extractAnnotations.R 52.1 KB
Newer Older
Emma Schymanski's avatar
Emma Schymanski committed
1 2 3 4 5 6 7 8
# Functions for Extracting Annotations Data from PubChem
# Established to get Metabolites/Metabolism Information from HSDB (and others)
# As yet not thoroughly tested on other annotation sources or types
# Emma Schymanski, 1 May 2020 (based off earlier files from late Apr. 2020)
# In collaboration with PubChem (Evan Bolton, Paul Thiessen, Jeff Zhang)

#depends
library(RChemMass)
Emma Schymanski's avatar
Emma Schymanski committed
9
#setwd("C:/DATA/PubChem/")
Emma Schymanski's avatar
Emma Schymanski committed
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40


#' Retrieve Number of Pages for Annotation JSON file
#' 
#' Retrieves the number of pages for annotations files. As this requires
#' retrieving the entire JSON file (up to 1000 entries), this can take 
#' some time (hence large default timeout). Thanks to Paul Thiessen 
#' and Evan Bolton from PubChem team for assistance. 
#' 
#' @usage getPcAnno.TotalPages(source, heading, timeout=100)
#' 
#' @param source Data source name of the annotations data, e.g. \code{"HSDB"}
#' @param heading Annotation category desired, e.g. \code{"Metabolism/Metabolites"}
#' @param timeout The timeout, in seconds.  
#' @return The number of pages
#' 
#' @details 
#' This is primarily a supporter function for retrieving full annotation data
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' PubChem Data Sources (with annotations):
#' \url{https://pubchem.ncbi.nlm.nih.gov/source/#type=Annotations}
#' 
#' @seealso \code{\link{getMSInfo.files}}
#' 
#' @examples
#' n_pages <- getPcAnno.TotalPages('HSDB','Metabolism/Metabolites')
Emma Schymanski's avatar
Emma Schymanski committed
41
#' n_pages <- getPcAnno.TotalPages("NORMAN Suspect List Exchange","Transformations")
Emma Schymanski's avatar
Emma Schymanski committed
42 43 44 45 46 47 48 49
#' 
#' @export
getPcAnno.TotalPages <- function(source, heading, timeout=100) {
  # set URL
  baseURL <- 'https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/annotations/heading/JSON/?source='
  midURL <- '&heading='
  endURL <- '&heading_type=Compound&page=1'
  url <- paste0(baseURL, source, midURL, heading, endURL)
Emma Schymanski's avatar
Emma Schymanski committed
50 51
  #Example:
  #https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/annotations/heading/JSON/?source=NORMAN%20Suspect%20List%20Exchange&heading_type=Compound&heading=Transformations&page=1
Emma Schymanski's avatar
Emma Schymanski committed
52 53 54 55 56
  
  errorvar <- 0
  currEnvir <- environment()
  
  tryCatch(
Emma Schymanski's avatar
Emma Schymanski committed
57 58 59 60 61
    #data <- getURL(URLencode(url),timeout=timeout),
    {
      res <- GET(URLencode(url))
      data <- httr::content(res, type="text", encoding="UTF-8")
    },
Emma Schymanski's avatar
Emma Schymanski committed
62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154
    error=function(e){
      currEnvir$errorvar <- 1
    })
  
  if(errorvar){
    return(NA)
  }
  
  r <- fromJSON(data)
  
  if(!is.null(r$Fault)) {
    return(NA)
  }
  
  n_pages <- r$Annotations$TotalPages
  return(n_pages)
  
}


#' Retrieve Annotation Details from Metabolism/Metabolites Sections
#' 
#' Retrieve the annotation information from the Metabolism/Metabolites 
#' section of data sources, per page. As this requires
#' retrieving the entire JSON file (up to 1000 entries), this can take 
#' some time (hence large default timeout). Thanks to Paul Thiessen 
#' and Evan Bolton from PubChem team for assistance. 
#' 
#' @usage getPcAnno.Metabolism(source, heading, page=1, file_name="", timeout=100)
#' 
#' @param source Data source name of the annotations data, e.g. \code{"HSDB"}
#' @param heading Annotation category desired, e.g. \code{"Metabolism/Metabolites"}
#' @param page Page number to retrieve. Use \link{getPcAnno.TotalPages} to 
#' determine page numbers available
#' @param file_name If empty, \code{"heading_source_pageX.csv"} is created, 
#' overwriting any existing file of the same name in the directory. 
#' @param timeout The timeout, in seconds. Should be generous as these are large files
#' @return file_name of a saved multi-column CSV containing Source Name, Source ID, 
#' Source Chemical Name, Reference text, Description text and 
#' pipe-separated CIDs of the metabolites (\code{tp_cids}).
#' 
#' @details 
#' This function may work for other annotation headings, but has not been 
#' tested beyond Metabolism/Metabolites for HSDB.
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' PubChem Data Sources (with annotations):
#' \url{https://pubchem.ncbi.nlm.nih.gov/source/#type=Annotations}
#' 
#' @seealso \code{\link{getPcAnno.TotalPages}}, \code{\link{getPcAnno.SourceCIDs}}
#' 
#' @examples
#' # This retrieves only the first page
#' getPcAnno.Metabolism(source = 'HSDB',heading = 'Metabolism/Metabolites')
#' # This will retrieve all (in single files)
#' n_pages <- getPcAnno.TotalPages('HSDB','Metabolism/Metabolites')
#' for (i in 1:n_pages) {
#'   getPcAnno.Metabolism(source = 'HSDB',heading = 'Metabolism/Metabolites', page=i)
#' }
#' # merge together
#' HSDB_data <- read.csv("Metabolism_Metabolites_HSDB_page1.csv")
#' HSDB_data2 <- read.csv("Metabolism_Metabolites_HSDB_page2.csv")
#' HSDB_data_all <- merge(HSDB_data,HSDB_data2,all.x=TRUE,all.y=TRUE)
#' 
#' for (i in 3:n_pages) {
#'     file_name <- paste0("Metabolism_Metabolites_HSDB_page",i,".csv")
#'     HSDB_data2 <- read.csv(file_name)
#'     HSDB_data_all <- merge(HSDB_data_all,HSDB_data2,all.x=TRUE,all.y=TRUE)
#'     }
#' 
#' @export
getPcAnno.Metabolism <- function(source, heading, page=1, file_name="", timeout=100) {
  # set file_name if not set...
  if (nchar(file_name) < 4) {
    file_name <- paste0(sub("/","_",heading), "_", source, "_page", page,".csv")
  }
  # set URL
  baseURL <- 'https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/annotations/heading/JSON/?source='
  #source <- 'HSDB'
  midURL <- '&heading='
  #heading <- 'Metabolism/Metabolites'
  #endURL <- '&heading_type=Compound&page=1&response_type=display'
  endURL <- '&heading_type=Compound&page='
  url <- paste0(baseURL, source, midURL, heading, endURL, page)
  
  errorvar <- 0
  currEnvir <- environment()
  
  tryCatch(
Emma Schymanski's avatar
Emma Schymanski committed
155 156 157 158 159
    #data <- getURL(URLencode(url),timeout=timeout),
    {
      res <- GET(URLencode(url))
      data <- httr::content(res, type="text", encoding="UTF-8")
    },
Emma Schymanski's avatar
Emma Schymanski committed
160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297
    error=function(e){
      currEnvir$errorvar <- 1
    })
  
  if(errorvar){
    return(NA)
  }
  
  r <- fromJSON(data)
  
  if(!is.null(r$Fault)) {
    return(NA)
  }
  
  n_annos <- length(r$Annotations$Annotation) #1000 per page
  source_name <- vector(mode="character",length=n_annos)
  source_ID <- vector(mode="character",length=n_annos)
  source_chemical_name <- vector(mode="character",length=n_annos)
  desc_text <- vector(mode="character",length=n_annos)
  ref_text <- vector(mode="character",length=n_annos)
  tp_cids <- vector(mode="character",length=n_annos)
  n <- 1
  
  for (i in 1:n_annos) {
    #for (i in 1:3) {
    l1 <- r$Annotations$Annotation[[i]]
    n_refs <- length(l1$Data)
    
    for (k in 1:n_refs) {
      #print source name
      source_name[n] <- l1$SourceName #HSDB
      source_ID[n] <- l1$SourceID #ID from source
      source_chemical_name[n] <- l1$Name # name of chemical from source
      #extract next layer
      l2 <- l1$Data[[k]]
      if (length(l2$Reference)>0) {
        ref_text[n] <- l2$Reference[1] # reference
      }
      # get next layer
      l3 <- l2$Value$StringWithMarkup[[1]]
      n_cids <- length(l3$Markup)
      desc_text[n] <- l3$String # description mentioning metabolites ... 
      # loop over n_cids ... 
      cids <- ""
      if (n_cids > 0) {
        for (j in 1:n_cids) {
          cid <- sub("CID-","",l3$Markup[[j]]$Extra)
          if (j == 1) {
            cids <- cid
          } else {
            cids <- paste(cids,cid,sep="|")
          }
        }
      }
      tp_cids[n] <- cids
      n <- n+1
    }
  }
  anno_data <- cbind(source_name, source_ID, source_chemical_name, ref_text, desc_text, tp_cids)
  write.csv(anno_data, file_name ,row.names = F)
  return(file_name)
}




