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comparison XSeekerPreparator.R @ 0:1660665c081e draft

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#issue9 branch Updating - - Fxx
author fgiacomoni
date Thu, 19 Nov 2020 14:28:46 +0000
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1
2
3 TOOL_NAME <- "XSeekerPreparator"
4 VERSION <- "1.1.2"
5
6 OUTPUT_SPECIFIC_TOOL <- "XSeeker_Galaxy"
7
8 ENRICHED_RDATA_VERSION <- paste("1.1.2", OUTPUT_SPECIFIC_TOOL, sep="-")
9 ENRICHED_RDATA_DOC <- sprintf("
10 Welcome to the enriched <Version %s> of the output of CAMERA/xcms.
11 This doc was generated by the tool: %s - Version %s
12 To show the different variables contained in this rdata, type:
13 - `load('this_rdata.rdata', rdata_env <- new.env())`
14 - `names(rdata_env)`
15
16 Sections
17 ######
18
19
20 This tools helpers
21 ------
22 The version number is somewhat special because the evolution of the
23 rdata's format is non-linear.
24 There may be different branches, each evolving separatly.
25 To reflect these branches's diversions, there may be a prepended
26 branch name following this format:
27 major.minor.patch-branch_name
28 Like this, we can process rdata with the same tool, and output
29 rdata formated differently, for each tool.
30
31
32 - enriched_rdata:
33 - Description: flag created by that tool to tell it was enriched.
34 - Retrieval method: enriched_rdata <- TRUE
35
36 - enriched_rdata_version:
37 - Description: A flag created by that tool to tell which version of
38 this tool has enriched the rdata.
39 - Retrieval method: enriched_rdata_version <- sprintf(\"%s\", ENRICHED_RDATA_VERSION)
40
41 - enriched_rdata_doc:
42 - Description: Contains the documentation string.
43
44 Data from original mzxml file
45 ------
46 - tic:
47 - Description: Those are the tic values from the original mzxml
48 file, extracted using xcms 2.
49 - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@tic
50 - xcms version: 2.0
51
52 - mz:
53 - Description: Those are the m/z values from the original mzxml
54 file, extracted using xcms 2.
55 - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@env$mz
56 - xcms version: 2.0
57
58 - scanindex:
59 - Description: Those are the scanindex values from the original mzxml
60 file, extracted using xcms 2.
61 - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@scanindex
62 - xcms version: 2.0
63
64 - scantime:
65 - Description: Those are the scantime values from the original mzxml
66 file, extracted using xcms 2.
67 - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@scantime
68 - xcms version: 2.0
69
70 - intensity:
71 - Description: Those are the intensity values from the original mzxml
72 file, extracted using xcms 2.
73 - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@env$intensity
74 - xcms version: 2.0
75
76 - polarity:
77 - Description: Those are the polarity values from the original mzxml
78 file, extracted using xcms 2.
79 - Retrieval method: as.character(xcms::xcmsRaw('original_file.mzxml')@polarity[[1]])
80 - xcms version: 2.0
81
82 Data taken from incoming rdata
83 ------
84 - variableMetadata:
85 - Description: Unmodified copy of variableMetadata from incoming rdata.
86 - Retrieval method: rdata_file$variableMetadata
87
88 - process_params:
89 - Description: Those are the processing parameters values from the
90 curent rdata. They have been simplified to allow easy access like:
91 for (params in process_params) {
92 if (params[[\"xfunction\"]] == \"annotatediff\") {
93 process_peak_picking_params(params)
94 }
95 }
96 - Retrieval method:
97 ## just he same list, but simplified
98 process_params <- list()
99 for (list_name in names(rdata_file$listOFlistArguments)) {
100 param_list <- list()
101 for (param_name in names(rdata_file$listOFlistArguments[[list_name]])) {
102 param_list[[param_name]] <- rdata_file$listOFlistArguments[[list_name]][[param_name]]
103 }
104 process_params[[length(process_params)+1]] <- param_list
105 }
106 ", ENRICHED_RDATA_VERSION, TOOL_NAME, VERSION, ENRICHED_RDATA_VERSION)
107
108
109
110 get_models <- function(path) {
111 if (is.null(path)) {
112 stop("No models to define the database schema")
113 } else {
114 message(sprintf("Loading models from %s", path))
115 }
116 ## galaxy mangles the "@" to a "__at__"
117 if (substr(path, 1, 9) == "git__at__") {
118 path <- sub("^git__at__", "git@", path, perl=TRUE)
119 }
120 if (
121 substr(path, 1, 4) == "git@"
122 || substr(path, length(path)-4, 4) == ".git"
123 ) {
124 return (get_models_from_git(path))
125 }
126 if (substr(path, 1, 4) == "http") {
127 return (get_models_from_url(path))
128 }
129 return (source(path)$value)
130 }
131
132 get_models_from_git <- function (url, target_file="models.R", rm=TRUE) {
133 tmp <- tempdir()
134 message(sprintf("Cloning %s", url))
135 system2("git", c("clone", url, tmp))
136 result <- search_tree(file.path(tmp, dir), target_file)
137 if (!is.null(result)) {
138 models <- source(result)$value
139 if (rm) {
140 unlink(tmp, recursive=TRUE)
141 }
142 return (models)
143 }
144 if (rm) {
145 unlink(tmp, recursive=TRUE)
146 }
147 stop(sprintf(
148 "Could not find any file named \"%s\" in this repo",
149 target_file
150 ))
151 }
152
153 get_models_from_url <- function (url, target_file="models.R", rm=TRUE) {
154 tmp <- tempdir()
155 message(sprintf("Downloading %s", url))
156 result <- file.path(tmp, target_file)
157 if (download.file(url, destfile=result) == 0) {
158 models <- source(result)$value
159 if (rm) {
160 unlink(tmp, recursive=TRUE)
161 }
162 return (models)
163 }
164 if (rm) {
165 unlink(tmp, recursive=TRUE)
166 }
167 stop("Could not download any file at this adress.")
