Monthly Archives: September 2014


A Family of Algorithms for Computing Consensus about Node State from Network Data

Eleanor R. Brush, David C. Krakauer, Jessica C. Flack


Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes–from ranking websites to determining critical species in ecosystems–yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node’s state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node’s direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus–through breadth or depth– impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes “form opinions” about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms’ cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions.


Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM


Charles J. Vaske1,†, Stephen C. Benz2,†, J. Zachary Sanborn2, Dent Earl2, Christopher Szeto2, Jingchun Zhu2, David Haussler1,2 and Joshua M. Stuart2,*

+ Author Affiliations

1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA

* To whom correspondence should be addressed.


Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines.

Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients.

Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway’s activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference.

Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients.

Availability:Source code available at


Supplementary information: Supplementary data are available at Bioinformatics online.


Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Katherine A. Hoadley1, 20, Christina Yau2, 20, Denise M. Wolf3, 20, Andrew D. Cherniack4, 20, David Tamborero5, Sam Ng6, Max D.M. Leiserson7, Beifang Niu8, Michael D. McLellan8, Vladislav Uzunangelov6, Jiashan Zhang9, Cyriac Kandoth8, Rehan Akbani10, Hui Shen11, 22, Larsson Omberg12, Andy Chu13, Adam A. Margolin12, 21, Laura J. van’t Veer3, Nuria Lopez-Bigas5, 14, Peter W. Laird11, 22, Benjamin J. Raphael7, Li Ding8, A. Gordon Robertson13, Lauren A. Byers10, Gordon B. Mills10, John N. Weinstein10, Carter Van Waes18, Zhong Chen19, Eric A. Collisson15,The Cancer Genome Atlas Research Network, Christopher C. Benz2, , , Charles M. Perou1, 16, 17, , , Joshua M. Stuart6, ,

1 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
2 Buck Institute for Research on Aging, Novato, CA 94945, USA
3 Department of Laboratory Medicine, University of California San Francisco, 2340 Sutter St, San Francisco, CA, 94115, USA
4 The Eli and Edythe Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
5 Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, Barcelona 08003, Spain
6 Department of Biomolecular Engineering, Center for Biomolecular Sciences and Engineering, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA
7 Department of Computer Science and Center for Computational Molecular Biology, Brown University, 115 Waterman St, Providence RI 02912, USA
8 The Genome Institute, Washington University, St Louis, MO 63108, USA
9 National Cancer Institute, NIH, Bethesda, MD 20892, USA
10 UT MD Anderson Cancer Center, Bioinformatics and Computational Biology, 1400 Pressler Street, Unit 1410, Houston, TX 77030, USA
11 USC Epigenome Center, University of Southern California Keck School of Medicine, 1450 Biggy Street, Los Angeles, CA 90033, USA
12 Sage Bionetworks 1100 Fairview Avenue North, M1-C108, Seattle, WA 98109-1024, USA
13 Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC V5Z 4S6, Canada
14 Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys, 23, Barcelona 08010, Spain
15 Department of Medicine, University of California San Francisco, 450 35d St, San Francisco, CA, 94148, USA
16 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
17 Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
18 Building 10, Room 4-2732, NIDCD/NIH, 10 Center Drive, Bethesda, MD 20892
19 Head and Neck Surgery Branch, NIDCD/NIH, 10 Center Drive, Room 5D55, Bethesda, MD 20892