#' Retrieve SourceID-CID mappings for HSDB
#' 
#' Retrieve the sourceID-CID mappings for HSDB. As this requires
#' retrieving the entire JSON file (up to 1000 entries), this can take 
#' some time (hence large default timeout). Thanks to Paul Thiessen 
#' and Evan Bolton from PubChem team for assistance. 
#' 
#' @usage getPcAnno.SourceCIDs(source, heading, page=1, file_name="",timeout=100)
#' 
#' @param source Data source name of the annotations data, e.g. \code{"HSDB"}
#' @param heading Heading of the category desired, e.g. 
#' \code{"Hazardous%20Substances%20DataBank%20Number"}
#' @param page Page number to retrieve. Use \link{getPcAnno.TotalPages} to 
#' determine page numbers available
#' @param file_name If empty, \code{"heading_source_pageX.csv"} is created, 
#' overwriting any existing file of the same name in the directory. 
#' @param timeout The timeout, in seconds. Should be generous as these are large files
#' @return file_name of a saved multi-column CSV containing Source Name, Source ID, 
#' Source Chemical Name, the first CID (\code{cids}), the number of CIDs 
#' (\code{n_cids}), pipe-separated CIDs of the metabolites (\code{all_cids}).
#' 
#' @details 
#' Although written more generically, this function is a special case for HSDB, 
#' unless extended to other annotation sources in the future. 
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' HSDB Annotations:
#' \url{https://pubchem.ncbi.nlm.nih.gov/source/11933#data=Annotations}
#' 
#' @seealso \code{\link{getPcAnno.TotalPages}}, \code{\link{getPcAnno.Metabolism}}
#' 
#' @examples
#' # This retrieves only the first page
#' getPcAnno.SourceCIDs('HSDB','Hazardous%20Substances%20DataBank%20Number')
#' # Get all data
#' n_source_pages <- getPcAnno.TotalPages('HSDB','Hazardous%20Substances%20DataBank%20Number')
#' getPcAnno.SourceCIDs('HSDB','Hazardous%20Substances%20DataBank%20Number',page=1)
#' HSDB_data <- read.csv("HazardousSubstancesDataBankNumber_HSDB_page1.csv")
#' for (i in 2:n_source_pages) {
#'   getPcAnno.SourceCIDs('HSDB','Hazardous%20Substances%20DataBank%20Number',page=i)
#' }
#' HSDB_data <- read.csv("HazardousSubstancesDataBankNumber_HSDB_page1.csv")
#' HSDB_data2 <- read.csv("HazardousSubstancesDataBankNumber_HSDB_page2.csv")
#' HSDB_data_all <- merge(HSDB_data,HSDB_data2,all.x=TRUE,all.y=TRUE)
#' for (i in 3:n_source_pages) {
#'   file_name <- paste0("HazardousSubstancesDataBankNumber_HSDB_page",i,".csv")
#'   HSDB_data2 <- read.csv(file_name)
#'   HSDB_data_all <- merge(HSDB_data_all,HSDB_data2,all.x=TRUE,all.y=TRUE)
#' }
#' 
#' write.csv(HSDB_data_all,"HazardousSubstancesDataBankNumber_HSDB_allpages.csv")
#' 
#' @export
getPcAnno.SourceCIDs <- function(source, heading, page=1, file_name="",timeout=100) {
  # set file_name if not set...
  if (nchar(file_name) < 4) {
    file_name <- paste0(gsub("%20","",heading,fixed=T), "_", source, "_page", page,".csv")
  }
  # set URL
  baseURL <- 'https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/annotations/heading/JSON/?source='
  midURL <- '&heading='
  endURL <- '&heading_type=Compound&page='
  url <- paste0(baseURL, source, midURL, heading, endURL, page)
  
  errorvar <- 0
  currEnvir <- environment()
  
  tryCatch(
Emma Schymanski's avatar
Emma Schymanski committed
298 299 300 301 302
#    data <- getURL(URLencode(url),timeout=timeout),
    {
      res <- GET(URLencode(url))
      data <- httr::content(res, type="text", encoding="UTF-8")
    },
Emma Schymanski's avatar
Emma Schymanski committed
303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385
    error=function(e){
      currEnvir$errorvar <- 1
    })
  
  if(errorvar){
    return(NA)
  }
  
  r <- fromJSON(data)
  
  if(!is.null(r$Fault)) {
    return(NA)
  }
  
  n_annos <- length(r$Annotations$Annotation) #1000 per page
  source_name <- vector(mode="character",length=n_annos)
  source_ID <- vector(mode="character",length=n_annos)
  source_chemical_name <- vector(mode="character",length=n_annos)
  cids <- vector(mode="character",length=n_annos)
  n_cids <- vector(mode="character",length=n_annos)
  all_cids <- vector(mode="character",length=n_annos)
  
  for (i in 1:n_annos) {
    #for (i in 1:3) {
    l1 <- r$Annotations$Annotation[[i]]
    source_name[i] <- l1$SourceName #HSDB
    source_ID[i] <- l1$SourceID #ID from source
    source_chemical_name[i] <- l1$Name # name of chemical from source
    cids_entry <- l1$LinkedRecords$CID
    if (length(cids_entry)>0) {
      cids[i] <- l1$LinkedRecords$CID[1]
      n_cids[i] <- length(cids_entry)
      all_cids[i] <- paste(cids_entry,collapse="|")
    }
  }
  anno_data <- cbind(source_name, source_ID, source_chemical_name, cids, n_cids, all_cids)
  #colnames(anno_data) 
  write.csv(anno_data, file_name ,row.names = F)
  return(file_name)
  
}




#' Retrieve Metabolite CIDs from Extracted Annotation Data 
#' 
#' Retrieve the metabolite CIDs from extracted annotation data and save
#' the extracted annotation information in a CSV. Thanks to Paul Thiessen, 
#' Jeff Zhang and Evan Bolton from PubChem team for assistance. 
#' 
#' @usage getPcAnno.TPcids(cid,anno_csv_name,tp_csv=TRUE)
#' 
#' @param cid PubChem CID to look up the metabolites, e.g. \code{2256}
#' @param anno_csv_name Name of the file containing the extracted annotation 
#' information, retrieved with \link{getPcAnno.TotalPages} 
#' @param tp_csv Default \code{TRUE} writes a CSV with the same name as
#' \code{anno_csv_name_CID.csv"} is created, 
#' overwriting any existing file of the same name in the directory. 
#' @return CIDs list and CSV file of exactly the same format as the input
#' file, only containing entries matching the input CID.
#' 
#' @details 
#' The files that are directly extracted do not contain the CIDs, these 
#' have to be merged. Function outstanding ... 
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' @seealso \code{\link{getPcAnno.Metabolism}}
#' 
#' @examples
#' #This retrieves only the first page (OK for atrazine)
#' anno_csv_name <- getPcAnno.Metabolism(source = 'HSDB',heading = 'Metabolism/Metabolites')
#' # retrieve the CIDs (saving a CSV as well)
#' getPcAnno.TPcids(2256,anno_csv_name)
#' 
#' @export
getPcAnno.TPcids <- function(cid,anno_csv_name,tp_csv=TRUE) {
  anno_data <- read.csv(anno_csv_name,stringsAsFactors = F)
  tps_index <- which(anno_data$cids==cid)
Emma Schymanski's avatar
Emma Schymanski committed
386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401
  if (length(tps_index)>0) {
    all_cids <- paste(anno_data$tp_cids[tps_index],collapse="|")
    #  unique_cids <- na.omit(suppressWarnings(as.numeric(unique(strsplit(all_cids,"|",fixed=T)[[1]]))))
    unique_cids <- suppressWarnings(as.numeric(unique(strsplit(all_cids,"|",fixed=T)[[1]])))
    if (tp_csv) {
      tp_file_name <- sub(".csv",paste0("_",cid,".csv"),anno_csv_name)
      anno_data <- anno_data[tps_index,]
      anno_data$selected_tp_cids <- anno_data$tp_cids
      anno_data$use <- FALSE
      anno_data$transformation <- ""
      anno_data$isPredecessor <- TRUE #most cases, the starting entry will be predecessor
      anno_data$comment <- ""
      write.csv(anno_data,tp_file_name,row.names = F)
    } else {
      tp_file_name <- NA
    }
Emma Schymanski's avatar
Emma Schymanski committed
402 403
  } else {
    tp_file_name <- NA
Emma Schymanski's avatar
Emma Schymanski committed
404
    unique_cids <- NA
Emma Schymanski's avatar
Emma Schymanski committed
405
  }
Emma Schymanski's avatar
Emma Schymanski committed
406 407 408 409 410 411
  results <- list()
  results[['TP_File_Name']] <- tp_file_name
  results[['TP_Unique_CIDs']] <- unique_cids
  return(results)
  # 
  # return(unique_cids)
Emma Schymanski's avatar
Emma Schymanski committed
412 413 414
}


