tragula

Mapping networks of shared research expertise by word embedding analysis

A pervasive obstacle to scientific collaboration is becoming aware of your peers - those with shared research interests. Similarly, academic units (e.g., departments) are often tasked with identifing clusters of expertise.

tragula is a set of Python and R scripts for analyzing and visualizing the shared research topics among a set of authors based on their published documents (scientific abstracts). In brief, the documents are processed through standard text-mining techniques to extract informative words. These words are then embedded into a “topic space”, such that words that tend to occur in the same texts occupy similar locations in this space. The frequency distribution of words for each author defines a probability distribution over this embedding. Lastly, we measure the distance between authors based on the amount of “work” required to transform one author’s probability distribution into another’s.

Installation

The Python and R scripts are provided as-is; there is no installation required, just download and run. However, you will need to have the following dependencies installed:

• Python 3.x and the following modules:

  • BioPython, pip install Bio
  • nltk, pip install nltk.

  In addition, the following nltk submodules need to be installed to ~/nltk_data:

  • punkt, python -c "import nltk; nltk.download('punkt')"
  • averaged_perceptron_tagger, python -c "import nltk; nltk.download('averaged_perceptron_tagger')"
  • wordnet, python -c "import nltk; nltk.download('wordnet')"

• R version 4.3+ and the following packages:

  • Matrix
  • transport
  • jsonlite
  • wordspace
  • uwot
  • igraph

Usage example

Build a list of authors

As an example of using this workflow, I will work through an analysis of the members of my home department (Pathology and Laboratory Medicine) at Western University. The starting point for this analysis is to manually curate a list of people affiliated with this unit.

After extracting first and last names from the department’s faculty list, I manually added initials (to be used as compact labels in plotting results) and then manually queried the NCBI PubMed database with each name. I evaluated each set of search results for signs of multiple hist, i.e., different authors with similar names, based on affiliations, middle initials and areas of research. When there was clear evidence of multiple authors in the search results, I added filtering criteria to the query string, such as AND Canada, until the results were consistent with a single author. I saved the listing of authors and query strings as a tab-separated (TSV) file.

Download text

We will use a Python script to retrieve publication records from the NCBI PubMed database, do some initial text processing, and write the results to JSON files (one per author) into a directory data/palm:

% python scripts/batch_fetch.py --retmax 200 --email [email protected] data/palm.tsv data/palm

Of course you need to replace [email protected] with your own e-mail address. This is required to query the NCBI database. In addition, I set up this script to pause for one second between API queries to avoid hitting the NCBI servers with too many requests. As a result, the script will take about a minute to run to completion.

Extract word counts

The next step is to process the documents that we’ve just downloaded into three outputs: 1. a JSON file containing associative arrays of word frequencies for each author; 2. a CSV file containing the term-document co-occurrence matrix for every pair of words that appears at least once in a given document, and; 3. a CSV file linking the integer indices used in the co-occurrence matrix to the actual word, in descending order of the word’s overall frequency in the entire corpus.

% python scripts/analyze.py data/palm --counts results/palm_author.json --matrix results/palm_cooccur.csv --index results/palm_index.csv

Import data

The index CSV file generated by analyze.py is a list of all words encountered in the text corpus, in descending order by frequency. The wordcloud package provides a nice way of visualizing the most frequent words.

index <- read.csv("results/palm_index.csv")
require(wordcloud, quietly=TRUE)
set.seed(3)
wordcloud(index$word[1:50], index$count[1:50], 
          colors=hcl.colors(20, 'Berlin'), random.color=TRUE)

Word embedding

The term-document co-occurrence matrix that we calculated in the previous step tracks the number of times every word appears in each document. This matrix becomes very large with even a modest number of documents, so we store the results in a “sparse” format in which we only record the number of times that word $i$ appears in document $j$, skipping all cases in which this count is zero.

We assume that words tending to appear in the same documents fall under a similar topic. For example, “phylogeny” and “sequence” tend to co-occur in the same abstracts. The next step calculates the distance between every pair of words based on their co-occurrence in documents.

To limit the amount of memory required to generate this distance matrix, we limit our analysis to the 5,000 most frequent words. The frequency distribution of words is very skewed, and words that have a very low frequency are not very useful to comparing authors. To illustrate this skewed distribution, let’s generate a quick barplot:

source("scripts/topicspace.R")
tab <- table(index$count)
par(mar=c(5,5,1,1))
plot(x=as.integer(names(tab)), y=as.integer(tab), las=2, 
     pch=19, cex=0.5,
     xlab="Number of times the word appears", ylab="Frequency")

For example, there are 4,415 times that a word appears only twice in the entire set of documents (upper-left point). Note that words that appeared only once were already filtered out during our processing steps).