168 }
169
170 search_tree <- function(path, target) {
171 target <- tolower(target)
172 for (file in list.files(path)) {
173 if (is.dir(file)) {
174 result <- search_tree(file.path(path, file), target)
175 if (!is.null(result)) {
176 return (result)
177 }
178 } else if (tolower(file) == target) {
179 return (file.path(path, file))
180 }
181 }
182 return (NULL)
183 }
184
185 create_database <- function(orm) {
186 orm$recreate_database(no_exists=FALSE)
187 set_database_version(orm, "created")
188 }
189
190 insert_adducts <- function(orm) {
191 message("Creating adducts...")
192 adducts <- list(
193 list("[M-H2O-H]-",1,-1,-48.992020312000001069,1,0,0.5,"H0","H1O3"),
194 list("[M-H-Cl+O]-",1,-1,-19.981214542000000022,2,0,0.5,"O1","H1Cl1"),
195 list("[M-Cl+O]-",1,-1,-18.973389510000000512,3,0,0.5,"O1","Cl1"),
196 list("[M-3H]3-",1,-3,-3.0218293560000000219,4,0,1.0,"H0","H3"),
197 list("[2M-3H]3-",2,-3,-3.0218293560000000219,4,0,0.5,"H0","H3"),
198 list("[3M-3H]3-",3,-3,-3.0218293560000000219,4,0,0.5,"H0","H3"),
199 list("[M-2H]2-",1,-2,-2.0145529039999998666,5,0,1.0,"H0","H2"),
200 list("[2M-2H]2-",2,-2,-2.0145529039999998666,5,0,0.5,"H0","H2"),
201 list("[3M-2H]2-",3,-2,-2.0145529039999998666,5,0,0.5,"H0","H2"),
202 list("[M-H]-",1,-1,-1.0072764519999999333,6,1,1.0,"H0","H1"),
203 list("[2M-H]-",2,-1,-1.0072764519999999333,6,0,0.5,"H0","H1"),
204 list("[3M-H]-",3,-1,-1.0072764519999999333,6,0,0.5,"H0","H1"),
205 list("[M]+",1,1,-0.00054858000000000000945,7,1,1.0,"H0","H0"),
206 list("[M]-",1,-1,0.00054858000000000000945,8,1,1.0,"H0","H0"),
207 list("[M+H]+",1,1,1.0072764519999999333,9,1,1.0,"H1","H0"),
208 list("[2M+H]+",2,1,1.0072764519999999333,9,0,0.5,"H1","H0"),
209 list("[3M+H]+",3,1,1.0072764519999999333,9,0,0.25,"H1","H0"),
210 list("[M+2H]2+",1,2,2.0145529039999998666,10,0,0.75,"H2","H0"),
211 list("[2M+2H]2+",2,2,2.0145529039999998666,10,0,0.5,"H2","H0"),
212 list("[3M+2H]2+",3,2,2.0145529039999998666,10,0,0.25,"H2","H0"),
213 list("[M+3H]3+",1,3,3.0218293560000000219,11,0,0.75,"H3","H0"),
214 list("[2M+3H]3+",2,3,3.0218293560000000219,11,0,0.5,"H3","H0"),
215 list("[3M+3H]3+",3,3,3.0218293560000000219,11,0,0.25,"H3","H0"),
216 list("[M-2H+NH4]-",1,-1,16.019272654000001665,12,0,0.25,"N1H4","H2"),
217 list("[2M-2H+NH4]-",2,-1,16.019272654000001665,12,0,0.0,"N1H4","H2"),
218 list("[3M-2H+NH4]-",3,-1,16.019272654000001665,12,0,0.25,"N1H4","H2"),
219 list("[M+NH4]+",1,1,18.033825558000000199,13,1,1.0,"N1H4","H0"),
220 list("[2M+NH4]+",2,1,18.033825558000000199,13,0,0.5,"N1H4","H0"),
221 list("[3M+NH4]+",3,1,18.033825558000000199,13,0,0.25,"N1H4","H0"),
222 list("[M+H+NH4]2+",1,2,19.041102009999999467,14,0,0.5,"N1H5","H0"),
223 list("[2M+H+NH4]2+",2,2,19.041102009999999467,14,0,0.5,"N1H5","H0"),
224 list("[3M+H+NH4]2+",3,2,19.041102009999999467,14,0,0.25,"N1H5","H0"),
225 list("[M+Na-2H]-",1,-1,20.974668176000001551,15,0,0.75,"Na1","H2"),
226 list("[2M-2H+Na]-",2,-1,20.974668176000001551,15,0,0.25,"Na1","H2"),
227 list("[3M-2H+Na]-",3,-1,20.974668176000001551,15,0,0.25,"Na1","H2"),
228 list("[M+Na]+",1,1,22.989221080000000086,16,1,1.0,"Na1","H0"),