Emma Schymanski's avatar
Emma Schymanski committed
415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519
#' Retrieve CIDs with Transformations Entries (by page)
#' 
#' Retrieve the CIDs with Transformations entries from the 
#' annotation information at the Transformation 
#' section of data sources, per page. As this requires
#' retrieving the entire JSON file (up to 1000 entries), this can take 
#' some time (hence large default timeout). Thanks to Paul Thiessen, 
#' Jeff Zhang and Evan Bolton from PubChem team for assistance. 
#' 
#' @usage getPcAnno.Transformations(source, heading, page=1, file_name="", timeout=100)
#' 
#' @param source Data source name of the annotations data, e.g. \code{"NORMAN Suspect List Exchange"}
#' @param heading Annotation category desired, e.g. \code{"Transformations"}
#' @param page Page number to retrieve. Use \link{getPcAnno.TotalPages} to 
#' determine page numbers available
#' @param timeout The timeout, in seconds. Should be generous as these are large files
#' @return The CIDs mentioned in the record.
#' 
#' @details 
#' This function will only extract CIDs and thus probably only works for
#' Transformations sections (and/or any other section where only CIDs are
#' needed)
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' PubChem Data Sources (with annotations):
#' \url{https://pubchem.ncbi.nlm.nih.gov/source/#type=Annotations}
#' 
#' Transformations Example: 
#' \url{https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/annotations/heading/JSON/?source=NORMAN%20Suspect%20List%20Exchange&heading_type=Compound&heading=Transformations&page=1}
#' 
#' @seealso \code{\link{getPcAnno.TotalPages}}, \code{\link{getPcAnno.Metabolism}}
#' 
#' @examples
#' # This retrieves only the CIDs from the first page
#' getPcAnno.Transformations(source = 'NORMAN Suspect List Exchange',heading = 'Transformations')
#' # This will retrieve all as a list of CIDs
#' source <- "NORMAN Suspect List Exchange"
#' heading <- "Transformations"
#' n_pages <- getPcAnno.TotalPages(source, heading)
#' for (i in 1:n_pages) {
#'   if (i == 1) {
#'     cids <- getPcAnno.Transformations(source, heading, page=i)
#'   } else {
#'     cids_px <- getPcAnno.Transformations(source, heading, page=i)
#'     cids <- c(cids,cids_px)
#'   }
#' }
#' # May 2021 this was 4730 CIDs
#' 
#' @export
getPcAnno.Transformations <- function(source, heading, page=1, timeout=100) {
  # set URL
  baseURL <- 'https://pubchem.ncbi.nlm.nih.gov/rest/pug_view/annotations/heading/JSON/?source='
  #source <- 'HSDB'
  midURL <- '&heading='
  #heading <- 'Metabolism/Metabolites'
  #endURL <- '&heading_type=Compound&page=1&response_type=display'
  endURL <- '&heading_type=Compound&page='
  url <- paste0(baseURL, source, midURL, heading, endURL, page)
  
  errorvar <- 0
  currEnvir <- environment()
  
  tryCatch(
    #data <- getURL(URLencode(url),timeout=timeout),
    {
      res <- GET(URLencode(url))
      data <- httr::content(res, type="text", encoding="UTF-8")
    },
    error=function(e){
      currEnvir$errorvar <- 1
    })
  
  if(errorvar){
    return(NA)
  }
  
  r <- fromJSON(data)
  
  if(!is.null(r$Fault)) {
    return(NA)
  }
  
  n_annos <- length(r$Annotations$Annotation) #1000 per page
  cids <- vector(mode="character",length=n_annos)
  
  for (i in 1:n_annos) {
    #for (i in 1:3) {
    l1 <- r$Annotations$Annotation[[i]]
    cid <- l1$LinkedRecords$CID
    if (is.null(cid)) {
      cids[i] <- NA
    } else {
      cids[i] <- l1$LinkedRecords$CID
    }
  }
  
  return(cids)
}


Emma Schymanski's avatar
Emma Schymanski committed
520 521 522 523 524 525 526

#' Retrieve Transformations Table from PubChem Transformation Sections
#' 
#' Retrieve the transformation table (CSV) from the Transformation
#' section of individual compounds, by CID. Thanks to Jeff Zhang, Paul Thiessen 
#' and Evan Bolton from PubChem team for assistance. 
#' 
Emma Schymanski's avatar
Emma Schymanski committed
527
#' @usage getPcCand.trans(query, dataset="transformations",file_name="")
Emma Schymanski's avatar
Emma Schymanski committed
528 529 530
#' 
#' @param query CID to retrieve the transformation table, e.g. \code{2256}
#' @param dataset The name of the table behind the scenes, 
Emma Schymanski's avatar
Emma Schymanski committed
531
#' currently \code{"transformations"}. Other values will likely not work. 
Emma Schymanski's avatar
Emma Schymanski committed
532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
#' @param file_name If empty, \code{"CID_query_dataset.csv"} is created, 
#' overwriting any existing file of the same name in the directory. 
#' @return file_name of a saved multi-column CSV containing the transformation
#' information. 
#' 
#' @details 
#' NOTE: The transformations section is under development and this function may
#' break unexpectedly at any point. Changes in format are also possible.
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' Example PubChem Data Source (with transformations):
#' \url{https://pubchem.ncbi.nlm.nih.gov/source/23819#data=Annotations}
#' 
#' Example Transformations entry: 
#' \url{https://pubchem.ncbi.nlm.nih.gov/compound/2256#section=Transformations}
#' 
#' @examples
#' getPcCand.trans(1017)
#' getPcCand.trans(2256)
#' # an entry that doesn't have any transformation entry
#' getPcCand.trans(1234567)
#' 
#' @export
Emma Schymanski's avatar
Emma Schymanski committed
559
getPcCand.trans <- function(query, dataset="transformations",file_name="") {
Emma Schymanski's avatar
Emma Schymanski committed
560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578
  # set file_name if not set...
  if (nchar(file_name) < 4) {
    file_name <- paste0("CID_",query,"_",dataset,".csv")
  }
  
  baseURL <- 'https://pubchem.ncbi.nlm.nih.gov/sdq/sdqagent.cgi?infmt=json&outfmt=csv&query={"download":"*","collection":"'
  #then dataset
  midURL1 <- '","where":{"ands":[{"cid":"'
  #then CID
  midURL2 <- '"}]},"order":["relevancescore,desc"],"start":1,"limit":10000000,"downloadfilename":"'
  #then file_name
  endURL <- '"}'
  url <- paste0(baseURL, dataset, midURL1, query, midURL2, file_name, endURL)
  con <- curl(url)
  content <- suppressWarnings(readLines(con,n=1))
  close(con)
  if (length(grep("Warning",content))==0) {
    download.file(url,file_name,quiet=T)
    print(paste0("CID_",query,"_",dataset,": download successful"))
Emma Schymanski's avatar
Emma Schymanski committed
579
    if (file.exists(file_name)) {
Emma Schymanski's avatar
Emma Schymanski committed
580
      tp_info <- read.csv(file_name,stringsAsFactors = F)
Emma Schymanski's avatar
Emma Schymanski committed
581 582 583 584 585
      unique_cids <- unique(c(tp_info$predecessorcid,tp_info$successorcid))
    } else {
      unique_cids <- NA
    }
    
