PLOS Biology Outlier Detection

In this blog post I play with dimensionality reduction techniques SVD and Isomap to map a corpus of 1,754 PLOS Biology articles from 27,210-dimensional feature space to 2-dimensional space.

This sort of approach is used oftentimes to estimate which data points are near each other. Here, I realized that the data points far away from the bulk discuss (almost) consistently neurobiological topics.

As far as I am aware PLOS Biology publish articles on all biological topics so there is probably no editorial factor to our observation here.

%matplotlib inline
%autosave 10

import gensim
import cPickle as pickle
from sklearn import *
from sklearn.manifold import Isomap
import numpy
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D

articles = pickle.load(open('data/plos_biology_articles_unfurled.list','r'))

dois = pickle.load(open('data/plos_biology_dois.list','r'))

The first article in this data set looks as follows

articles[0][:10]

['introduction',
 'during',
 '1980s',
 '1990s',
 'methods',
 'molecular',
 'genetics',
 'used',
 'determine',
 'contributions']

And the corresponding DOI of this article is

dois[0]

'10.1371/journal.pbio.1000584'

Checking the main text of the above DOI we make certain that the article stored in articles[0] corresponds to the DOI stored in dois[0].

Let us now load the same corpus as in articles but already formatted as a numerical matrix that represents each article (row of the matrix) as a bag of words. We generated this corpus and the corresponding dictionary earlier.

corpus = gensim.corpora.MmCorpus('data/plos_biology_corpus.mm')
dictionary = dictionary = gensim.corpora.dictionary.Dictionary().load('data/plos_biology.dict')


corpus_mat = gensim.matutils.corpus2csc(corpus)
corpus_mat = corpus_mat.T
print corpus_mat.shape

(1754, 27210)

SVD

svd = decomposition.TruncatedSVD(n_components=2)


corpus_mat_transform = svd.fit_transform(corpus_mat)


pyplot.scatter(corpus_mat_transform[:,0], corpus_mat_transform[:,1])
pyplot.scatter(numpy.median(corpus_mat_transform[:,0]), numpy.median(corpus_mat_transform[:,1]), color='red')

<matplotlib.collections.PathCollection at 0x39dd4690>

png

Outliers far Away from Median

As we can see there are a few articles that lie relatively far away from the bulk of the corpus (denoted by the red disk which marks the median). Let’s focus on some of these:

corpus_mat_transform[corpus_mat_transform[:,0]>150]

array([[ 188.00455202,  207.0323185 ],
       [ 173.92204694,  149.59252031],
       [ 153.2889464 ,  215.74459155],
       [ 162.25069234,  102.40518113],
       [ 150.996759  ,  145.03623767]])




numpy.where(corpus_mat_transform[:,0]>150)

(array([  35, 1074, 1109, 1371, 1544]),)




for index in numpy.where(corpus_mat_transform[:,0]>150)[0]:
    print 'http://www.plosbiology.org/article/info:doi/%s' % dois[index]

The first of these “outliers” in the above reduced space is a Synopsis articles so it may be understandable why that one sticks out. However, the remaining articles are research articles that all deal with neurobiological topics - so off-hand it is not obvious to me why these would lie a bit further away from the bulk of the articles.

Articles Near Median

For comparison with these outliers, let us take a look at articles near the median (red disk in the above scatter plot).

median = (numpy.median(corpus_mat_transform[:,0]), numpy.median(corpus_mat_transform[:,1]))
print median

(41.996541953291668, -11.862843898802915)



distances = numpy.asarray([numpy.linalg.norm(vec) for vec in corpus_mat_transform-median])
near_median = numpy.where(distances<1.5)
print near_median

(array([ 233,  810,  872, 1096, 1353, 1408, 1705]),)



for index in near_median[0]:
    print 'http://www.plosbiology.org/article/info:doi/%s' % dois[index]

Articles near the median discuss topics such as gene expression in C. elegans, development and patterning of the neural plat, and stomata in Arabidopsis.

Isomap

Isomap is another dimensionality reduction tool that promises to preserve the higher-dimensional shape of your data cloud better than SVD.

isomap = Isomap(n_components=2)


isomap_transformed = isomap.fit_transform(corpus_mat.toarray())


pyplot.scatter(isomap_transformed[:,0], isomap_transformed[:,1])
pyplot.scatter(numpy.median(isomap_transformed[:,0]), numpy.median(isomap_transformed[:,1]), color='red')

<matplotlib.collections.PathCollection at 0x37bc4910>

png

Outlying Group of Articles with 1st Component > 400

Let us focus on the group of articles on the right in the above plot.

indeces = numpy.where(isomap_transformed[:,0]>450)
print indeces
for index in indeces[0]:
    print 'http://www.plosbiology.org/article/info:doi/%s' % dois[index]

(array([ 388,  397,  486,  521,  676,  949, 1087, 1109, 1221, 1325, 1427]),)

Clicking through these, we realize that most of these articles deal with neurobiological topics again - except for articles on insulin resistance and B cell lymphomas.

Two Articles in the Top Left Corner

indeces = numpy.where((isomap_transformed[:,0]<-100) & (isomap_transformed[:,1]>600))
print indeces
for index in indeces[0]:
    print 'http://www.plosbiology.org/article/info:doi/%s' % dois[index]

(array([  35, 1074]),)

Also these two outliers deal with neurobiological topics.

One Article Center Top

indeces = numpy.where((isomap_transformed[:,0]>100) & (isomap_transformed[:,1]>600))
print indeces
for index in indeces[0]:
    print 'http://www.plosbiology.org/article/info:doi/%s' % dois[index]

(array([421]),)

And again an article about a neurobiological topic.

Articles Near the Median

median = (numpy.median(isomap_transformed[:,0]), numpy.median(isomap_transformed[:,1]))
print median

(-22.134749426543411, -4.3751913714736927)



distances = numpy.asarray([numpy.linalg.norm(vec) for vec in isomap_transformed-median])
near_median = numpy.where(distances<2)
print near_median

(array([  49,  323,  350,  772,  773,  889,  908,  960, 1038, 1158, 1285,
       1309, 1321, 1530]),)

for index in near_median[0]:
    print 'http://www.plosbiology.org/article/info:doi/%s' % dois[index]

Articles near the median discuss topics such as stochastic gene expression, DNA transcription and repair, metabolic symbiosis, T cell differentiation, chromatin, an RNAi screen for cytokinesis inhibitors, and a study on p53.

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