229 list("[2M+Na]+",2,1,22.989221080000000086,16,0,0.5,"Na1","H0"),
230 list("[3M+Na]+",3,1,22.989221080000000086,16,0,0.25,"Na1","H0"),
231 list("[M+H+Na]2+",1,2,23.996497531999999353,17,0,0.5,"Na1H1","H0"),
232 list("[2M+H+Na]2+",2,2,23.996497531999999353,17,0,0.5,"Na1H1","H0"),
233 list("[3M+H+Na]2+",3,2,23.996497531999999353,17,0,0.25,"Na1H1","H0"),
234 list("[M+2H+Na]3+",1,3,25.003773983999998619,18,0,0.25,"H2Na1","H0"),
235 list("[M+CH3OH+H]+",1,1,33.033491200000000276,19,0,0.25,"C1O1H5","H0"),
236 list("[M-H+Cl]2-",1,-2,33.962124838000001148,20,0,1.0,"Cl1","H1"),
237 list("[2M-H+Cl]2-",2,-2,33.962124838000001148,20,0,0.5,"Cl1","H1"),
238 list("[3M-H+Cl]2-",3,-2,33.962124838000001148,20,0,0.5,"Cl1","H1"),
239 list("[M+Cl]-",1,-1,34.969401290000000416,21,1,1.0,"Cl1","H0"),
240 list("[2M+Cl]-",2,-1,34.969401290000000416,21,0,0.5,"Cl1","H0"),
241 list("[3M+Cl]-",3,-1,34.969401290000000416,21,0,0.5,"Cl1","H0"),
242 list("[M+K-2H]-",1,-1,36.948605415999999479,22,0,0.5,"K1","H2"),
243 list("[2M-2H+K]-",2,-1,36.948605415999999479,22,0,0.0,"K1","H2"),
244 list("[3M-2H+K]-",3,-1,36.948605415999999479,22,0,0.0,"K1","H2"),
245 list("[M+K]+",1,1,38.963158319999998013,23,1,1.0,"K1","H0"),
246 list("[2M+K]+",2,1,38.963158319999998013,23,0,0.5,"K1","H0"),
247 list("[3M+K]+",3,1,38.963158319999998013,23,0,0.25,"K1","H0"),
248 list("[M+H+K]2+",1,2,39.970434771999997281,24,0,0.5,"K1H1","H0"),
249 list("[2M+H+K]2+",2,2,39.970434771999997281,24,0,0.5,"K1H1","H0"),
250 list("[3M+H+K]2+",3,2,39.970434771999997281,24,0,0.25,"K1H1","H0"),
251 list("[M+ACN+H]+",1,1,42.033825557999996646,25,0,0.25,"C2H4N1","H0"),
252 list("[2M+ACN+H]+",2,1,42.033825557999996646,25,0,0.25,"C2H4N1","H0"),
253 list("[M+2Na-H]+",1,1,44.971165708000000902,26,0,0.5,"Na2","H1"),
254 list("[2M+2Na-H]+",2,1,44.971165708000000902,26,0,0.25,"Na2","H1"),
255 list("[3M+2Na-H]+",3,1,44.971165708000000902,26,0,0.25,"Na2","H1"),
256 list("[2M+FA-H]-",2,-1,44.998202851999998586,27,0,0.25,"C1O2H2","H1"),
257 list("[M+FA-H]-",1,-1,44.998202851999998586,27,0,0.5,"C1O2H2","H1"),
258 list("[M+2Na]2+",1,2,45.978442160000000172,28,0,0.5,"Na2","H0"),
259 list("[2M+2Na]2+",2,2,45.978442160000000172,28,0,0.5,"Na2","H0"),
260 list("[3M+2Na]2+",3,2,45.978442160000000172,28,0,0.25,"Na2","H0"),
261 list("[M+H+2Na]3+",1,3,46.985718611999999438,29,0,0.25,"H1Na2","H0"),
262 list("[M+H+FA]+",1,1,47.012755755999997122,30,0,0.25,"C1O2H3","H0"),
263 list("[M+Hac-H]-",1,-1,59.013852915999997607,31,0,0.25,"C2O2H4","H1"),
264 list("[2M+Hac-H]-",2,-1,59.013852915999997607,31,0,0.25,"C2O2H4","H1"),
265 list("[M+IsoProp+H]+",1,1,61.064791327999998317,32,0,0.25,"C3H9O1","H0"),
266 list("[M+Na+K]2+",1,2,61.9523793999999981,33,0,0.5,"Na1K1","H0"),
267 list("[2M+Na+K]2+",2,2,61.9523793999999981,33,0,0.5,"Na1K1","H0"),
268 list("[3M+Na+K]2+",3,2,61.9523793999999981,33,0,0.25,"Na1K1","H0"),
269 list("[M+NO3]-",1,-1,61.988366450000000895,34,0,0.5,"N1O3","H0"),
270 list("[M+ACN+Na]+",1,1,64.015770185999997464,35,0,0.25,"C2H3N1Na1","H0"),
271 list("[2M+ACN+Na]+",2,1,64.015770185999997464,35,0,0.25,"C2H3N1Na1","H0"),
272 list("[M+NH4+FA]+",1,1,64.039304861999994502,36,0,0.25,"N1C1O2H6","H0"),