Emma Schymanski's avatar
Emma Schymanski committed
586 587
  } else {
    print(paste0("CID_",query,"_",dataset,": ",content))
Emma Schymanski's avatar
Emma Schymanski committed
588
    unique_cids <- NA
Emma Schymanski's avatar
Emma Schymanski committed
589
  }
Emma Schymanski's avatar
Emma Schymanski committed
590 591 592 593 594 595
  
  results <- list()
  results[['TP_File_Name']] <- file_name
  results[['TP_Unique_CIDs']] <- unique_cids
  return(results)
  #return(file_name)
Emma Schymanski's avatar
Emma Schymanski committed
596 597 598 599
}



Emma Schymanski's avatar
Emma Schymanski committed
600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642
#' Retrieve Download File from PubChem Queries (text-based search)
#' 
#' Download the results of text-based queries to PubChem. Thanks to Paul Thiessen 
#' and Evan Bolton from PubChem team for assistance. 
#' 
#' @usage getPcCand.query(query,file_name="")
#' 
#' @param query text-based query to search, e.g. \code{"coronavirus"}
#' @param file_name If empty, \code{"PubChem_Candidates_query.csv"} is created, 
#' overwriting any existing file of the same name in the directory. 
#' @return file_name of the multi-column CSV download file containing 
#' the search results. 
#' 
#' @details 
#' NOTE: This was designed to work off small queries and could download
#' huge files for large query terms...this has not been tested.
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' Example PubChem Text-Based Query:
#' \url{https://pubchem.ncbi.nlm.nih.gov/#query=coronavirus}
#' 
#' @examples
#' getPcCand.query("coronavirus","PC_CoronaQuery.csv")
#' 
#' @export
getPcCand.query <- function(query, file_name="") {
  # set file_name if not set...
  if (nchar(file_name) < 4) {
    file_name <- paste0("PubChem_Candidates_",query,".csv")
  }
  
  baseURL <- 'https://pubchem.ncbi.nlm.nih.gov/sdq/sdqagent.cgi?query={"download":"*","collection":"compound","where":{"ands":{"*":"'
  endURL <- '"}},"start":1,"limit":100000}'
  url <- paste0(baseURL, query, endURL)
  download.file(url,file_name)
  return(file_name)
}


Emma Schymanski's avatar
Emma Schymanski committed
643 644 645 646 647 648
#' Retrieve Information for Mass Spectral Data Processing from CIDs
#' 
#' Retrieve various pieces of information for mass spectral screening, 
#' by CID, via parent CID mappings. Thanks to Paul Thiessen 
#' and Evan Bolton from PubChem team for assistance. 
#' 
Emma Schymanski's avatar
Emma Schymanski committed
649
#' @usage getMSInfo.cid(cid,useParent=TRUE)
Emma Schymanski's avatar
Emma Schymanski committed
650 651
#' 
#' @param cid CID to retrieve information
Emma Schymanski's avatar
Emma Schymanski committed
652 653
#' @param useParent Default \code{TRUE} searches and returns info for
#' parent CID. If \code{FALSE}, retrieves on \code{cid}.
Emma Schymanski's avatar
Emma Schymanski committed
654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671
#' @return list containing the mass spectral screening information. 
#' 
#' @details 
#' This currently relies on functionality in RChemMass. 
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' @seealso \code{\link{getMSInfo.files}} \code{\link{getMSInfo.cids}}
#' 
#' @examples
#' getMSInfo.cid(2256)
#' # generates NAs when invalid CIDs are given
#' getMSInfo.cid("blah")
#' 
#' @export
Emma Schymanski's avatar
Emma Schymanski committed
672
getMSInfo.cid <- function(cid,useParent=TRUE) {
Emma Schymanski's avatar
Emma Schymanski committed
673
  Input_CID <- cid
Emma Schymanski's avatar
Emma Schymanski committed
674 675 676 677 678 679
  if (useParent) {
    Parent_CID <- getPCIDs.CIDtype(cid,type = "parent",timeout=60)
  } else {
    Parent_CID <- cid
  }
  
Emma Schymanski's avatar
Emma Schymanski committed
680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705
  # get properties
  extra_info <- getPCproperty.MF(Parent_CID)
  name_info <- getPCdesc.title(Parent_CID)
  # fill in the gaps
  Name <- name_info$Title
  IUPAC_Name <- extra_info$IUPACName
  SMILES <- extra_info$IsomericSMILES
  InChI <- extra_info$InChI
  InChIKey <- extra_info$InChIKey
  MolecularFormula <- extra_info$MolecularFormula
  ExactMass <- extra_info$MonoisotopicMass
  
  MSInfo <- list()
  MSInfo[['Input_CID']] <- Input_CID
  MSInfo[['Parent_CID']] <- Parent_CID
  MSInfo[['Name']] <- Name
  MSInfo[['IUPAC_Name']] <- IUPAC_Name
  MSInfo[['SMILES']] <- SMILES
  MSInfo[['InChI']] <- InChI
  MSInfo[['InChIKey']] <- InChIKey
  MSInfo[['MolecularFormula']] <- MolecularFormula
  MSInfo[['ExactMass']] <- ExactMass
  return(MSInfo)
}


Emma Schymanski's avatar
Emma Schymanski committed
706

Emma Schymanski's avatar
Emma Schymanski committed
707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730
#' Retrieve Information for Mass Spectral Data Processing from CIDs
#' 
#' Retrieve various pieces of information for mass spectral screening, 
#' by CID, via parent CID mappings. Thanks to Paul Thiessen 
#' and Evan Bolton from PubChem team for assistance. 
#' 
#' @usage getMSInfo.cids(cids,file_name)
#' 
#' @param cids Vector of CIDs to retrieve information
#' @param file_name File name to save the resulting information
#' @return file_name of the resulting multi-column CSV containing the 
#' mass spectral screening information. 
#' 
#' @details 
#' NOTE: This function goes through two series of web retrievals and
#' can take a while to run (several minutes for tens of CIDs)
#' Future improvement will be to retrieve by entire list not 1 by 1
#' This currently relies on functionality in RChemMass. 
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
Emma Schymanski's avatar
Emma Schymanski committed
731
#' @seealso \code{\link{getMSInfo.files}} \code{\link{getMSInfo.cid}}
Emma Schymanski's avatar
Emma Schymanski committed
732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814
#' 
#' @examples
#' test_cids <- c(2256, 124886, 70292631, 136096020, 136114911, NA)
#' # the last 3 cause various problems and have been included deliberately
#' test_file <- getMSInfo.cids(test_cids,"test_MSinfo.csv")
#' 
#' @export
getMSInfo.cids <- function(cids,file_name) {
  n_cids <- length(cids)
  Input_CID <- vector(mode="character",length=n_cids)
  Parent_CID <- vector(mode="character",length=n_cids)
  Name <- vector(mode="character",length=n_cids)
  IUPAC_Name <- vector(mode="character",length=n_cids)
  SMILES <- vector(mode="character",length=n_cids)
  InChI <- vector(mode="character",length=n_cids)
  InChIKey <- vector(mode="character",length=n_cids)
  MolecularFormula <- vector(mode="character",length=n_cids)
  ExactMass <- vector(mode="character",length=n_cids)
  
  for (i in 1:n_cids) {
    # save input
    Input_CID[i] <- cids[i]
    # get parent CID
    Parent_CID[i] <- getPCIDs.CIDtype(cids[i],type = "parent",timeout=60)
    # get properties
    extra_info <- getPCproperty.MF(Parent_CID[i])
    name_info <- getPCdesc.title(Parent_CID[i])
    # fill in the gaps
    Name[i] <- name_info$Title
    IUPAC_Name[i] <- extra_info$IUPACName
    SMILES[i] <- extra_info$IsomericSMILES
    InChI[i] <- extra_info$InChI
    InChIKey[i] <- extra_info$InChIKey
    MolecularFormula[i] <- extra_info$MolecularFormula
    ExactMass[i] <- extra_info$MonoisotopicMass
  }
  MSinfo <- cbind(Input_CID, Parent_CID, Name, IUPAC_Name, SMILES, InChI, InChIKey,
                  MolecularFormula, ExactMass)
  write.csv(MSinfo, file_name ,row.names = F)
  return(file_name)
}



