Full four-color book.

Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R.

All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies.

Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Motivation: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance.

Results: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.

Availability: topgo.bioinf.mpi-inf.mpg.de

Contact: alexa@mpi-sb.mpg.de

Supplementary Information: Supplementary data are available at Bioinformatics online.

Improved scoring of functional groups from gene expression data by decorrelating GO graph structure

Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.

/pdf/high-quality-binary-protein-interaction-map-of-the-yeast-3d9exp8h0t.pdf

High-Quality Binary Protein Interaction Map of the Yeast Interactome Network

Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

/pdf/network-based-prediction-of-protein-function-1cwqg32chi.pdf

Network-based prediction of protein function

http://www.hmphanmi.com.cn/hmp/upload/event/study_4/5.pdf

Virus-plus-susceptibility gene interaction determines Crohn's disease gene Atg16L1 phenotypes in intestine

Motivation: Systems biology requires accurate models of protein complexes, including physical interactions that assemble and regulate these molecular machines. Yeast two-hybrid (Y2H) and affinity--purification/mass-spectrometry (AP--MS) technologies measure different protein--protein relationships, and issues of completeness, sensitivity and specificity fuel debate over which is best for high-throughput 'interactome' data collection. Static graphs currently used to model Y2H and AP--MS data neglect dynamic and spatial aspects of macromolecular complexes and pleiotropic protein function.

Results: We apply the local modeling methodology proposed by Scholtens and Gentleman (2004) to two publicly available datasets and demonstrate its uses, interpretation and limitations. Specifically, we use this technology to address four major issues pertaining to protein--protein networks. (1) We motivate the need to move from static global interactome graphs to local protein complex models. (2) We formally show that accurate local interactome models require both Y2H and AP--MS data, even in idealized situations. (3) We briefly discuss experimental design issues and how bait selection affects interpretability of results. (4) We point to the implications of local modeling for systems biology including functional annotation, new complex prediction, pathway interactivity and coordination with gene-expression data.

Availability: The local modeling algorithm and all protein complex estimates reported here can be found in the R package apComplex, available at http://www.bioconductor.org

Contact: dscholtens@northwestern.edu

Supplementary information: http://daisy.prevmed.northwestern.edu/~denise/pubs/LocalModeling

Local modeling of global interactome networks

Purpose: To prospectively evaluate the quality of life (QOL) before, at completion, and after therapy for patients receiving an accelerated fractionation schedule of radiotherapy for advanced, unresectable non-small-cell lung cancer in a Phase II multi-institutional trial Methods and Materials: The Functional Assessment of Cancer Therapy-Lung (FACT-L) patient questionnaire was used to score the QOL in patients enrolled in the Eastern Cooperative Oncology Group Phase II trial (ECOG 4593) of hyperfractionated accelerated radiotherapy in non-small-cell lung cancer Radiotherapy (total dose 576 Gy in 36 fractions) was delivered during 15 days, with three radiation fractions given each treatment day The protocol was activated in 1993, and 30 patients had accrued by November 1995 The FACT-L questionnaire was administered at study entry (baseline), on the last day of radiotherapy (assessment 2), and 4 weeks after therapy (assessment 3) The FACT-L includes scores for physical, functional, emotional, and social well-being (33 items), and a subscale of lung cancer symptoms (10 additional items) The summation of the physical, functional, and lung cancer symptom subscales (21 items) constitutes the Trial Outcome Index (TOI), considered the most clinically relevant outcome measure in lung cancer treatment trials Results: The FACT-L completion rates at the designated study time points were as follows: baseline, 30 of 30 (100%); assessment 2, 29 (97%) of 30; and assessment 3, 24 (80%) of 30 At treatment completion, statistically significant declines in QOL scores were noted, compared with baseline for physical and functional well-being Emotional well-being scores improved at both assessment 2 and assessment 3 The physical and functional scores returned approximately to baseline values at assessment 3 The change in TOI score was evaluated as a function of the clinical response to treatment, toxicity grade, and survival; no clear association was noted A trend for the largest decrease in QOL was noted for patient groups with shorter survival times The mean change in the TOI score from baseline to assessment 3 was −896 for patients surviving 52 weeks Conclusions: The FACT-L questionnaire can be successfully administered to non-small-cell lung cancer patients enrolled in a prospective Phase II trial of accelerated radiation fractionation The decrement in physical and functional QOL during treatment returned to baseline level at 4 weeks after treatment Emotional well-being improved at all time points A trend was noted for shorter survival times in patients with the largest negative change in TOI score These data suggest that the clinical use of hyperfractionated accelerated radiotherapy did not cause a significant, long-term decrease in the QOL of the treated patients, and that it is feasible to perform a QOL study of patients enrolled in such a trial

Quality of life assessment in advanced non-small-cell lung cancer patients undergoing an accelerated radiotherapy regimen: report of ecog study 4593

Motivation: The last few years have seen the advent of high-throughput technologies to analyze various properties of the transcriptome and proteome of several organisms. The congruency of these different data sources, or lack thereof, can shed light on the mechanisms that govern cellular function. A central challenge for bioinformatics research is to develop a unified framework for combining the multiple sources of functional genomics information and testing associations between them, thus obtaining a robust and integrated view of the underlying biology.

Results: We present a graph-theoretic approach to test the significance of the association between multiple disparate sources of functional genomics data by proposing two statistical tests, namely edge permutation and node label permutation tests. We demonstrate the use of the proposed tests by finding significant association between a Gene Ontology-derived predictome and data obtained from mRNA expression and phenotypic experiments for Saccharomyces cerevisiae. Moreover, we employ the graph-theoretic framework to recast a surprising discrepancy presented elsewhere between gene expression and knockout phenotype, using expression data from a different set of experiments.

Availability: An R software package, GraphAT, containing the data and statistical procedures is available from Bioconductor: http://www.bioconductor.org

/pdf/a-graph-theoretic-approach-to-testing-associations-between-3kc9ysjj52.pdf

A graph-theoretic approach to testing associations between disparate sources of functional genomics data

Analyzing factorial designed microarray experiments

Both treatment efficacy and safety are typically the primary endpoints in Phase II, and even in some Phase III, clinical trials. Efficacy is frequently measured by time to response, death, or some other milestone event and thus is a continuous, possibly censored, outcome. Safety, however, is frequently measured on a discrete scale; in Eastern Cooperative Oncology Group clinical trial E2290, it was measured as the number of weekly rounds of chemotherapy that were tolerable to colorectal cancer patients. For the joint analysis of efficacy and safety, we propose a non-parametric, computationally simple estimator for the bivariate survival function when one time-to-event is continuous, one is discrete, and both are subject to right-censoring. The bivariate censoring times may depend on each other, but they are assumed to be independent of both event times. We derive a closed-form covariance estimator for the survivor function which allows for inference to be based on any of several possible statistics of interest. In addition, we derive its covariance with respect to calendar time of analysis, allowing for its use in sequential studies.

Denise Scholtens

Papers

Local modeling of global interactome networks

Quality of life assessment in advanced non-small-cell lung cancer patients undergoing an accelerated radiotherapy regimen: report of ecog study 4593

A graph-theoretic approach to testing associations between disparate sources of functional genomics data

Analyzing factorial designed microarray experiments

A computationally simple bivariate survival estimator for efficacy and safety