273 list("[M-2H+Na+FA]-",1,-1,66.980147479999999405,37,0,0.5,"NaC1O2H2","H2"),
274 list("[M+3Na]3+",1,3,68.967663239999993153,38,0,0.25,"Na3","H0"),
275 list("[M+Na+FA]+",1,1,68.99470038399999794,39,0,0.25,"Na1C1O2H2","H0"),
276 list("[M+2Cl]2-",1,-2,69.938802580000000832,40,0,1.0,"Cl2","H0"),
277 list("[2M+2Cl]2-",2,-2,69.938802580000000832,40,0,0.5,"Cl2","H0"),
278 list("[3M+2Cl]2-",3,-2,69.938802580000000832,40,0,0.5,"Cl2","H0"),
279 list("[M+2K-H]+",1,1,76.919040187999996758,41,0,0.5,"K2","H1"),
280 list("[2M+2K-H]+",2,1,76.919040187999996758,41,0,0.25,"K2","H1"),
281 list("[3M+2K-H]+",3,1,76.919040187999996758,41,0,0.25,"K2","H1"),
282 list("[M+2K]2+",1,2,77.926316639999996028,42,0,0.5,"K2","H0"),
283 list("[2M+2K]2+",2,2,77.926316639999996028,42,0,0.5,"K2","H0"),
284 list("[3M+2K]2+",3,2,77.926316639999996028,42,0,0.25,"K2","H0"),
285 list("[M+Br]-",1,-1,78.918886479999997619,43,1,1.0,"Br1","H0"),
286 list("[M+Cl+FA]-",1,-1,80.974880593999998268,44,0,0.5,"Cl1C1O2H2","H0"),
287 list("[M+AcNa-H]-",1,-1,80.995797543999998426,45,0,0.25,"C2H3Na1O2","H1"),
288 list("[M+2ACN+2H]2+",1,2,84.067651115999993292,46,0,0.25,"C4H8N2","H0"),
289 list("[M+K+FA]+",1,1,84.968637623999995868,47,0,0.25,"K1C1O2H2","H0"),
290 list("[M+Cl+Na+FA-H]-",1,-1,102.95682522200000619,48,0,0.5,"Cl1Na1C1O2H2","H1"),
291 list("[2M+3H2O+2H]+",2,1,104.03153939599999944,49,0,0.25,"H8O6","H0"),
292 list("[M+TFA-H]-",1,-1,112.98558742000000165,50,0,0.5,"C2F3O2H1","H1"),
293 list("[M+H+TFA]+",1,1,115.00014032400000019,51,0,0.25,"C2F3O2H2","H0"),
294 list("[M+3ACN+2H]2+",1,2,125.09420022199999778,52,0,0.25,"C6H11N3","H0"),
295 list("[M+NH4+TFA]+",1,1,132.02668943000000468,53,0,0.25,"N1C2F3O2H5","H0"),
296 list("[M+Na+TFA]+",1,1,136.98208495200000811,54,0,0.25,"Na1C2F3O2H1","H0"),
297 list("[M+Cl+TFA]-",1,-1,148.96226516199999423,55,0,0.5,"Cl1C2F3O2H1","H0"),
298 list("[M+K+TFA]+",1,1,152.95602219200000604,56,0,0.25,"K1C2F3O2H1","H0")
299 )
300 dummy_adduct <- orm$adduct()
301 for (adduct in adducts) {
302 i <- 0
303 dummy_adduct$set_name(adduct[[i <- i+1]])
304 dummy_adduct$set_multi(adduct[[i <- i+1]])
305 dummy_adduct$set_charge(adduct[[i <- i+1]])
306 dummy_adduct$set_mass(adduct[[i <- i+1]])
307 dummy_adduct$set_oidscore(adduct[[i <- i+1]])
308 dummy_adduct$set_quasi(adduct[[i <- i+1]])
309 dummy_adduct$set_ips(adduct[[i <- i+1]])
310 dummy_adduct$set_formula_add(adduct[[i <- i+1]])
311 dummy_adduct$set_formula_ded(adduct[[i <- i+1]])
312 dummy_adduct$save()
313 dummy_adduct$clear(unset_id=TRUE)
314 }
315 message("Adducts created")
316 }
317
318 insert_base_data <- function(orm, path, archetype=FALSE) {
319 if (archetype) {
320 ## not implemented yet
321 return ()
322 }
323 base_data <- readLines(path)
324 for (sql in strsplit(paste(base_data, collapse=" "), ";")[[1]]) {
325 orm$execute(sql)
326 }
327 set_database_version(orm, "enriched")
328 }
329
330 insert_compounds <- function(orm, compounds_path) {
331 compounds <- read.csv(file=compounds_path, sep="\t")
332 if (is.null(compounds <- translate_compounds(compounds))) {
333 stop("Could not find asked compound's attributes in csv file.")