#' Format getMSInfo.cids files to input files for Shinyscreen
#' 
#' Format the getMSInfo.cids output to create input files for 
#' Shinyscreen (compound list, set list) plus a suspect file. 
#' 
#' @usage getMSInfo.files(file_name, set_id,min_mass=50,id_offset=0)
#' 
#' @param file_name File name from \code{getMSInfo.cids}
#' @param set_id The name of the set ID for Shinyscreen. Keep short and 
#' informative, e.g. \code{"atrazine_tps"}
#' @param min_mass The minimum mass to include in the suspect screening. 
#' Default \code{50}; increase to \code{100} if only MS/MS is run 
#' at \code{mz=100-1000}
#' @param id_offset This can be used to adjust the ID number assigned
#' automatically (default starts at 1)
#' @return Silently returns three files named systematically off \code{file_name} 
#' 
#' @details 
#' NOTE: For maximum convenience, run this function directly in future 
#' Shinyscreen dir. 
#' 
#' @author Emma Schymanski <emma.schymanski@@uni.lu>
#' 
#' @references 
#' PubChem: \url{http://pubchem.ncbi.nlm.nih.gov/} 
#' 
#' @seealso \code{\link{getMSInfo.cids}}
#' 
#' @examples
#' test_cids <- c(2256, 124886, 70292631, 136096020, 136114911, NA)
#' # the last 3 cause various problems and have been included deliberately
#' test_file <- getMSInfo.cids(test_cids,"test_MSinfo.csv")
#' getMSInfo.files("test_MSinfo.csv",set_id="atrazine_tp_subset")
#' 
#' @export
getMSInfo.files <- function(file_name, set_id,min_mass=50,id_offset=0) {
  #this takes the output from getMSInfo.cids and creates files for Shinyscreen
  MS_info <- read.csv(file_name,stringsAsFactors = F)
  #check which are NAs - these are only ions - and remove
Emma Schymanski's avatar
Emma Schymanski committed
815 816 817 818
  check_nas <- which(is.na(MS_info$Parent_CID))
  if (length(check_nas)>0) {
    MS_info <- MS_info[-which(is.na(MS_info$Parent_CID)),]
  }
Emma Schymanski's avatar
Emma Schymanski committed
819 820
  #remove duplicates
  MS_info <- unique(MS_info)
Emma Schymanski's avatar
Emma Schymanski committed
821
  # remove duplicate parent CIDs that had different source CIDs
Emma Schymanski's avatar
Emma Schymanski committed
822 823 824 825
  dup_CID_test <- which(duplicated(MS_info$Parent_CID))
  if (length(dup_CID_test)>0) {
    MS_info <- MS_info[-which(duplicated(MS_info$Parent_CID)),]
  }
Emma Schymanski's avatar
Emma Schymanski committed
826
  #remove those below mz=min_mass ... 
Emma Schymanski's avatar
Emma Schymanski committed
827 828 829 830
  mass_test <- which(as.numeric(MS_info$ExactMass)>min_mass)
  if (length(mass_test)>0) {
    MS_info <- MS_info[mass_test,]
  }
Emma Schymanski's avatar
Emma Schymanski committed
831
  #remove any SMILES with wildcards
Emma Schymanski's avatar
Emma Schymanski committed
832 833 834 835
  star_smiles <- grep("*",MS_info$SMILES,fixed=T)
  if (length(star_smiles)>0) {
    MS_info <- MS_info[-grep("*",MS_info$SMILES,fixed=T),]
  }
Emma Schymanski's avatar
Emma Schymanski committed
836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854
  # transform into compound list for ShinyScreen
  # minimum fields ID (4-digit), Name, SMILES, RT (can be empty)
  MS_info$ID <- sequence(length(MS_info$Parent_CID))+id_offset
  MS_info$RT <- ""
  MS_info$set <- set_id
  suspect_filename <- paste0(set_id, "_SuspectList.csv")
  cmpdList_filename <- paste0(set_id, "_CmpdList.csv")
  set_filename <- paste0(set_id, "_Set.csv")
  #output CSV with all info
  write.csv(MS_info,suspect_filename,row.names = F)
  # output compound list for ShinyScreen
  #ID, Name, SMILES, RT
  write.csv(MS_info[,c(10,3,5,11)],cmpdList_filename,row.names = F)
  #create set file
  # ID, set_id
  write.csv(MS_info[,c(10,12)], set_filename,row.names = F)
}


Emma Schymanski's avatar
Emma Schymanski committed
855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881
# Create Transformations table entries from HSDB Directory
createTransFile.hsdb <- function(hsdb_dir, hsdb_structure_file, file_name="",
                                 hsdb_desc="", dataset_DOI="",dataset_desc="",
                                 MSInfo=FALSE) {
  # set the default output file name: 
  if (nchar(file_name) < 4) {
    file_name <- "S68_HSDBTPS_TransformationTable.csv"
  }
  # note: if MSInfo=TRUE, can go off the MSInfo file not the structure file
  
  # set the default descriptions
  if (nchar(hsdb_desc) < 4) {
    hsdb_desc <- paste0("HSDB is a toxicology database that focuses on the ",
                        "toxicology of potentially hazardous chemicals. See ",
                        "https://pubchem.ncbi.nlm.nih.gov/source/11933")
  }
  
  if (nchar(dataset_desc) < 4) {
    dataset_desc <- "Transformation Products extracted from HSDB content in PubChem, validated by LCSB-ECI"
  }
  
  if (nchar(dataset_DOI) < 4) {
    dataset_DOI <- "10.5281/zenodo.3827487"
  }
  
  # get the file listing
  hsdb_files <- list.files(hsdb_dir,pattern="selected.csv",full.names = T)
Emma Schymanski's avatar
Emma Schymanski committed
882
  hsdb_all_tp_info <- ""
Emma Schymanski's avatar
Emma Schymanski committed
883
  
Emma Schymanski's avatar
Emma Schymanski committed
884
  for (i in 1:length(hsdb_files)) {
Emma Schymanski's avatar
Emma Schymanski committed
885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921
    
    hsdb_selected_file <- hsdb_files[i]
    hsdb_info <- read.csv(hsdb_selected_file, stringsAsFactors = F)
    tp_cids <- getHSDBCIDs.selected(hsdb_info)
    if (length(tp_cids)>0) {
      hsdb_tp_info <- createTPinfo.hsdb(tp_cids, hsdb_selected_file, hsdb_structure_file, 
                                        hsdb_desc, dataset_DOI, dataset_desc,MSInfo)
    } else {
      #    print(paste0("No information to add for ", basename(hsdb_selected_file)))
      # run this just with the file CID, so it's on record that this was done
      hsdb_tp_info <- createTPinfo.hsdb(hsdb_info$cids[1], hsdb_selected_file, 
                                        hsdb_structure_file, 
                                        hsdb_desc, dataset_DOI, dataset_desc,MSInfo)
    }
    
    if (nchar(hsdb_all_tp_info[1]) == 0) {
      hsdb_all_tp_info <- hsdb_tp_info
    } else {
      if (length(tp_cids)>0) {
        hsdb_all_tp_info <- merge(hsdb_all_tp_info,hsdb_tp_info,all.x=T,all.y=T)
      }
    }
    
  }
  write.csv(hsdb_all_tp_info,file_name,row.names = F)
  return(file_name)
}


# This is a small helper function to get only the CIDs from 
# selected records in the manaully-checked HSDB-extracted records
getHSDBCIDs.selected <- function(hsdb_info) {
  hsdb_tp_cids <- hsdb_info$selected_tp_cids[which(hsdb_info$use==TRUE)]
  hsdb_tp_cids <- paste(hsdb_tp_cids,collapse="|")
  hsdb_tp_cids <- suppressWarnings(as.numeric(unique(strsplit(hsdb_tp_cids,"|",fixed=T)[[1]])))
  return(hsdb_tp_cids)
}
Emma Schymanski's avatar
Emma Schymanski committed
922

Emma Schymanski's avatar
Emma Schymanski committed
923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938