The topicspace function combines the three outputs generated by the Python scripts, and uses a non-linear dimensionality reduction method (UMAP) to “embed” the words into a smaller number of dimensions (controlled by the n.comp argument). This function outputs a custom (topicspace) S3 class object for which I’ve written a few generic methods (print, summary and plot).

ts <- topicspace(
  index.path = "results/palm_index.csv",
  cooccur.path = "results/palm_cooccur.csv",
  author.path = "results/palm_author.json",
  max.words=5000, n.comp=3)
ts  # calls generic print function

## topicspace object
##    79 authors, 5000 words, 3 UMAP components
##    Top words: cell, patient, cancer, expression, clinical, disease 
##    Words per author: 1583.53 (range 231-4709)

Calling plot on a topicspace object displays the embedding of words into the first two dimensions of the reduced space:

plot(ts)

By default the plot function only displays the 100 most frequent words, replacing all other words with a small dot. Displaying all 5,000 words on this plot would make it impossible to read! The same function can also be used to resize points in proportion to a specific author’s word frequency distribution, and compare it to the distributions of other authors:

par(mfrow=c(1,2))  # side-by-side format
plot(ts, author=39)  # plot(ts, author="Poon_Art")
plot(ts, author=77)

Earth mover’s distance

The final step of the analysis is to calculate a matrix of distances between members of the department, based on the differences in their respective word frequency distributions. Mapping a person’s word frequencies onto the “topic space” into which we have embedded all the words results in a weighted point pattern or discrete probability distribution. Embedding words into topic space also enables us to compare distributions between people who do not use exactly the same words. For example, “carcinoma” is located close to “tumor”.

To calculate the distance between two authors, we use the Wasserstein distance, also known as the Kantorovich-Rubenstein or earth mover’s distance. This distance roughly corresponds to the minimum amount of work required to reshape one distribution into another, by transporting probability mass between points. This is one of the most time-consuming steps of the analysis, so I have set it up to run on 24 cores so that it takes about one minute on my machine running an AMD Ryzen 9 7950X. Hopefully your computer’s CPU has at least this many cores; if not, make sure you to decrease the mc.cores argument in the following expression. This particular dataset also requires about 1 GB of RAM per core, so adjust for your system accordingly.

t0 <- Sys.time()
wdist <- get.dist(ts, mc.cores=24)
Sys.time() - t0

## Time difference of 1.029745 mins

save(wdist, file="results/palm_wdist.RData")
class(wdist)  # show that this is a custom S3 object

## [1] "wdist" "dist"

Visualization

The last thing that we need to do is visually interpret the results of this analysis. This is complicated by the fact that we’ve generated another distance matrix. It is much smaller (79-by-79) than the co-occurrence matrix that we started with (5000-by-79), but still difficult to interpret.

Of course there are many methods to visualize a distance matrix. I’ve provided some functions in a script called visualize.R. One of these is simply a generic plot function calling a dimensionality reduction method such as multi-dimensional scaling or UMAP:

source("scripts/visualize.R")

# map author names to initials
fac <- read.csv("data/palm.tsv", sep='\t')
fac$filename <- paste(fac$lastname, fac$forename, sep="_")
fac$filename <- gsub(" ", "_", fac$filename)
idx <- match(attr(wdist, "Labels"), fac$filename)
labels <- fac$initials[idx]

# use generic plot function for `wdist` object
par(mfrow=c(1,2))
plot(wdist, labels=labels, main='UMAP')
plot(wdist, labels=labels, type='m', main="MDS")

We can also use R’s built-in hierarchical clustering methods:

hc <- hclust(wdist)
plot(hc, cex=0.5, main=NA, xlab=NA, sub=NA, labels=labels)

My preferred method, however, is to generate a network (graph) in which each node represents an author, and connections between authors indicate that their similarity exceeds some threshold. Because some authors end up forming a large number of connections (high degree size), I wrote a function that only connects each author to their $k$ nearest neighbours, where $k$ is set to 3 by default.

g <- make.knn(wdist)
plot(g, vertex.shape="none", vertex.label=labels, 
     vertex.label.cex=1, vertex.size=10, 
     edge.width=2, edge.arrow.mode='-')

We can use community detection to identify clusters in this graph, which can be visualized by drawing coloured polygons:

cl <- cluster_louvain(g)
plot(g, mark.groups=cl, vertex.shape="none", vertex.label=labels, 
     vertex.label.cex=1, vertex.size=10, 
     edge.width=2, edge.arrow.mode='-')

Note that the clusters produced by the Louvain method are non-overlapping; any overlap between polygons is due to constraints in drawing polygons.