CODEWITHSUNDEEP
rxmlxpathpubmedrentrez
Shirley
Shirley

Reputation: 147

Calculating number of xmlchildren under each parent node for a list in R

I am querying PubMED with a long list of PMIDs using R. Because entrez_fetch can only do a certain number at a time, I have broken down my ~2000 PMIDs into one list with several vectors (each about 500 in length). When I query PubMED, I am extracting information from XML files for each publication. What I would like to have in the end is something like this:

    Original.PMID     Publication.type
    26956987          Journal.article
    26956987          Meta.analysis
    26956987          Multicenter.study
    26402000          Journal.article
    25404043          Journal.article
    25404043          Meta.analysis

Each publication has a unique PMID but there may be several publication types associated with each PMID (as seen above). I can query the PMID number from the XML file, and I can get the publication types of each PMID. What I have problems with is repeating the PMID x number of times so that each PMID is associated with each of the publication type it has. I am able to do this if I don't have my data in a list with multiple sublists (e.g., if I have 14 batches, each as its own data frame) by getting the number of children nodes from the parent PublicationType node. But I can't seem to figure out how to do this for within a list.

My code so far is this:

library(rvest)
library(tidyverse)
library(stringr)
library(regexr)
library(rentrez)
library(XML)

pubmed<-my.data.frame

into.batches<-function(x,n) split(x,cut(seq_along(x),n,labels=FALSE))
batches<-into.batches(pubmed.fwd$PMID, 14)
headings<-lapply(1:14, function(x) {paste0("Batch",x)})
names(batches)<-headings
fwd<-sapply(batches, function(x) entrez_fetch(db="pubmed", id=x, rettype="xml", parsed=TRUE))
trial1<-lapply(fwd, function(x) 
  list(pub.type = xpathSApply(x, "//PublicationTypeList/PublicationType", xmlValue),
  or.pmid = xpathSApply(x, "//ArticleId[@IdType='pubmed']", xmlValue)))

trial1 is what I am having problems with. This gives me a list where within each Batch, I have a vector for pub.type and a vector for or.pmid but they're different lengths.

I am trying to figure out how many children publication types there are for each publication, so I can repeat the PMID that many number of times. I am currently using the following code which does not do what I want:

trial1<-lapply(fwd, function(x) 
  list(childnodes = xpathSApply(xmlRoot(x), "count(.//PublicationTypeList/PublicationType)", xmlChildren)))

Unfortunately, this just tells me the total number of children nodes for each batch, not for each publication (or pmid).

Upvotes: 1

Views: 699

Answers (2)

Chris S.
Chris S.

Reputation: 2225

I would split the XML results into separate Article nodes and apply xpath functions to get pmids and pubtypes.

pmids <- c(11677608, 22328765 ,11337471)
res <- entrez_fetch(db="pubmed", rettype="xml", id = pmids)
doc <- xmlParse(res)
x <-  getNodeSet(doc, "//PubmedArticle")
x1 <- sapply(x, xpathSApply, ".//ArticleId[@IdType='pubmed']", xmlValue)
x2 <- sapply(x, xpathSApply, ".//PublicationType", xmlValue)
data.frame( pmid= rep(x1, sapply(x2, length) ), pubtype = unlist(x2) )
      pmid                          pubtype
1 11677608                  Journal Article
2 11677608 Research Support, Non-U.S. Gov't
3 22328765                  Journal Article
4 22328765 Research Support, Non-U.S. Gov't
5 11337471                  Journal Article

Also, NCBI says to use the HTTP POST method if using more than 200 UIDs. rentrez does not support POSTing, but you can run that with a few lines of code.