# a helper function to create the files for the #Transformations table
# from HSDB extracted-data. Although this is was originally set up to 
# export a csv, it now exports information for a larger function. 
createTPinfo.hsdb <- function(hsdb_cids, hsdb_selected_file, hsdb_structure_file, 
                              hsdb_desc, dataset_DOI, dataset_desc,MSInfo=FALSE) {
  # read in the information
  hsdb_info <- read.csv(hsdb_selected_file,stringsAsFactors = F)
  MS_info <- read.csv(hsdb_structure_file,stringsAsFactors = F)
  #select only those that we'll use
  hsdb_info <- hsdb_info[which(hsdb_info$use==T),]
  
  n_cids <- length(hsdb_cids)
  # initiate vectors
  Input_CID <- vector(mode="character",length=n_cids)
  Parent_CID <- vector(mode="character",length=n_cids)
Emma Schymanski's avatar
Emma Schymanski committed
939 940
  Name <- vector(mode="character",length=n_cids)
  IUPAC_Name <- vector(mode="character",length=n_cids)
Emma Schymanski's avatar
Emma Schymanski committed
941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252
  Predecessor_CID <- vector(mode="character",length=n_cids)
  Predecessor_Name <- vector(mode="character",length=n_cids)
  Successor_CID <- vector(mode="character",length=n_cids)
  Successor_Name <- vector(mode="character",length=n_cids)
  IsPredecessor <- vector(mode="logical",length=n_cids)
  Transformation <- vector(mode="character",length=n_cids)
  Source_ID <- vector(mode="character",length=n_cids)
  Source_CID <- vector(mode="character",length=n_cids)
  Source <- vector(mode="character",length=n_cids)
  Source_Description <- vector(mode="character",length=n_cids)
  Dataset_DOI <- vector(mode="character",length=n_cids)
  Dataset_Description <- vector(mode="character",length=n_cids)
  Evidence_ID <- vector(mode="character",length=n_cids)
  Evidence_Description <- vector(mode="character",length=n_cids)
  
  # take care of the starting CID. If it's in the TP list, remove.
  # we will have to treat this as a special case
  start_cid <- hsdb_info$cids[1]
  i_start_cid <- which(hsdb_cids==start_cid)
  if (length(i_start_cid)>0) {
    hsdb_cids <- hsdb_cids[-i_start_cid]
  }
  # count the number of CIDs now starting CID is removed
  n_cids <- length(hsdb_cids)
  # start the counter, the parent goes at n=1
  n <- 1
  # set the current CID to the start CID
  curr_cid <- start_cid
  #find the index of the start CID in MSInfo
  if (MSInfo) {
    i_MSInfo <- which(MS_info$Input_CID==start_cid)
  } else {
    i_MSInfo <- which(MS_info$PubChemCID==start_cid)
  }
  # if the info doesn't exist, reretrieve, otherwise, get from MS_info
  if (length(i_MSInfo)==0) {
    cid_MSInfo <- getMSInfo.cid(curr_cid)
    Input_CID[n] <- cid_MSInfo$Input_CID
    Parent_CID[n] <- cid_MSInfo$Parent_CID
    Name[n] <- cid_MSInfo$Name
    IUPAC_Name[n] <- cid_MSInfo$IUPAC_Name
  } else {
    if (MSInfo) {
      Input_CID[n] <- MS_info$Input_CID[i_MSInfo]
      Parent_CID[n] <- MS_info$Parent_CID[i_MSInfo]
    } else {
      Input_CID[n] <- MS_info$PubChemCID[i_MSInfo]
      Parent_CID[n] <- MS_info$PubChemCID[i_MSInfo]
    }
    Name[n] <- MS_info$Name[i_MSInfo]
    IUPAC_Name[n] <- MS_info$IUPAC_Name[i_MSInfo]
  }
  Predecessor_CID[n] <- Parent_CID[n]
  Predecessor_Name[n] <- Name[n]
  Successor_CID[n] <- Parent_CID[n]
  Successor_Name[n] <- Name[n]
  IsPredecessor[n] <- NA
  Transformation[n] <- "none"
  Source_ID[n] <- hsdb_info$source_ID[1]
  Source_CID[n] <- hsdb_info$cids[1]
  Source[n] <- hsdb_info$source_name[1]
  Source_Description[n] <- hsdb_desc
  Dataset_DOI[n] <- dataset_DOI
  Dataset_Description[n] <- dataset_desc
  Evidence_ID[n] <- ""
  Evidence_Description[n] <- ""
  
  # go over the rest of the TPs
  for (i in 1:n_cids) {
    # for each CID, find out which reference(s) it is in
    curr_cid <- hsdb_cids[i]
    tp_entries <- grep(curr_cid,hsdb_info$selected_tp_cids,fixed=T)
    if (length(tp_entries)>0) {
      for (j in 1:length(tp_entries)) {
        #add one more to the counter
        n <- n+1
        # get the index of the TP in hsdb_info
        i_tp_entry <- tp_entries[j]
        # get the index of the TP in MS_info
        if (MSInfo) {
          i_MSInfo <- which(MS_info$Input_CID==curr_cid)
        } else {
          i_MSInfo <- which(MS_info$PubChemCID==curr_cid)
        }
        #i_MSInfo <- which(MS_info$Input_CID==curr_cid)
        # if the value is not found, reretrieve the info, otherwise, save
        if (length(i_MSInfo)==0) {
          cid_MSInfo <- getMSInfo.cid(curr_cid)
          Input_CID[n] <- cid_MSInfo$Input_CID
          Parent_CID[n] <- cid_MSInfo$Parent_CID
          Name[n] <- cid_MSInfo$Name
          IUPAC_Name[n] <- cid_MSInfo$IUPAC_Name
        } else {
          if (MSInfo) {
            Input_CID[n] <- MS_info$Input_CID[i_MSInfo]
            Parent_CID[n] <- MS_info$Parent_CID[i_MSInfo]
          } else {
            Input_CID[n] <- MS_info$PubChemCID[i_MSInfo]
            Parent_CID[n] <- MS_info$PubChemCID[i_MSInfo]
          }
          Name[n] <- MS_info$Name[i_MSInfo]
          IUPAC_Name[n] <- MS_info$IUPAC_Name[i_MSInfo]
        }
        if (hsdb_info$isPredecessor[i_tp_entry]) {
          # if the starting CID is predecessor, this entry is a TP
          Predecessor_CID[n] <- Parent_CID[1]
          Predecessor_Name[n] <- Name[1]
          Successor_CID[n] <- Parent_CID[n]
          Successor_Name[n] <- Name[n]
          IsPredecessor[n] <- FALSE
        } else {
          # otherwise this entry is the predecessor
          Predecessor_CID[n] <- Parent_CID[n]
          Predecessor_Name[n] <- Name[n]
          Successor_CID[n] <- Parent_CID[1]
          Successor_Name[n] <- Name[1]
          IsPredecessor[n] <- TRUE
        }
        Transformation[n] <- hsdb_info$transformation[i_tp_entry]
        Source_ID[n] <- hsdb_info$source_ID[1]
        Source_CID[n] <- hsdb_info$cids[1]
        Source[n] <- hsdb_info$source_name[1]
        Source_Description[n] <- hsdb_desc
        Dataset_DOI[n] <- dataset_DOI
        Dataset_Description[n] <- dataset_desc
        Evidence_ID[n] <- ""
        Evidence_Description[n] <- hsdb_info$ref_text[i_tp_entry]
      }
    }
  }
  #PubChem_CID <- Parent_CID
  #Source_CID <- Input_CID
  # create the format for saving
  tp_info <- cbind(#Name, IUPAC_Name, PubChem_CID, SMILES, InChI, InChIKey,
    #MolecularFormula, ExactMass, Source_CID, 
    Predecessor_CID, Predecessor_Name,
    Successor_CID, Successor_Name, #IsPredecessor, 
    Transformation, Source_ID, Source_CID,
    Source, Source_Description, Dataset_DOI, Dataset_Description, 
    Evidence_ID, Evidence_Description)
  #remove duplicate entries
  tp_info <- unique(tp_info)
  #remove NAs if there was no parent CID
  na_entries <- which(is.na(tp_info[,2]))
  if (length(na_entries)>0) {
    tp_info <- tp_info[-which(is.na(tp_info[,2])),]
  }
  # #write the file and return the file name. 
  # write.csv(tp_info, file_name ,row.names = F)
  # return(file_name)
  return(tp_info)
  
