Identification of aberrant pathway and network activity from high-throughput data.

The workshop focused on approaches to deduce changes in biological activity in cellular pathways and networks that drive phenotype from high-throughput data. Work in cancer has demonstrated conclusively that cancer etiology is driven not by single gene mutation or expression change, but by coordinated changes in multiple signaling pathways. These pathway changes involve different genes in different individuals, leading to the failure of gene-focused analysis to identify the full range of mutations or expression changes driving cancer development. There is also evidence that metabolic pathways rather than individual genes play the critical role in a number of metabolic diseases. Tools to look at pathways and networks are needed to improve our understanding of disease and to improve our ability to target therapeutics at appropriate points in these pathways.

Interpreting genetics of gene expression: integrative architecture in Bioconductor.

Several influential studies of genotypic determinants of gene expression in humans have now been published based on various populations including HapMap cohorts. The magnitude of the analytic task (transcriptome vs. SNP-genome) is a hindrance to dissemination of efficient, thorough, and auditable inference methods for this project. We describe the structure and use of Bioconductor facilities for inference in genetics of gene expression, with simultaneous application to multiple HapMap cohorts. Tools distributed for this purpose are readily adapted for the structure and analysis of privately-generated data in expression genetics.

Quality assessment and data analysis for microRNA expression arrays.

MicroRNAs are small (approximately 22 nt) RNAs that regulate gene expression and play important roles in both normal and disease physiology. The use of microarrays for global characterization of microRNA expression is becoming increasingly popular and has the potential to be a widely used and valuable research tool. However, microarray profiling of microRNA expression raises a number of data analytic challenges that must be addressed in order to obtain reliable results. We introduce here a universal reference microRNA reagent set as well as a series of nonhuman spiked-in synthetic microRNA controls, and demonstrate their use for quality control and between-array normalization of microRNA expression data. We also introduce diagnostic plots designed to assess and compare various normalization methods. We anticipate that the reagents and analytic approach presented here will be useful for improving the reliability of microRNA microarray experiments.

Using flowViz to visualize flow cytometry data.

UNLABELLED: Automated analysis of flow cytometry (FCM) data is essential for it to become successful as a high throughput technology. We believe that the principles of Trellis graphics can be adapted to provide useful visualizations that can aid such automation. In this article, we describe the R/Bioconductor package flowViz that implements such visualizations. AVAILABILITY: flowViz is available as an R package from the Bioconductor project: http://bioconductor.org

Using GOstats to test gene lists for GO term association.

MOTIVATION: Functional analyses based on the association of Gene Ontology (GO) terms to genes in a selected gene list are useful bioinformatic tools and the GOstats package has been widely used to perform such computations. In this paper we report significant improvements and extensions such as support for conditional testing. RESULTS: We discuss the capabilities of GOstats, a Bioconductor package written in R, that allows users to test GO terms for over or under-representation using either a classical hypergeometric test or a conditional hypergeometric that uses the relationships among GO terms to decorrelate the results. AVAILABILITY: GOstats is available as an R package from the Bioconductor project: http://bioconductor.org

Global expression changes of constitutive and hormonally regulated genes during endometrial neoplastic transformation.

OBJECTIVE: Endometrioid endometrial carcinoma is caused by a combination of mutational events and hormonal factors. We used large-scale messenger RNA expression analysis to discover genes that distinguish neoplastic transformation and examine the patterns of tumor expression of those genes which are normally regulated during the menstrual cycle. METHODS: Expression of approximately 6000 unique genes was quantified in 4 normal (2 proliferative, 2 secretory) and 10 malignant endometria using Affymetrix Hu6800 GeneChip probe arrays. Expression differences between normal and malignant tissue groups were measured by a test of statistical significance comparing the individual t statistic for each gene to the distribution of maximum t statistics among all genes following 1001 permutations of the tissue group assignments (Permax test). Hormonally responsive genes, selected by comparison of proliferative and secretory subsets of normal endometria using a combination of filters applied to the group means and t test rankings, were then examined in the tumors. RESULTS: Fifty genes with a Permax <0.50 provided excellent discrimination between normal and malignant groups and were predominantly characterized by diminished expression levels in the cancers. We found that 100 genes which are hormonally regulated in normal tissues are expressed in a disordered and heterogeneous fashion in cancers, with tumors resembling proliferative more than secretory endometrium. CONCLUSION: Neoplastic transformation is accompanied by predominant loss of activity of many genes constitutively expressed in normal source tissues and absence of expression profiles which characterize the antitumorigenic progestin response.

Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease.

Multi-state Markov models can be useful in analysing disease history data. We apply the general estimation methods of Kalbfleisch and Lawless to panel data in which individuals are viewed over only a portion of their life history and complete information about transition times between states is unavailable. Methods to assess goodness-of-fit are proposed. To illustrate the methods, we consider models of HIV disease relating important immunological marker measurements to the onset of AIDS.

A graphical approach to the analysis of censored data.

The presence of censoring has hampered the graphical exploration of survival data. We present several graphical approaches to the analysis of such data here, many based on functionals of the distribution function and estimated using the Kaplan-Meier estimate of the distribution function. Topics covered include comparing two samples, comparing many samples, and regression.

Local full likelihood estimation for the proportional hazards model.

In this paper a new approach to local likelihood estimation for censored data is proposed. This method employs the full likelihood and alternates between estimating the baseline hazard and estimating the covariate effect. The proposed methodology incorporates multidimensional data via additive models. Some results regarding inference for the covariate effect are also presented.