First, you need a vector with 1000s of Pubmed IDs (6171 from the microbial genome table)

library(readr)
x <- read_tsv( "ftp://ftp.ncbi.nih.gov/genomes/GENOME_REPORTS/prokaryotes.txt", 
                na = "-", quote = "")
ids <- unique( x$`Pubmed ID` )
ids <- ids[ids < 1e9 & !is.na(ids)]

Post the ids to NCBI using httr POST.

uri = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/epost.fcgi?"
response <- httr::POST(uri, body= list(id = paste(ids, collapse=","), db = "pubmed"))

Parse the results following the code in entrez_post to get the web history.

 doc  <-   xmlParse( httr::content(response, as="text", encoding="UTF-8") )
 result <- xpathApply(doc, "/ePostResult/*", xmlValue)
 names(result) <- c("QueryKey", "WebEnv")
 class(result) <- c("web_history", "list")

Finally, fetch up to 10K records (or loop through using the retstart option if you have more than 10K)

res <- entrez_fetch(db="pubmed", rettype="xml", web_history=result)
doc <- xmlParse(res)

These only take a second to run on my laptop.

articles <- getNodeSet(doc, "//PubmedArticle")
x1 <- sapply(articles, xpathSApply, ".//ArticleId[@IdType='pubmed']", xmlValue)
x2 <- sapply(articles, xpathSApply, ".//PublicationType", xmlValue)

data_frame( pmid= rep(x1, sapply(x2, length) ), pubtype = unlist(x2) )
# A tibble: 9,885 × 2
       pmid                                  pubtype
      <chr>                                    <chr>
 1 11677608                          Journal Article
 2 11677608         Research Support, Non-U.S. Gov't
 3 12950922                          Journal Article
 4 12950922         Research Support, Non-U.S. Gov't
 5 22328765                          Journal Article
 ...

And one last comment. If you want one row per article, I usually create a function that combines multiple tags into a delimited list and adds NAs for missing nodes. 

xpath2 <-function(x, ...){
    y <- xpathSApply(x, ...)
    ifelse(length(y) == 0, NA,  paste(y, collapse="; "))
}

data_frame( pmid = sapply(articles, xpath2, ".//ArticleId[@IdType='pubmed']", xmlValue),
            journal = sapply(articles, xpath2, ".//Journal/Title", xmlValue),
           pubtypes = sapply(articles, xpath2, ".//PublicationType", xmlValue))

# A tibble: 6,172 × 3
      pmid                 journal                                          pubtypes
     <chr>                   <chr>                                             <chr>
1 11677608                  Nature Journal Article; Research Support, Non-U.S. Gov't
2 12950922  Molecular microbiology Journal Article; Research Support, Non-U.S. Gov't
3 22328765 Journal of bacteriology Journal Article; Research Support, Non-U.S. Gov't
4 11337471         Genome research                                   Journal Article
...

Upvotes: 3

Parfait
Parfait

Reputation: 107687

Since likely ArticleId is unique for each article and PublicationType may be more than one per article, consider iteratively creating dataframes instead of separate vectors.

Specifically, use node indexing, [#], across each PubmedArticle node of XML doc since this is the shared ancestor of id and type, then xpath to needed descendants. Below creates a list of dataframes of equal length to fwd:

trial1 <- lapply(fwd, function(doc) {
  # RETRIEVE NUMBER OF ARTICLES PER EACH XML
  num_of_articles <- length(xpathSApply(doc, "//PubmedArticle"))

  # LOOP THROUGH EACH ARTICLE AND BIND XML VALUES TO DATAFRAME
  dfList <- lapply(seq(num_of_articles), function(i)
    data.frame(
     Original.PMID = xpathSApply(doc, paste0("//PubmedArticle[",i,"]/descendant::ArticleId[@IdType='pubmed']"), xmlValue),
     Publication.type = xpathSApply(doc, paste0("//PubmedArticle[",i,"]/descendant::PublicationTypeList/PublicationType"), xmlValue)
  ))

  # ROW BIND ALL DFS INTO ONE
  df <- do.call(rbind, dfList)
})

For a final master dataframe across all batches, run do.call(rbind, ...) again out the loop:

finaldf <- do.call(rbind, trial1)

Upvotes: 2

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