}


# # superceded function version, kept for the record. 
# # clean out once other version is thoroughly tested
# createTPinfo.hsdb <- function(hsdb_cids, hsdb_selected_file, MSInfo_file, hsdb_desc, 
#                               dataset_DOI, dataset_desc,file_name="") {
#   # if no filename, autocreate from hsdb_selected_file
#   if (nchar(file_name) < 4) {
#     file_name <- sub(".csv","_allInfo.csv",hsdb_selected_file)
#   }
#   
#   # read in the information
#   hsdb_info <- read.csv(hsdb_selected_file,stringsAsFactors = F)
#   MS_info <- read.csv(MSInfo_file,stringsAsFactors = F)
#   #select only those that we'll use
#   hsdb_info <- hsdb_info[which(hsdb_info$use==T),]
#   
#   n_cids <- length(hsdb_cids)
#   # initiate vectors
#   Input_CID <- vector(mode="character",length=n_cids)
#   Parent_CID <- vector(mode="character",length=n_cids)
#   Predecessor_CID <- vector(mode="character",length=n_cids)
#   Predecessor_Name <- vector(mode="character",length=n_cids)
#   Successor_CID <- vector(mode="character",length=n_cids)
#   Successor_Name <- vector(mode="character",length=n_cids)
#   IsPredecessor <- vector(mode="logical",length=n_cids)
#   Transformation <- vector(mode="character",length=n_cids)
#   Source_ID <- vector(mode="character",length=n_cids)
#   Source_CID <- vector(mode="character",length=n_cids)
#   Source <- vector(mode="character",length=n_cids)
#   Source_Description <- vector(mode="character",length=n_cids)
#   Dataset_DOI <- vector(mode="character",length=n_cids)
#   Dataset_Description <- vector(mode="character",length=n_cids)
#   Evidence_ID <- vector(mode="character",length=n_cids)
#   Evidence_Description <- vector(mode="character",length=n_cids)
#   
#   # take care of the starting CID. If it's in the TP list, remove.
#   # we will have to treat this as a special case
#   start_cid <- hsdb_info$cids[1]
#   i_start_cid <- which(hsdb_cids==start_cid)
#   if (length(i_start_cid)>0) {
#     hsdb_cids <- hsdb_cids[-i_start_cid]
#   }
#   # count the number of CIDs now starting CID is removed
#   n_cids <- length(hsdb_cids)
#   # start the counter, the parent goes at n=1
#   n <- 1
#   # set the current CID to the start CID
#   curr_cid <- start_cid
#   #find the index of the start CID in MSInfo
#   i_MSInfo <- which(MS_info$Input_CID==start_cid)
#   # if the info doesn't exist, reretrieve, otherwise, get from MS_info
#   if (length(i_MSInfo)==0) {
#     cid_MSInfo <- getMSInfo.cid(curr_cid)
#     Input_CID[n] <- cid_MSInfo$Input_CID
#     Parent_CID[n] <- cid_MSInfo$Parent_CID
#     Name[n] <- cid_MSInfo$Name
#     IUPAC_Name[n] <- cid_MSInfo$IUPAC_Name
#   } else {
#     Input_CID[n] <- MS_info$Input_CID[i_MSInfo]
#     Parent_CID[n] <- MS_info$Parent_CID[i_MSInfo]
#     Name[n] <- MS_info$Name[i_MSInfo]
#     IUPAC_Name[n] <- MS_info$IUPAC_Name[i_MSInfo]
#   }
#   Predecessor_CID[n] <- Parent_CID[n]
#   Predecessor_Name[n] <- Name[n]
#   Successor_CID[n] <- Parent_CID[n]
#   Successor_Name[n] <- Name[n]
#   IsPredecessor[n] <- NA
#   Transformation[n] <- "none"
#   Source_ID[n] <- hsdb_info$source_ID[1]
#   Source_CID[n] <- hsdb_info$cids[1]
#   Source[n] <- hsdb_info$source_name[1]
#   Source_Description[n] <- hsdb_desc
#   Dataset_DOI[n] <- dataset_DOI
#   Dataset_Description[n] <- dataset_desc
#   Evidence_ID[n] <- ""
#   Evidence_Description[n] <- ""
#   
#   # go over the rest of the TPs
#   for (i in 1:n_cids) {
#     # for each CID, find out which reference(s) it is in
#     curr_cid <- hsdb_cids[i]
#     tp_entries <- grep(curr_cid,hsdb_info$selected_tp_cids,fixed=T)
#     if (length(tp_entries)>0) {
#       for (j in 1:length(tp_entries)) {
#         #add one more to the counter
#         n <- n+1
#         # get the index of the TP in hsdb_info
#         i_tp_entry <- tp_entries[j]
#         # get the index of the TP in MS_info
#         i_MSInfo <- which(MS_info$Input_CID==curr_cid)
#         # if the value is not found, reretrieve the info, otherwise, save
#         if (length(i_MSInfo)==0) {
#           cid_MSInfo <- getMSInfo.cid(curr_cid)
#           Input_CID[n] <- cid_MSInfo$Input_CID
#           Parent_CID[n] <- cid_MSInfo$Parent_CID
#           Name[n] <- cid_MSInfo$Name
#           IUPAC_Name[n] <- cid_MSInfo$IUPAC_Name
#         } else {
#           Input_CID[n] <- MS_info$Input_CID[i_MSInfo]
#           Parent_CID[n] <- MS_info$Parent_CID[i_MSInfo]
#           Name[n] <- MS_info$Name[i_MSInfo]
#           IUPAC_Name[n] <- MS_info$IUPAC_Name[i_MSInfo]
#         }
#         if (hsdb_info$isPredecessor[i_tp_entry]) {
#           # if the starting CID is predecessor, this entry is a TP
#           Predecessor_CID[n] <- Parent_CID[1]
#           Predecessor_Name[n] <- Name[1]
#           Successor_CID[n] <- Parent_CID[n]
#           Successor_Name[n] <- Name[n]
#           IsPredecessor[n] <- FALSE
#         } else {
#           # otherwise this entry is the predecessor
#           Predecessor_CID[n] <- Parent_CID[n]
#           Predecessor_Name[n] <- Name[n]
#           Successor_CID[n] <- Parent_CID[1]
#           Successor_Name[n] <- Name[1]
#           IsPredecessor[n] <- TRUE
#         }
#         Transformation[n] <- hsdb_info$transformation[i_tp_entry]
#         Source_ID[n] <- hsdb_info$source_ID[1]
#         Source_CID[n] <- hsdb_info$cids[1]
#         Source[n] <- hsdb_info$source_name[1]
#         Source_Description[n] <- hsdb_desc
#         Dataset_DOI[n] <- dataset_DOI
#         Dataset_Description[n] <- dataset_desc
#         Evidence_ID[n] <- ""
#         Evidence_Description[n] <- hsdb_info$ref_text[i_tp_entry]
#       }
#     }
#   }
#   #PubChem_CID <- Parent_CID
#   #Source_CID <- Input_CID
#   # create the format for saving
#   tp_info <- cbind(#Name, IUPAC_Name, PubChem_CID, SMILES, InChI, InChIKey,
#     #MolecularFormula, ExactMass, Source_CID, 
#     Predecessor_CID, Predecessor_Name,
#     Successor_CID, Successor_Name, #IsPredecessor, 
#     Transformation, Source_ID, Source_CID,
#     Source, Source_Description, Dataset_DOI, Dataset_Description, 
#     Evidence_ID, Evidence_Description)
#   #remove duplicate entries
#   tp_info <- unique(tp_info)
#   #remove NAs if there was no parent CID
#   na_entries <- which(is.na(tp_info[,2]))
#   if (length(na_entries)>0) {
#     tp_info <- tp_info[-which(is.na(tp_info[,2])),]
#   }
#   # #write the file and return the file name. 
#   # write.csv(tp_info, file_name ,row.names = F)
#   # return(file_name)
#   return(tp_info)
# }




#NOTE: This function is now superceded by createTPinfo.hsdb
Emma Schymanski's avatar
Emma Schymanski committed
1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394
createFiles.trans <- function(hsdb_cids, hsdb_selected_file, MSInfo_file, hsdb_desc, 
                              dataset_DOI, dataset_desc,file_name="",
                              startCIDisPredecessor=TRUE) {
  # if no filename, autocreate from hsdb_selected_file
  if (nchar(file_name) < 4) {
    file_name <- sub(".csv","_allInfo.csv",hsdb_selected_file)
  }
  
