Biomedical subjects

Joseph T Chang

Publications and source records attributed to Joseph T Chang.

3 recordsLinked to original sources

PubMed2003

ExpressYourself: A modular platform for processing and visualizing microarray data.

DNA microarrays are widely used in biological research; by analyzing differential hybridization on a single microarray slide, one can detect changes in mRNA expression levels, increases in DNA copy numbers and the location of transcription factor binding sites on a genomic scale. Having performed the experiments, the major challenge is to process large, noisy datasets in order to identify the specific array elements that are significantly differentially hybridized. This normally requires aggregating different, often incompatible programs into a multi-step pipeline. Here we present ExpressYourself, a fully integrated platform for processing microarray data. In completely automated fashion, it will correct the background array signal, normalize the Cy5 and Cy3 signals, score levels of differential hybridization, combine the results of replicate experiments, filter problematic regions of the array and assess the quality of individual and replicate experiments. ExpressYourself is designed with a highly modular architecture so various types of microarray analysis algorithms can readily be incorporated as they are developed; for example, the system currently implements several normalization methods, including those that simultaneously consider signal intensity and slide location. The processed data are presented using a web-based graphical interface to facilitate comparison with the original images of the array slides. In particular, Express Yourself is able to regenerate images of the original microarray after applying various steps of processing, which greatly facilities identification of position-specific artifacts. The program is freely available for use at http://bioinfo.mbb.yale.edu/expressyourself.

Algorithms↗

PubMed2003

Spectral biclustering of microarray data: coclustering genes and conditions.

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

Breast Neoplasms↗

PubMed2002

A reverse transcription comparative real-time PCR method for quantitative detection of angiogenic growth factors in head and neck cancer patients.

OBJECTIVES: Head and neck cancer is one of the ten most frequent cancers in the world. The angiogenic growth factors VEGF, PDGF and bFGF play a role in cancer aggressiveness. We developed a sensitive method to quantify the gene expression of these factors in the tissues of head and neck cancer patients. DESIGN AND METHODS: All assays were performed using real-time RT-PCR, which yields a value (Ct) denoting the threshold cycle of PCR amplification at which product is first detected by fluorescence. The Ct is dependent on the quantity of the target molecule in the sample. To control for variation in RNA quantity and quality, we used 18S ribosome RNA as an internal control to calculate a relative Ct for the target molecules of interest, VEGF, PDGF and bFGF. A serially diluted positive control sample was analyzed by linear regression to determine the sensitivity and linearity of the assay. Paired normal and cancerous tissue samples from 115 head and neck cancer patients were assayed to ascertain the relative levels of the growth factors. RESULTS: The CVs of within-run and between-run assays for VEGF, PDGF and bFGF were all less than 3%. The correlation coefficient of the RNA concentrations and Ct values were 0.9987, 0.9977, and 0.9996 respectively for VEGF, PDGF and bFGF. The assay was sensitive to as little as 10(-3) ng of RNA. All three growth factors were significantly increased in tumor tissue as compared to normal tissue. VEGF, PDGF and bFGF levels were elevated in 71.3%, 58.2% and 54.0% of cancerous tissue samples, with average levels of over-expression of 35.1, 24.6 and 13. sixfold, respectively. CONCLUSION: This method provides sensitive, quantitative, high-throughput analysis for direct comparison of gene expression levels between samples, while adjusting for factors that may influence quantity determination. It should be applicable to molecules other than angiogenic growth factors, as well.

Endothelial Growth Factors↗