Biomedical subjects
Publications and source records attributed to Robert Gentleman.
UNLABELLED: This paper reviews the central concepts and implementation of data structures and methods for studying genetics of gene expression with the GGtools package of Bioconductor. Illustration with a HapMap+expression dataset is provided. AVAILABILITY: Package GGtools is part of Bioconductor 1.9 (http://bioconductor.org). Open source with Artistic License.
MOTIVATION: Gene Set Enrichment Analysis (GSEA) has been developed recently to capture changes in the expression of pre-defined sets of genes. We propose number of extensions to GSEA, including the use of different statistics to describe the association between genes and phenotypes of interest. We make use of dimension reduction procedures, such as principle component analysis, to identify gene sets with correlated expression. We also address issues that arise when gene sets overlap. RESULTS: Our proposals extend the range of applicability of GSEA and allow for adjustments based on other covariates. We have provided a well-defined procedure to address interpretation issues that can raise when gene sets have substantial overlap. We have shown how standard dimension reduction methods, such as PCA, can be used to help further interpret GSEA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Explore the source record for details and available documents.
Breast cancer is the most common nonskin malignancy affecting women. Currently, no simple, blood-based diagnostic test exists to complement radiological screening and increase sensitivity of detection. To screen plasma specimens and identify biomarkers that detect HER2-positive breast cancer, automated robotic sample processing followed by surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) mass spectroscopy was used. Multiple statistical algorithms were used to select biomarkers that segregate cancer patients versus controls and produced average CV rates ranging from 20% to 29%. A set of seven biomarkers were validated on an independent test data set and achieved the best error rate of 19.1%. A permutation test indicated a p-value for CV error less than 0.002. Moreover, a ROC curve using these biomarkers achieved an area-under-the-curve value of 0.95 on an independent test data set. The marker responsible for most of the resolving power was identified as a fragment of Fibrinogen Alpha (FGA) encompassing residues 605-629. This marker was present at lower levels in cancer patients as compared to controls. The importance of this biomarker was validated in a longitudinal study comparing pre- and post-operative levels and was shown to revert to normal levels after surgery. This fragment may serve as a useful diagnostic and treatment-monitoring marker.
PURPOSE: To characterize gene expression signatures in acute lymphocytic leukemia (ALL) cells associated with known genotypic abnormalities in adult patients. EXPERIMENTAL DESIGN: Gene expression profiles from 128 adult patients with newly diagnosed ALL were characterized using high-density oligonucleotide microarrays. All patients were enrolled in the Italian GIMEMA multicenter clinical trial 0496 and samples had >90% leukemic cells. Uniform phenotypic, cytogenetic, and molecular data were also available for all cases. RESULTS: T-lineage ALL was characterized by a homogeneous gene expression pattern, whereas several subgroups of B-lineage ALL were evident. Within B-lineage ALL, distinct signatures were associated with ALL1/AF4 and E2A/PBX1 gene rearrangements. Expression profiles associated with ALL1/AF4 and E2A/PBX1 are similar in adults and children. BCR/ABL+ gene expression pattern was more heterogeneous and was most similar to ALL without known molecular rearrangements. We also identified a set of 83 genes that were highly expressed in leukemia blasts from patients without known molecular abnormalities who subsequently relapsed following therapy. Supervised analysis of kinase genes revealed a high-level FLT3 expression in a subset of cases without molecular rearrangements. Two other kinases (PRKCB1 and DDR1) were highly expressed in cases without molecular rearrangements, as well as in BCR/ABL-positive ALL. CONCLUSIONS: Genomic signatures are associated with phenotypically and molecularly well defined subgroups of adult ALL. Genomic profiling also identifies genes associated with poor outcome in cases without molecular aberrations and specific genes that may be new therapeutic targets in adult ALL.
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
While scientific research and the methodologies involved have gone through substantial technological evolution the technology involved in the publication of the results of these endeavors has remained relatively stagnant. Publication is largely done in the same manner today as it was fifty years ago. Many journals have adopted electronic formats, however, their orientation and style is little different from a printed document. The documents tend to be static and take little advantage of computational resources that might be available. Recent work, Gentleman and Temple Lang (2003), suggests a methodology and basic infrastructure that can be used to publish documents in a substantially different way. Their approach is suitable for the publication of papers whose message relies on computation. Stated quite simply, Gentleman and Temple Lang (2003) propose a paradigm where documents are mixtures of code and text. Such documents may be self-contained or they may be a component of a compendium which provides the infrastructure needed to provide access to data and supporting software. These documents, or compendiums, can be processed in a number of different ways. One transformation will be to replace the code with its output -- thereby providing the familiar, but limited, static document. In this paper we apply these concepts to a seminal paper in bioinformatics, namely The Molecular Classification of Cancer, Golub et al (1999). The authors of that paper have generously provided data and other information that have allowed us to largely reproduce their results. Rather than reproduce this paper exactly we demonstrate that such a reproduction is possible and instead concentrate on demonstrating the usefulness of the compendium concept itself.
Accurate systems biology modeling requires a complete catalog of protein complexes and their constituent proteins. We discuss a graph-theoretic/statistical algorithm for local dynamic modeling of protein complexes using data from affinity purification-mass spectrometry experiments. The algorithm readily accommodates multicomplex membership by individual proteins and dynamic complex composition, two biological realities not accounted for in existing topological descriptions of the overall protein network. A likelihood-based objective function guides the protein complex modeling algorithm. With an accurate complex membership catalog in place, systems biology can proceed with greater precision.
