MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data

Cross-study validation of gene expression investigations is critical in genomic analysis. We developed an R package and associated object definitions to merge and visualize multiple gene expression datasets. Our merging functions use arbitrary character IDs and generate objects that can efficiently support a variety of joint analyses. Visualization tools support exploration and cross-study validation of the data, without requiring normalization across platforms. Tools include “integrative correlation” plots that is, scatterplots of all pairwise correlations in one study against the corresponding pairwise correlations of another, both for individual genes and all genes combined. Gene-specific plots can be used to identify genes whose changes are reliably measured across studies. Visualizations also include scatterplots of gene-specific statistics quantifying relationships between expression and phenotypes of interest, using linear, logistic and Cox regression. Availability: Free open source from url http://www.bioconductor.org. Contact: Xiaogang Zhong [email protected] Supplementary information: Documentation available with the package

Cross-study Validation and Combined Analysis of Gene Expression Microarray Data

Investigations of transcript levels on a genomic scale using hybridization-based arrays led to formidable advances in our understanding of the biology of many human illnesses. At the same time, these investigations have generated controversy, because of the probabilistic nature of the conclusions, and the surfacing of noticeable discrepancies between the results of studies addressing the same biological question. In this article we present simple and effective data analysis and visualization tools for gauging the degree to which the finding of one study are reproduced by others, and for integrating multiple studies in a single analysis. We describe these approaches in the context of studies of breast cancer, and illustrate that it is possible to identify a substantial, biologically relevant subset of the human genome within which hybridization results are reproducible. The subset generally varies with the platforms used, the tissues studied, and the populations being sampled. Despite important differences, it is also possible to develop simple expression measures that allow comparison across platforms, studies, labs and populations. Important biological signal is often preserved or enhanced. Cross-study validation and combination of microarray results requires careful, but not overly complex, statistical thinking, and can become a routine component of genomic analysis

Construction and characterization of infectious hepatitis C virus chimera containing structural proteins directly from genotype 1b clinical isolates

AbstractHCV genotype is a major determinant of clinical outcome, and GT1b HCV infection is the most difficult to treat and also the predominant genotype in East Asia and Europe. We developed 1b/JFH-1 inter-genotypic recombinants containing the structural genes (Core, E1, E2), p7 and the 1stTMD of NS2 directly from GT1b clinical isolates. Through a cloning selection strategy, we obtained 4 functional clones from 3 cases of GT1b patients' sera, which could produce infectious viruses in Huh7.5.1 cells. Sequencing analysis of recovered viruses from serial passage and reverse genetics revealed that adaptive mutations in the GT1b-originated region were enough for the enhancement of infectivity. A monoclonal antibody to E2 and original patient sera could efficiently block 3 of the viruses (26C3mt, 52B6mt and 79L9) while had little effect on 26C6mt viruses. The availability of 1b/JFH-1 chimeric viruses will be important for studies of isolate-specific neutralization and useful in evaluating antiviral therapies

OPTIMIZED CROSS-STUDY ANALYSIS OF MICROARRAY-BASED PREDICTORS

Background: Microarray-based gene expression analysis is widely used in cancer research to discover molecular signatures for cancer classification and prediction. In addition to numerous independent profiling projects, a number of investigators have analyzed multiple published data sets for purposes of cross-study validation. However, the diverse microarray platforms and technical approaches make direct comparisons across studies difficult, and without means to identify aberrant data patterns, less than optimal. To address this issue, we previously developed an integrative correlation approach to systematically address agreement of gene expression measurements across studies, providing a basis for cross-study validation analysis. Here we generalize this methodology to provide a metric for evaluating the overall efficacy of preprocessing and cross-referencing, and explore optimal combinations of filtering and cross-referencing strategies. We operate in the context of validating prognostic breast cancer gene expression signatures on data reported by three different groups, each using a different platform. Results: To evaluate overall cross-platform reproducibility in the context of a specific prediction problem, we suggest integrative association, that is the cross-study correlation of gene-specific measure of association with the phenotype predicted. Specifically, in this paper we use the correlation among the Cox proportional hazard coefficients for association of gene expression to relapse free survival (RFS). Gene filtering by integrative correlation to select reproducible genes emerged as the key factor to increase the integrative association, while alternative methods of gene cross-referencing and gene filtering proved only to modestly improve the overall reproducibility. Patient selection was another major factor affecting the validation process. In particular, in one of the studies considered, gene expression association with RFS varied across subsets of patients that differ by their ascertainment criteria. One of the subsets proved to be highly consistent with other studies, while others showed significantly lower consistency. Third, as expected, use of cluster-specific mean expression profiles in the Cox model yielded more generalizable results than expression data from individual genes. Finally, by using our approach we were able to validate the association between the breast cancer molecular classes proposed by Sorlie et al. and RFS. Conclusions: This paper provides a simple, practical and comprehensive technique for measuring consistency of molecular classification results across microarray platforms, without requiring subjective judgments about membership of samples in putative clusters. This methodology will be of value in consistently typing breast and other cancers across different studies and platforms in the future. Although the tumor subtypes considered here have been previously validated by their proponents, this is the first independent validation, and the first to include the Affymetrix platform