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HoughFeature, a novel method for assessing drug effects in three-color cDNA microarray experiments.

ABSTRACT: BACKGROUND: Three-color microarray experiments can be performed to assess drug effects on the genomic scale. The methodology may be useful in shortening the cycle, reducing the cost, and improving the efficiency in drug discovery and development compared with the commonly used dual-color technology. A visualization tool, the hexaMplot, is able to show the interrelations of gene expressions in normal-disease-drug samples in three-color microarray data. However, it is not enough to assess the complicated drug therapeutic effects based on the plot alone. It is important to explore more effective tools so that a deeper insight into gene expression patterns can be gained with three-color microarrays. RESULTS: Based on the celebrated Hough transform, a novel algorithm, HoughFeature, is proposed to extract line features in the hexaMplot corresponding to different drug effects. Drug therapy results can then be divided into a number of levels in relation to different groups of genes. We apply the framework to experimental microarray data to assess the complex effects of Rg1 (an extract of Chinese medicine) on Hcy-related HUVECs in details. Differentially expressed genes are classified into 15 functional groups corresponding to different levels of drug effects. CONCLUSION: Our study shows that the HoughFeature algorithm can reveal natural cluster patterns in gene expression data of normal-disease-drug samples. It provides both qualitative and quantitative information about up- or down-regulated genes. The methodology can be employed to predict disease susceptibility in gene therapy and assess drug effects on the disease based on three-color microarray data.

SUBMITTER: Zhao H

PROVIDER: S-EPMC1965489 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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HoughFeature, a novel method for assessing drug effects in three-color cDNA microarray experiments.

Zhao Hongya H Yan Hong H

BMC bioinformatics 20070717

<h4>Background</h4>Three-color microarray experiments can be performed to assess drug effects on the genomic scale. The methodology may be useful in shortening the cycle, reducing the cost, and improving the efficiency in drug discovery and development compared with the commonly used dual-color technology. A visualization tool, the hexaMplot, is able to show the interrelations of gene expressions in normal-disease-drug samples in three-color microarray data. However, it is not enough to assess t ...[more]

PMID: 17634089

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Project description:BACKGROUND: High-throughput, parallel gene expression analysis by means of microarray technology has become a widely used technique in recent years. There are currently two main dye-labelling strategies for microarray studies based on custom-spotted cDNA or oligonucleotides arrays: (I) Dye-labelling of a single target sample with a particular dye, followed by subsequent hybridisation to a single microarray slide, (II) Dye-labelling of two different target samples with two different dyes, followed by subsequent co-hybridisation to a single microarray slide. The two dyes most frequently used for either method are Cy3 and Cy5. We propose and evaluate a novel experiment set-up utilising three differently labelled targets co-hybridised to one microarray slide. In addition to Cy3 and Cy5, this incorporates Alexa 594 as a third dye-label. We evaluate this approach in line with current data processing and analysis techniques for microarrays, and run separate analyses on Alexa 594 used in single-target, dual-target and the intended triple-target experiment set-ups (a total of 18 microarray slides). We follow this by pointing out practical applications and suitable analysis methods, and conclude that triple-target microarray experiments can add value to microarray research by reducing material costs for arrays and related processes, and by increasing the number of options for pragmatic experiment design. RESULTS: The addition of Alexa 594 as a dye-label for an additional--third--target sample works within the framework of more commonplace Cy5/Cy3 labelled target sample combinations. Standard normalisation methods are still applicable, and the resulting data can be expected to allow identification of expression differences in a biological experiment, given sufficient levels of biological replication (as is necessary for most microarray experiments). CONCLUSION: The use of three dye-labelled target samples can be a valuable addition to the standard repertoire of microarray experiment designs. The method enables direct comparison between two experimental populations as well as measuring these two populations in relation to a third reference sample, allowing comparisons within the slide and across slides. These benefits are only offset by the added level of consideration required in the experimental design and data processing of a triple-target study design. Common methods for data processing and analysis are still applicable, but there is scope for the development of custom models for triple-target data. In summary, we do not consider the triple-target approach to be a new standard, but a valuable addition to the existing microarray study toolkit.

Project description:BackgroundThe incorporation of prior biological knowledge in the analysis of microarray data has become important in the reconstruction of transcription regulatory networks in a cell. Most of the current research has been focused on the integration of multiple sets of microarray data as well as curated databases for a genome scale reconstruction. However, individual researchers are more interested in the extraction of most useful information from the data of their hypothesis-driven microarray experiments. How to compile the prior biological knowledge from literature to facilitate new hypothesis generation from a microarray experiment is the focus of this work. We propose a novel method based on the statistical analysis of reported gene interactions in PubMed literature.ResultsUsing Gene Ontology (GO) Molecular Function annotation for reported gene regulatory interactions in PubMed literature, a statistical analysis method was proposed for the derivation of a likelihood of interaction (LOI) score for a pair of genes. The LOI-score and the Pearson correlation coefficient of gene profiles were utilized to check if a pair of query genes would be in the above specified interaction. The method was validated in the analysis of two gene sets formed from the yeast Saccharomyces cerevisiae cell cycle microarray data. It was found that high percentage of identified interactions shares GO Biological Process annotations (39.5% for a 102 interaction enriched gene set and 23.0% for a larger 999 cyclically expressed gene set).ConclusionThis method can uncover novel biologically relevant gene interactions. With stringent confidence levels, small interaction networks can be identified for further establishment of a hypothesis testable by biological experiment. This procedure is computationally inexpensive and can be used as a preprocessing procedure for screening potential biologically relevant gene pairs subject to the analysis with sophisticated statistical methods.

Dataset Information

HoughFeature, a novel method for assessing drug effects in three-color cDNA microarray experiments.

Publications

HoughFeature, a novel method for assessing drug effects in three-color cDNA microarray experiments.

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