334 }
335 dummy_compound <- orm$compound()
336 compound_list <- list()
337 for (i in seq_len(nrow(compounds))) {
338 dummy_compound$set_mz(compounds[i, "mz"])
339 dummy_compound$set_name(compounds[i, "name"])
340 dummy_compound$set_common_name(compounds[i, "common_name"])
341 dummy_compound$set_formula(compounds[i, "formula"])
342 # dummy_compound$set_mz(compounds[i, "mz"])
343 # dummy_compound$set_mz(compounds[i, "mz"])
344 compound_list[[length(compound_list)+1]] <- as.list(
345 dummy_compound,
346 c("mz", "name", "common_name", "formula")
347 )
348 dummy_compound$clear(unset_id=TRUE)
349 }
350 dummy_compound$save(bulk=compound_list)
351 }
352
353 translate_compounds <- function(compounds) {
354 recognized_headers <- list(
355 c("HMDB_ID", "MzBank", "X.M.H..", "X.M.H...1", "MetName", "ChemFormula", "INChIkey")
356 )
357 header_translators <- list(
358 hmdb_header_translator
359 )
360 for (index in seq_along(recognized_headers)) {
361 headers <- recognized_headers[[index]]
362 if (identical(colnames(compounds), headers)) {
363 return (header_translators[[index]](compounds))
364 }
365 }
366 if (is.null(translator <- guess_translator(colnames(compounds)))) {
367 return (NULL)
368 }
369 return (csv_header_translator(translator, compounds))
370 }
371
372 guess_translator <- function(header) {
373 result <- list(
374 # HMDB_ID=NULL,<
375 mz=NULL,
376 name=NULL,
377 common_name=NULL,
378 formula=NULL,
379 # inchi_key=NULL
380 )
381 asked_cols <- names(result)
382 for (asked_col in asked_cols) {
383 for (col in header) {
384 if ((twisted <- tolower(col)) == asked_col
385 || gsub("-", "_", twisted) == asked_col
386 || gsub(" ", "_", twisted) == asked_col
387 || tolower(gsub("(.)([A-Z])", "\\1_\\2", col)) == asked_col
388 ) {
389 result[[asked_col]] <- col
390 next
391 }
392 }
393 }
394 if (any(mapply(is.null, result))) {
395 return (NULL)
396 }
397 return (result)
398 }
399
400 hmdb_header_translator <- function(compounds) {
401 return (csv_header_translator(
402 list(
403 HMDB_ID="HMDB_ID",
404 mz="MzBank",
405 name="MetName",
406 common_name="MetName",
407 formula="ChemFormula",
408 inchi_key="INChIkey"
409 ), compounds
410 ))
411 }
412
413 csv_header_translator <- function(translation_table, csv) {
414 header_names <- names(translation_table)
415 result <- data.frame(1:nrow(csv))
416 # colnames(result) <- header_names
417 for (i in seq_along(header_names)) {
418 result[, header_names[[i]]] <- csv[, translation_table[[i]]]
419 }
420 print(result[, "mz"])
421 result[, "mz"] <- as.numeric(result[, "mz"])
422 print(result[, "mz"])
423 return (result)
424 }
425
426 set_database_version <- function(orm, version) {
427 orm$set_tag(
428 version,
429 tag_name="database_version",
430 tag_table_name="XSeeker_tagging_table"
431 )
432 }
433
434 process_rdata <- function(orm, rdata, options) {
435 mzml_tmp_dir <- gather_mzml_files(rdata)
436 samples <- names(rdata$singlefile)
437 if (!is.null(options$samples)) {
438 samples <- samples[options$samples %in% samples]
439 }
440 show_percent <- (
441 is.null(options$`not-show-percent`)
442 || options$`not-show-percent` == FALSE
443 )
444 error <- tryCatch({
445 process_sample_list(
446 orm, rdata, samples,
447 show_percent=show_percent
448 )
449 NULL
450 }, error=function(e) {
451 message(e)
452 e
453 })
454 if (!is.null(mzml_tmp_dir)) {
455 unlink(mzml_tmp_dir, recursive=TRUE)
456 }
457 if (!is.null(error)) {
458 stop(error)
459 }
460 }
461
462 gather_mzml_files <- function(rdata) {
463 if (is.null(rdata$singlefile)) {
464 message("Extracting mxml files")
465 tmp <- tempdir()
466 rdata$singlefile <- utils::unzip(rdata$zipfile, exdir=tmp)
467 names(rdata$singlefile) <- tools::file_path_sans_ext(basename(rdata$singlefile))
468 message("Extracted")
469 return (tmp)
470 }
471 return (NULL)
472 }
473
474 process_sample_list <- function(orm, radta, sample_names, show_percent) {
475 file_grouping_var <- find_grouping_var(rdata$variableMetadata)
476 message("Processing samples.")
477 message(sprintf("File grouping variable: %s", file_grouping_var))
478 if(is.null(file_grouping_var)) {
479 stop("Malformed variableMetada.")
480 }
481
482 process_arg_list <- rdata$listOFlistArguments
483 process_params <- list()
484 for (list_name in names(process_arg_list)) {
485 param_list <- list()
486 for (param_name in names(process_arg_list[[list_name]])) {
487 param_list[[param_name]] <- process_arg_list[[list_name]][[param_name]]
488 }
489 process_params[[length(process_params)+1]] <- param_list
490 }
491 message("Parameters from previous processes extracted.")
