Document Type


Original Publication Date


Journal/Book/Conference Title

BMC Bioinformatics



DOI of Original Publication



Originally published at

Date of Submission

September 2014



Networks are ubiquitous in modern cell biology and physiology. A large literature exists for inferring/proposing biological pathways/networks using statistical or machine learning algorithms. Despite these advances a formal testing procedure for analyzing network-level observations is in need of further development. Comparing the behaviour of a pharmacologically altered pathway to its canonical form is an example of a salient one-sample comparison. Locating which pathways differentiate disease from no-disease phenotype may be recast as a two-sample network inference problem.


We outline an inferential method for performing one- and two-sample hypothesis tests where the sampling unit is a network and the hypotheses are stated via network model(s). We propose a dissimilarity measure that incorporates nearby neighbour information to contrast one or more networks in a statistical test. We demonstrate and explore the utility of our approach with both simulated and microarray data; random graphs and weighted (partial) correlation networks are used to form network models. Using both a well-known diabetes dataset and an ovarian cancer dataset, the methods outlined here could better elucidate co-regulation changes for one or more pathways between two clinically relevant phenotypes.


Formal hypothesis tests for gene- or protein-based networks are a logical progression from existing gene-based and gene-set tests for differential expression. Commensurate with the growing appreciation and development of systems biology, the dissimilarity-based testing methods presented here may allow us to improve our understanding of pathways and other complex regulatory systems. The benefit of our method was illustrated under select scenarios.


© 2013 Yates and Mukhopadhyay; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Is Part Of

VCU Biostatistics Publications

1471-2105-14-94-s1.doc (91 kB)
R code.

1471-2105-14-94-s2.doc (35 kB)
Ovarian cancer genes analyzed. Subset of analyzed genes as categorized by Bracken et al. [

1471-2105-14-94-s3.pdf (50 kB)
Two-sample comparison for partial correlation networks under H0. A uniform qq-plot of the 100 resample p-values for a test of H0 Π1= Π2 versus H1 Π1≠ Π2 under the null hypothesis. The y-axis is the observed p-value; the x-axis the expected p-value.