic_origin[i] <- sample_prep_data_frame$geographic_origin[i];
775 }
776 write_data_frame(output_data_dir, "reef.tabular", reef_table_data_frame);
777
778 # Output the file needed for populating the sample table.
779 sample_table_data_frame <- data.frame(matrix(ncol=20, nrow=num_rows));
780 colnames(sample_table_data_frame) <- c("affy_id", "colony_location", "collection_date", "user_specimen_id",
781 "registry_id", "depth", "dna_extraction_method", "dna_concentration",
782 "public", "public_after_date", "percent_missing_data_coral",
783 "percent_missing_data_sym", "percent_reference_coral",
784 "percent_reference_sym", "percent_alternative_coral",
785 "percent_alternative_sym", "percent_heterozygous_coral",
786 "percent_heterozygous_sym", "field_call", "bcoral_genet_id");
787 for (i in 1:num_rows) {
788 sample_table_data_frame$affy_id[i] <- sample_prep_data_frame$affy_id[i];
789 sample_table_data_frame$colony_location[i] <- sample_prep_data_frame$colony_location[i];
790 sample_table_data_frame$collection_date[i] <- sample_prep_data_frame$collection_date[i];
791 sample_table_data_frame$user_specimen_id[i] <- sample_prep_data_frame$user_specimen_id[i];
792 sample_table_data_frame$registry_id[i] <- sample_prep_data_frame$registry_id[i];
793 sample_table_data_frame$depth[i] <- sample_prep_data_frame$depth[i];
794 sample_table_data_frame$dna_extraction_method[i] <- sample_prep_data_frame$dna_extraction_method[i];
795 sample_table_data_frame$dna_concentration[i] <- sample_prep_data_frame$dna_concentration[i];
796 sample_table_data_frame$public[i] <- sample_prep_data_frame$public[i];
797 sample_table_data_frame$public_after_date[i] <- sample_prep_data_frame$public_after_date[i];
798 sample_table_data_frame$percent_missing_data_coral[i] <- sample_prep_data_frame$percent_missing_data_coral[i];
799 sample_table_data_frame$percent_missing_data_sym[i] <- DEFAULT_MISSING_NUMERIC_VALUE;
800 sample_table_data_frame$percent_reference_coral[i] <- sample_prep_data_frame$percent_reference_coral[i];
801 sample_table_data_frame$percent_reference_sym[i] <- DEFAULT_MISSING_NUMERIC_VALUE;
802 sample_table_data_frame$percent_alternative_coral[i] <- sample_prep_data_frame$percent_alternative_coral[i];
803 sample_table_data_frame$percent_alternative_sym[i] <- DEFAULT_MISSING_NUMERIC_VALUE;
804 sample_table_data_frame$percent_heterozygous_coral[i] <- sample_prep_data_frame$percent_heterozygous_coral[i];
805 sample_table_data_frame$percent_heterozygous_sym[i] <- DEFAULT_MISSING_NUMERIC_VALUE;
806 sample_table_data_frame$field_call[i] <- sample_prep_data_frame$field_call[i];
807 sample_table_data_frame$bcoral_genet_id[i] <- sample_prep_data_frame$bcoral_genet_id[i];
808 }
809 write_data_frame(output_data_dir, "sample.tabular", sample_table_data_frame);
810
811 # Output the file needed for populating the taxonomy table.
812 # taxonomy_table_data_frame looks like this:
813 # genetic_coral_species_call affy_id genus_name species_name
814 # A.palmata a550962-4368120-060520-500_A05.CEL Acropora palmata
815 taxonomy_table_data_frame <- stag_db_report %>%
816 select(genetic_coral_species_call, affy_id) %>%
817 mutate(genus_name = ifelse(genetic_coral_species_call == genetic_coral_species_call[grep("^A.*", genetic_coral_species_call)], "Acropora", "other")) %>%
818 mutate(species_name = ifelse(genetic_coral_species_call == "A.palmata", "palmata", "other")) %>%
819 mutate(species_name = ifelse(genetic_coral_species_call == "A.cervicornis", "cervicornis", species_name)) %>%
820 mutate(species_name = ifelse(genetic_coral_species_call == "A.prolifera", "prolifera", species_name));
821 colnames(taxonomy_table_data_frame) <- c("genetic_coral_species_call", "affy_id", "genus_name", "species_name");
822 write_data_frame(output_data_dir, "taxonomy.tabular", taxonomy_table_data_frame);
823 time_elapsed(start_time);