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Lang Chen

Publications and source records attributed to Lang Chen.

2 recordsLinked to original sources

Transcriptional coordination of the metabolic network in Arabidopsis.

Patterns of coexpression can reveal networks of functionally related genes and provide deeper understanding of processes requiring multiple gene products. We performed an analysis of coexpression networks for 1,330 genes from the AraCyc database of metabolic pathways in Arabidopsis (Arabidopsis thaliana). We found that genes associated with the same metabolic pathway are, on average, more highly coexpressed than genes from different pathways. Positively coexpressed genes within the same pathway tend to cluster close together in the pathway structure, while negatively correlated genes typically occupy more distant positions. The distribution of coexpression links per gene is highly skewed, with a small but significant number of genes having numerous coexpression partners but most having fewer than 10. Genes with multiple connections (hubs) tend to be single-copy genes, while genes with multiple paralogs are coexpressed with fewer genes, on average, than single-copy genes, suggesting that the network expands through gene duplication, followed by weakening of coexpression links involving duplicate nodes. Using a network-analysis algorithm based on coexpression with multiple pathway members (pathway-level coexpression), we identified and prioritized novel candidate pathway members, regulators, and cross pathway transcriptional control points for over 140 metabolic pathways. To facilitate exploration and analysis of the results, we provide a Web site (http://www.transvar.org/at_coexpress/analysis/web) listing analyzed pathways with links to regression and pathway-level coexpression results. These methods and results will aid in the prioritization of candidates for genetic analysis of metabolism in plants and contribute to the improvement of functional annotation of the Arabidopsis genome.

Arabidopsis↗

Sources of variation in Affymetrix microarray experiments.

BACKGROUND: A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates. RESULTS: We performed a microarray experiment using a total of 24 Affymetrix GeneChip arrays. The study included 4th mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates. CONCLUSION: The greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates.

Animals↗