492
493 var_meta <- rdata$variableMetadata
494 align_group <- rep(0, nrow(var_meta))
495 var_meta <- cbind(var_meta, align_group)
496 context <- new.env()
497 context$clusters <- list()
498 context$groupidx <- rdata$xa@xcmsSet@groupidx
499 context$peaks <- rdata$xa@xcmsSet@peaks
500 context$show_percent <- show_percent
501
502 indices <- as.numeric(unique(var_meta[, file_grouping_var]))
503 smol_xcms_set <- orm$smol_xcms_set()
504 mz_tab_info <- new.env()
505 xcms_set <- rdata$xa@xcmsSet
506 g <- xcms::groups(xcms_set)
507 mz_tab_info$group_length <- nrow(g)
508 mz_tab_info$dataset_path <- xcms::filepaths(xcms_set)
509 mz_tab_info$sampnames <- xcms::sampnames(xcms_set)
510 mz_tab_info$sampclass <- xcms::sampclass(xcms_set)
511 mz_tab_info$rtmed <- g[,"rtmed"]
512 mz_tab_info$mzmed <- g[,"mzmed"]
513 mz_tab_info$smallmolecule_abundance_assay <- xcms::groupval(xcms_set, value="into")
514 blogified <- blob::blob(fst::compress_fst(serialize(mz_tab_info, NULL), compression=100))
515 smol_xcms_set$set_raw(blogified)$save()
516 for (no in indices) {
517 sample_name <- names(rdata$singlefile)[[no]]
518 sample_path <- rdata$singlefile[[no]]
519 if (
520 is.na(no)
521 || is.null(sample_path)
522 || !(sample_name %in% sample_names)
523 ) {
524 next
525 }
526 ms_file=xcms::xcmsRaw(sample_path)
527 env <- new.env()
528 env$variableMetadata <- var_meta[var_meta[, file_grouping_var]==no,]
529 env$tic <- ms_file@tic
530 env$mz <- ms_file@env$mz
531 env$scanindex <- ms_file@scanindex
532 env$scantime <- ms_file@scantime
533 env$intensity <- ms_file@env$intensity
534 env$polarity <- as.character(ms_file@polarity[[1]])
535 env$sample_name <- sample_name
536 env$dataset_path <- sample_path
537 env$process_params <- process_params
538 env$enriched_rdata <- TRUE
539 env$enriched_rdata_version <- ENRICHED_RDATA_VERSION
540 env$tool_name <- TOOL_NAME
541 env$enriched_rdata_doc <- ENRICHED_RDATA_DOC
542 context$sample_no <- no
543 add_sample_to_database(orm, env, context, smol_xcms_set)
544 }
545 message("Features enrichment")
546 complete_features(orm, context)
547 message("Features enrichment done.")
548 return (NULL)
549 }
550
551 find_grouping_var <- function(var_meta) {
552 for (grouping_var in c(".", "Bio")) {
553 if (!is.null(rdata$variableMetadata[[grouping_var]])) {
554 return (grouping_var)
555 }
556 }
557 return (NULL)
558 }
559
560 add_sample_to_database <- function(orm, env, context, smol_xcms_set) {
561 message(sprintf("Processing sample %s", env$sample_name))
562 sample <- (
563 orm$sample()
564 $set_name(env$sample_name)
565 $set_path(env$dataset_path)
566 $set_kind("enriched_rdata")
567 $set_polarity(
568 if (is.null(env$polarity) || identical(env$polarity, character(0))) ""
569 else env$polarity
570 )
571 $set_smol_xcms_set(smol_xcms_set)
572 $set_raw(blob::blob(fst::compress_fst(
573 serialize(env, NULL),
574 compression=100
575 )))
576 $save()
577 )
578 load_variable_metadata(orm, sample, env$variableMetadata, context)
579 load_process_params(orm, sample, env$process_params)
580 message(sprintf("Sample %s inserted.", env$sample_name))
581 return (sample)
582 }
583
584
585 load_variable_metadata <- function(orm, sample, var_meta, context) {
586 all_clusters <- orm$cluster()$all()
587
588 next_feature_id <- get_next_id(orm$feature()$all(), "featureID")
589 next_cluster_id <- get_next_id(all_clusters, "clusterID")
590 next_pc_group <- get_next_id(all_clusters, "pc_group")
591 next_align_group <- get_next_id(all_clusters, "align_group")
592 message("Extracting features")
593 invisible(create_features(
594 orm, sample, var_meta, context,
595 next_feature_id, next_cluster_id,
596 next_pc_group, next_align_group
597 ))
598 message("Extracting features done.")