  # read in the information
  hsdb_info <- read.csv(hsdb_selected_file,stringsAsFactors = F)
  MS_info <- read.csv(MSInfo_file,stringsAsFactors = F)
  #select only those that we'll use
  hsdb_info <- hsdb_info[which(hsdb_info$use==T),]
  
  n_cids <- length(hsdb_cids)
  # initiate vectors
  Input_CID <- vector(mode="character",length=n_cids)
  Parent_CID <- vector(mode="character",length=n_cids)
  Name <- vector(mode="character",length=n_cids)
  IUPAC_Name <- vector(mode="character",length=n_cids)
  SMILES <- vector(mode="character",length=n_cids)
  InChI <- vector(mode="character",length=n_cids)
  InChIKey <- vector(mode="character",length=n_cids)
  MolecularFormula <- vector(mode="character",length=n_cids)
  ExactMass <- vector(mode="character",length=n_cids)
  Predecessor_CID <- vector(mode="character",length=n_cids)
  Successor_CID <- vector(mode="character",length=n_cids)
  IsPredecessor <- vector(mode="logical",length=n_cids)
  Transformation <- vector(mode="character",length=n_cids)
  Source <- vector(mode="character",length=n_cids)
  Source_Description <- vector(mode="character",length=n_cids)
  Dataset_DOI <- vector(mode="character",length=n_cids)
  Dataset_Description <- vector(mode="character",length=n_cids)
  Evidence_DOI <- vector(mode="character",length=n_cids)
  Evidence_Description <- vector(mode="character",length=n_cids)
  
  # take care of the starting CID. If it's in the TP list, remove. 
  # we will have to treat this as a special case
  start_cid <- hsdb_info$cids[1]
  i_start_cid <- which(hsdb_cids==start_cid)
  if (length(i_start_cid)>0) {
    hsdb_cids <- hsdb_cids[-i_start_cid]
  }
  # count the number of CIDs now starting CID is removed
  n_cids <- length(hsdb_cids)
  # start the counter, the parent goes at n=1
  n <- 1
  # set the current CID to the start CID
  curr_cid <- start_cid
  #find the index of the start CID in MSInfo
  i_MSInfo <- which(MS_info$Input_CID==start_cid)
  # if the info doesn't exist, reretrieve, otherwise, get from MS_info
  if (length(i_MSInfo)==0) {
    cid_MSInfo <- getMSInfo.cid(curr_cid)
    Input_CID[n] <- cid_MSInfo$Input_CID
    Parent_CID[n] <- cid_MSInfo$Parent_CID
    Name[n] <- cid_MSInfo$Name
    IUPAC_Name[n] <- cid_MSInfo$IUPAC_Name
    SMILES[n] <- cid_MSInfo$SMILES
    InChI[n] <- cid_MSInfo$InChI
    InChIKey[n] <- cid_MSInfo$InChIKey
    MolecularFormula[n] <- cid_MSInfo$MolecularFormula
    ExactMass[n] <- cid_MSInfo$ExactMass
  } else {
    Input_CID[n] <- MS_info$Input_CID[i_MSInfo]
    Parent_CID[n] <- MS_info$Parent_CID[i_MSInfo]
    Name[n] <- MS_info$Name[i_MSInfo]
    IUPAC_Name[n] <- MS_info$IUPAC_Name[i_MSInfo]
    SMILES[n] <- MS_info$SMILES[i_MSInfo]
    InChI[n] <- MS_info$InChI[i_MSInfo]
    InChIKey[n] <- MS_info$InChIKey[i_MSInfo]
    MolecularFormula[n] <- MS_info$MolecularFormula[i_MSInfo]
    ExactMass[n] <- MS_info$ExactMass[i_MSInfo]
  }
  Predecessor_CID[n] <- Parent_CID[n]
  Successor_CID[n] <- Parent_CID[n]
  IsPredecessor[n] <- startCIDisPredecessor
  Transformation[n] <- "none"
  Source[n] <- hsdb_info$source_name[1]
  Source_Description[n] <- hsdb_desc
  Dataset_DOI[n] <- dataset_DOI
  Dataset_Description[n] <- dataset_desc
  Evidence_DOI[n] <- ""
  Evidence_Description[n] <- ""
  
  # go over the rest of the TPs
  for (i in 1:n_cids) {
    # for each CID, find out which reference(s) it is in
    curr_cid <- hsdb_cids[i]
    tp_entries <- grep(curr_cid,hsdb_info$selected_tp_cids,fixed=T)
    if (length(tp_entries)>0) {
      for (j in 1:length(tp_entries)) {
        #add one more to the counter
        n <- n+1
        # get the index of the TP in hsdb_info
        i_tp_entry <- tp_entries[j]
        # get the index of the TP in MS_info
        i_MSInfo <- which(MS_info$Input_CID==curr_cid)
        # if the value is not found, reretrieve the info, otherwise, save
        if (length(i_MSInfo)==0) {
          cid_MSInfo <- getMSInfo.cid(curr_cid)
          Input_CID[n] <- cid_MSInfo$Input_CID
          Parent_CID[n] <- cid_MSInfo$Parent_CID
          Name[n] <- cid_MSInfo$Name
          IUPAC_Name[n] <- cid_MSInfo$IUPAC_Name
          SMILES[n] <- cid_MSInfo$SMILES
          InChI[n] <- cid_MSInfo$InChI
          InChIKey[n] <- cid_MSInfo$InChIKey
          MolecularFormula[n] <- cid_MSInfo$MolecularFormula
          ExactMass[n] <- cid_MSInfo$ExactMass
        } else {
          Input_CID[n] <- MS_info$Input_CID[i_MSInfo]
          Parent_CID[n] <- MS_info$Parent_CID[i_MSInfo]
          Name[n] <- MS_info$Name[i_MSInfo]
          IUPAC_Name[n] <- MS_info$IUPAC_Name[i_MSInfo]
          SMILES[n] <- MS_info$SMILES[i_MSInfo]
          InChI[n] <- MS_info$InChI[i_MSInfo]
          InChIKey[n] <- MS_info$InChIKey[i_MSInfo]
          MolecularFormula[n] <- MS_info$MolecularFormula[i_MSInfo]
          ExactMass[n] <- MS_info$ExactMass[i_MSInfo]
        }
        if (hsdb_info$isPredecessor[i_tp_entry]) {
          # if the starting CID is predecessor, this entry is a TP
          Predecessor_CID[n] <- Parent_CID[1]
          Successor_CID[n] <- Parent_CID[n]
          IsPredecessor[n] <- FALSE
        } else {
          # otherwise this entry is the predecessor
          Predecessor_CID[n] <- Parent_CID[n]
          Successor_CID[n] <- Parent_CID[1]
          IsPredecessor[n] <- TRUE
        }
        Transformation[n] <- hsdb_info$transformation[i_tp_entry]
        Source[n] <- hsdb_info$source_name[1]
        Source_Description[n] <- hsdb_desc
        Dataset_DOI[n] <- dataset_DOI
        Dataset_Description[n] <- dataset_desc
        Evidence_DOI[n] <- ""
        Evidence_Description[n] <- hsdb_info$ref_text[i_tp_entry]
      }
    }
  }
Emma Schymanski's avatar
Emma Schymanski committed
1395 1396
  PubChem_CID <- Parent_CID
  Source_CID <- Input_CID
Emma Schymanski's avatar
Emma Schymanski committed
1397
  # create the format for saving
Emma Schymanski's avatar
Emma Schymanski committed
1398 1399 1400
  tp_info <- cbind(Name, IUPAC_Name, PubChem_CID, SMILES, InChI, InChIKey,
                   MolecularFormula, ExactMass, Source_CID, 
                   Predecessor_CID, Successor_CID, #IsPredecessor, 
Emma Schymanski's avatar
Emma Schymanski committed
1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416
                   Transformation, Source, Source_Description, 
                   Dataset_DOI, Dataset_Description, Evidence_DOI, Evidence_Description)
  #remove duplicate entries
  tp_info <- unique(tp_info)
  #remove NAs if there was no parent CID
  na_entries <- which(is.na(tp_info[,2]))
  if (length(na_entries)>0) {
    tp_info <- tp_info[-which(is.na(tp_info[,2])),]
  }
  #write the file and return the file name. 
  write.csv(tp_info, file_name ,row.names = F)
  return(file_name)
  
}


Emma Schymanski's avatar
Emma Schymanski committed
1417

Emma Schymanski's avatar
Emma Schymanski committed
1418 1419 1420
# Missing:
# a function to merge CIDs by source ID - CID mappings
# a function to retrieve source ID / CIDs for data sources with substance depositions
Emma Schymanski's avatar
Emma Schymanski committed
1421 1422
# TODO:
# rewrite getMSInfo.cids to depend on getMSInfo.cid