The false discovery rate (FDR) procedure has become a popular method for handling multiplicity in high-dimensional data. The definition of FDR has a natural Bayesian interpretation; it is the expected proportion of null hypotheses mistakenly rejected given a measure of evidence for their truth. In this article, we propose controlling the positive FDR using a Bayesian approach where the rejection rule is based on the posterior probabilities of the null hypotheses. Correspondence between Bayesian and frequentist measures of evidence in hypothesis testing has been studied in several contexts. Here we extend the comparison to multiple testing with control of the FDR and illustrate the procedure with an application to wavelet thresholding. The problem consists of recovering signal from noisy measurements. This involves extracting wavelet coefficients that result from true signal and can be formulated as a multiple hypotheses-testing problem. We use simulated examples to compare the performance of our approach to the Benjamini and Hochberg (1995, Journal of the Royal Statistical Society, Series B57, 289-300) procedure. We also illustrate the method with nuclear magnetic resonance spectral data from human brain.
DNA microarrays in conjunction with statistical models may help gain a deeper understanding of the molecular basis for specific diseases. An intense area of research is concerned with the identification of genes related to particular phenotypes. The technology, however, is subject to various sources of error that may lead to expression readings that are substantially different from the true transcript levels. Few methods for microarray data analysis have accounted for measurement error in a substantial way and that is the purpose of this investigation. We describe a Bayesian error-in-variable model for the analysis of microarray data from a clinical study of patients with acute lymphoblastic leukemia. We focus in particular on the problem of identifying genes whose expression patterns are associated with duration of remission. This is a question of great practical interest since relapse is a major concern in the treatment of this disease. We explore the effects of ignoring the uncertainty in the expression estimates on the selection and ranking of genes.
UNLABELLED: In this paper, we review the central concepts and implementations of tools for working with network structures in Bioconductor. Interfaces to open source resources for visualization (AT&T Graphviz) and network algorithms (Boost) have been developed to support analysis of graphical structures in genomics and computational biology. AVAILABILITY: Packages graph, Rgraphviz, RBGL of Bioconductor (www.bioconductor.org).
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.
UNLABELLED: The nucleotide sequences of the probes on a microarray can be used for a variety of purposes in the analysis of microarray experiments. We describe software and a paradigm for the creation of data packages for curating, distributing and working with probe sequence data in a uniform, across-types-of-microarrays manner. While the implementation is specific to the Bioconductor project, the ideas and general strategies are more general and could be easily adopted by other projects. AVAILABILITY: The R package matchprobes is available under LGPL at http://www.bioconductor.org SUPPLEMENTARY INFORMATION: The package contains documentation in the form of a vignette and manual pages.
Gene expression profiles were examined in 33 adult patients with T-cell acute lymphocytic leukemia (T-ALL). Nonspecific filtering criteria identified 313 genes differentially expressed in the leukemic cells. Hierarchical clustering of samples identified 2 groups that reflected the degree of T-cell differentiation but was not associated with clinical outcome. Comparison between refractory patients and those who responded to induction chemotherapy identified a single gene, interleukin 8 (IL-8), that was highly expressed in refractory T-ALL cells and a set of 30 genes that was highly expressed in leukemic cells from patients who achieved complete remission. We next identified 19 genes that were differentially expressed in T-ALL cells from patients who either had a relapse or remained in continuous complete remission. A model based on the expression of 3 of these genes was predictive of duration of remission. The 3-gene model was validated on a further set of T-ALL samples from 18 additional patients treated on the same clinical protocol. This study demonstrates that gene expression profiling can identify a limited number of genes that are predictive of response to induction therapy and remission duration in adult patients with T-ALL.
Little is known regarding the graft response to transplantation injury. This study investigates the posttransplantation response of genes that are constitutively expressed in the heart. Constitutive heart and lymph node tissue-restricted gene expression was first analyzed with DNA microarrays. To demonstrate changes following transplantation in genes constitutively expressed in the heart, we performed vascularized murine heart transplants in allogeneic (BALB/c to B6), syngeneic (B6 to B6), and alymphoid (BALB/c-RAG2-/- to B6-RAG1-/-) experimental groups. Temporal induction of genes posttransplant relative to constitutive expression was evaluated with DNA microarrays. Dendrograms and self-organizing maps were generated to determine the dissimilarity between the experimental groups and to identify subsets of differentially expressed genes within the groups, respectively. Expression patterns of selected genes were confirmed by real-time PCR. Biological processes were assigned to genes induced posttransplant using the AnnBuilder package via the Gene Ontology Database. Post-transplant, a shift was noted in genes classified as defense, communication, and metabolism. Our results identify novel components of the graft response to transplantation injury and rejection.
SUMMARY: AnnBuilder is an R package for assembling genomic annotation data. The system currently provides parsers to process annotation data from LocusLink, Gene Ontology Consortium, and Human Gene Project and can be extended to new data sources via user defined parsers. AnnBuilder differs from other existing systems in that it provides users with unlimited ability to assemble data from user selected sources. The products of AnnBuilder are files in XML format that can be easily used by different systems. AVAILABILITY: (http://www.bioconductor.org). Open source.