In vivo predictive toxicogenomics.

Reference databases consisting of large sample numbers and high-dimensional microarray data are now available for the investigation of adverse events in animal model systems such as the rat. This large volume of data, accompanied by appropriate study designs, compound and dose selection procedure, and minimization of technical and biological confounding effects, can yield successful predictive models for a variety of hypotheses. The process of training, validating, and implementing predictive models is cyclical and complex. This chapter highlights individual decisions that need to be made before, during, and after a model or set of models has been trained, with an emphasis on proper statistical methods and suitable interpretation of the results.

Transcriptome analysis of human immune responses following live vaccine strain (LVS) Francisella tularensis vaccination.

The live vaccine strain (LVS) of Francisella tularensis is the only vaccine against tularemia available for humans, yet its mechanism of protection remains unclear. We probed human immunological responses to LVS vaccination with transcriptome analysis using PBMC samples from volunteers at time points pre- and post-vaccination. Gene modulation was highly uniform across all time points, implying commonality of vaccine responses. Principal components analysis revealed three highly distinct principal groupings: pre-vaccination (-144 h), early (+18 and +48 h), and late post-vaccination (+192 and +336 h). The most significant changes in gene expression occurred at early post-vaccination time points (

A summarization approach for Affymetrix GeneChip data using a reference training set from a large, biologically diverse database.

BACKGROUND: Many of the most popular pre-processing methods for Affymetrix expression arrays, such as RMA, gcRMA, and PLIER, simultaneously analyze data across a set of predetermined arrays to improve precision of the final measures of expression. One problem associated with these algorithms is that expression measurements for a particular sample are highly dependent on the set of samples used for normalization and results obtained by normalization with a different set may not be comparable. A related problem is that an organization producing and/or storing large amounts of data in a sequential fashion will need to either re-run the pre-processing algorithm every time an array is added or store them in batches that are pre-processed together. Furthermore, pre-processing of large numbers of arrays requires loading all the feature-level data into memory which is a difficult task even with modern computers. We utilize a scheme that produces all the information necessary for pre-processing using a very large training set that can be used for summarization of samples outside of the training set. All subsequent pre-processing tasks can be done on an individual array basis. We demonstrate the utility of this approach by defining a new version of the Robust Multi-chip Averaging (RMA) algorithm which we refer to as refRMA. RESULTS: We assess performance based on multiple sets of samples processed over HG U133A Affymetrix GeneChip arrays. We show that the refRMA workflow, when used in conjunction with a large, biologically diverse training set, results in the same general characteristics as that of RMA in its classic form when comparing overall data structure, sample-to-sample correlation, and variation. Further, we demonstrate that the refRMA workflow and reference set can be robustly applied to naïve organ types and to benchmark data where its performance indicates respectable results. CONCLUSION: Our results indicate that a biologically diverse reference database can be used to train a model for estimating probe set intensities of exclusive test sets, while retaining the overall characteristics of the base algorithm. Although the results we present are specific for RMA, similar versions of other multi-array normalization and summarization schemes can be developed.

Balancing entrepreneurship and business practices for e-collaboration: responsible information sharing in academic research.

Flexible, highly accessible collaboration tools can inherently conflict with controls placed on information sharing by offices charged with privacy protection, compliance, and maintenance of the general business environment. Our implementation of a commercial enterprise wiki within the academic research environment addresses concerns of all involved through the development of a robust user training program, a suite of software customizations that enhance security elements, a robust auditing program, allowance for inter-institutional wiki collaboration, and wiki-specific governance.