599 return (NULL)
600 }
601
602 get_next_id <- function(models, attribute) {
603 if ((id <- models$max(attribute)) == Inf || id == -Inf) {
604 return (1)
605 }
606 return (id + 1)
607 }
608
609 create_features <- function(
610 orm, sample, var_meta, context,
611 next_feature_id, next_cluster_id,
612 next_pc_group, next_align_group
613 ) {
614 field_names <- as.list(names(orm$feature()$fields__))
615 field_names[field_names=="id"] <- NULL
616
617 features <- list()
618 dummy_feature <- orm$feature()
619
620 if (show_percent <- context$show_percent) {
621 percent <- -1
622 total <- nrow(var_meta)
623 }
624 for (row in seq_len(nrow(var_meta))) {
625 if (show_percent && (row / total) * 100 > percent) {
626 percent <- percent + 1
627 message("\r", sprintf("\r%d %%", percent), appendLF=FALSE)
628 }
629
630 curent_var_meta <- var_meta[row, ]
631
632 peak_list <- context$peaks[context$groupidx[[row]], ]
633 sample_peak_list <- peak_list[peak_list[, "sample"] == context$sample_no, , drop=FALSE]
634 if (!identical(sample_peak_list, numeric(0)) && !is.null(nrow(sample_peak_list)) && nrow(sample_peak_list) != 0) {
635 if (!is.na(int_o <- extract_peak_var(sample_peak_list, "into"))) {
636 dummy_feature$set_int_o(int_o)
637 }
638 if (!is.na(int_b <- extract_peak_var(sample_peak_list, "intb"))) {
639 dummy_feature$set_int_b(int_b)
640 }
641 if (!is.na(max_o <- extract_peak_var(sample_peak_list, "maxo"))) {
642 dummy_feature$set_max_o(max_o)
643 }
644 }
645
646 set_feature_fields_from_var_meta(dummy_feature, curent_var_meta)
647
648 dummy_feature$set_featureID(next_feature_id)
649 next_feature_id <- next_feature_id + 1
650 fake_iso <- dummy_feature$get_iso()
651 iso <- extract_iso(fake_iso)
652 clusterID <- extract_clusterID(fake_iso, next_cluster_id)
653 context$clusterID <- clusterID
654 dummy_feature$set_iso(iso)
655 create_associated_cluster(
656 sample, dummy_feature, clusterID,
657 context, curent_var_meta, next_pc_group,
658 next_align_group
659 )
660 next_align_group <- next_align_group + 1
661 features[[length(features)+1]] <- as.list(dummy_feature, field_names)
662 dummy_feature$clear()
663 }
664 message("")## +\n for previous message
665 message("Saving features")
666 dummy_feature$save(bulk=features)
667 message("Saved.")
668 return (context$clusters)
669 }
670
671 extract_peak_var <- function(peak_list, var_name, selector=max) {
672 value <- peak_list[, var_name]
673 names(value) <- NULL
674 return (selector(value))
675 }
676
677 set_feature_fields_from_var_meta <- function(feature, var_meta) {
678 if (!is.null(mz <- var_meta[["mz"]]) && !is.na(mz)) {
679 feature$set_mz(mz)
680 }
681 if (!is.null(mzmin <- var_meta[["mzmin"]]) && !is.na(mzmin)) {
682 feature$set_mz_min(mzmin)
683 }
684 if (!is.null(mzmax <- var_meta[["mzmax"]]) && !is.na(mzmax)) {
685 feature$set_mz_max(mzmax)
686 }
687 if (!is.null(rt <- var_meta[["rt"]]) && !is.na(rt)) {
688 feature$set_rt(rt)
689 }
690 if (!is.null(rtmin <- var_meta[["rtmin"]]) && !is.na(rtmin)) {
691 feature$set_rt_min(rtmin)
692 }
693 if (!is.null(rtmax <- var_meta[["rtmax"]]) && !is.na(rtmax)) {
694 feature$set_rt_max(rtmax)
695 }
696 if (!is.null(isotopes <- var_meta[["isotopes"]]) && !is.na(isotopes)) {
697 feature$set_iso(isotopes)
698 }
699 return (feature)
700 }
701
702 extract_iso <- function(weird_data) {
703 if (grepl("^\\[\\d+\\]", weird_data)[[1]]) {
704 return (sub("^\\[\\d+\\]", "", weird_data, perl=TRUE))
705 }
706 return (weird_data)
707 }
708
709 extract_clusterID <- function(weird_data, next_cluster_id){
710 if (grepl("^\\[\\d+\\]", weird_data)[[1]]) {
711 clusterID <- stringr::str_extract(weird_data, "^\\[\\d+\\]")
712 clusterID <- as.numeric(stringr::str_extract(clusterID, "\\d+"))
713 } else {
714 clusterID <- 0
715 }
716 return (clusterID + next_cluster_id)
717 }
718
719 create_associated_cluster <- function(
720 sample, feature, grouping_variable,
721 context, curent_var_meta, next_pc_group, next_align_group
722 ) {
723 pcgroup <- as.numeric(curent_var_meta[["pcgroup"]])
724 adduct <- as.character(curent_var_meta[["adduct"]])
725 annotation <- curent_var_meta[["isotopes"]]
726 grouping_variable <- as.character(grouping_variable)
727 if (is.null(cluster <- context$clusters[[grouping_variable]])) {
728 cluster <- context$clusters[[grouping_variable]] <- orm$cluster(
729 pc_group=pcgroup + next_pc_group,
730 adduct=adduct,
731 align_group=next_align_group,
732 # curent_group=curent_group,
733 clusterID=context$clusterID,
734 annotation=annotation
735 )$set_sample(sample)
736 } else {
737 if (context$clusterID != 0 && cluster$get_clusterID() == 0) {
738 cluster$set_clusterID(context$clusterID)
739 }
740 }
741 cluster$save()
742 feature$set_cluster(cluster)
743 return (feature)
744 }
745
746 complete_features <- function(orm, context) {
747 for (cluster in context$clusters) {
748 features <- orm$feature()$load_by(cluster_id=cluster$get_id())
749 if (features$any()) {
750 if (!is.null(rt <- features$mean("rt"))) {
751 cluster$set_mean_rt(rt)$save()
752 }
753 features_df <- as.data.frame(features)
754 central_feature <- features_df[grepl("^\\[M\\]", features_df[, "iso"]), ]
755 central_feature_into <- central_feature[["int_o"]]
756 if (!identical(central_feature_into, numeric(0)) && central_feature_into != 0) {
757 for (feature in as.vector(features)) {
758 feature$set_abundance(
759 feature$get_int_o() / central_feature_into * 100
760 )$save()
761 }
762 }
763 }
764 }
765 return (NULL)
766 }
767
768 load_process_params <- function(orm, sample, params) {
769 for (param_list in params) {
770 if (is.null(param_list[["xfunction"]])) {
771 next
772 }
773 if (param_list[["xfunction"]] == "annotatediff") {
774 load_process_params_peak_picking(orm, sample, param_list)
775 }
776 }
777 return (sample)
778 }
779
780 load_process_params_peak_picking <- function(orm, sample, peak_picking_params) {
781 return (add_sample_process_parameters(
782 params=peak_picking_params,
783 params_translation=list(
784 ppm="ppm",
785 maxcharge="maxCharge",
786 maxiso="maxIso"
787 ),
788 param_model_generator=orm$peak_picking_parameters,
789 sample_param_setter=sample$set_peak_picking_parameters
790 ))
791 }
792
793 add_sample_process_parameters <- function(
794 params,
795 params_translation,
796 param_model_generator,
797 sample_param_setter
798 ) {
799 model_params <- list()
800 for (rdata_param_name in names(params_translation)) {
801 database_param_name <- params_translation[[rdata_param_name]]
802 if (is.null(rdata_param <- params[[rdata_param_name]])) {
803 next
804 }
805 model_params[[database_param_name]] <- rdata_param
806 }
807 params_models <- do.call(param_model_generator()$load_by, model_params)
808 if (params_models$any()) {
809 params_model <- params_models$first()
810 } else {
811 params_model <- do.call(param_model_generator, model_params)
812 params_model$save()
813 }
814 return (sample_param_setter(params_model)$save())
815 }
816
817
818 library(optparse)
819
820 option_list <- list(
821 optparse::make_option(
822 c("-v", "--version"),
823 action="store_true",
824 help="Display this tool's version and exits"
825 ),
826 optparse::make_option(
827 c("-i", "--input"),
828 type="character",
829 help="The rdata path to import in XSeeker"
830 ),
831 optparse::make_option(
832 c("-s", "--samples"),
833 type="character",
834 help="Samples to visualise in XSeeker"
835 ),
836 optparse::make_option(
837 c("-B", "--archetype"),
838 type="character",
839 help="The name of the base database"
840 ),
841 optparse::make_option(
842 c("-b", "--database"),
843 type="character",
844 help="The base database's path"
845 ),
846 optparse::make_option(
847 c("-c", "--compounds-csv"),
848 type="character",
849 help="The csv containing compounds"
850 ),
851 optparse::make_option(
852 c("-m", "--models"),
853 type="character",
854 help="The path or url (must begin with http[s]:// or git@) to the database's models"
855 ),
856 optparse::make_option(
857 c("-o", "--output"),
858 type="character",
859 help="The path where to output sqlite"
860 ),
861 optparse::make_option(
862 c("-P", "--not-show-percent"),
863 action="store_true",
864 help="Flag not to show the percents",
865 default=FALSE
866 )
867 )
868
869 options(error=function(){traceback(3)})
870
871 parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
872 args <- parse_args(parser, positional_arguments=0)
873
874 err_code <- 0
875
876 if (!is.null(args$options$version)) {
877 message(sprintf("%s %s", TOOL_NAME, VERSION))
878 quit()
879 }
880
881 models <- get_models(args$options$models)
882 orm <- DBModelR::ORM(
883 connection_params=list(dbname=args$options$output),
884 dbms="SQLite"
885 )
886
887 invisible(orm$models(models))
888 invisible(create_database(orm))
889
890 message("Database model created")
891
892 insert_adducts(orm)
893
894 if (!is.null(args$options$database)) {
895 insert_base_data(orm, args$options$database)
896 }
897 message(sprintf("Base data inserted using %s.", args$options$database))
898
899 if (!is.null(args$options$archetype)) {
900 insert_base_data(orm, args$options$archetype, archetype=TRUE)
901 }
902 if (!is.null(args$options$`compounds-csv`)) {
903 insert_compounds(orm, args$options$`compounds-csv`)
904 }
905
906 # if (!is.null(args$options$rdata)) {
907 # load_rdata_in_base(args$options$rdata, args$options$samples, args$options$`not-show-percent`)
908 # }
909
910
911 load(args$options$input, rdata <- new.env())
912
913 process_rdata(orm, rdata, args$options)
914
915 quit(status=err_code)
916
917