Microarrays Commons^™

A Bayesian Model For Pooling Gene Expression Studies That Incorporates Co-Regulation Information, Erin M. Conlon, Bradley L. L. Postier, Barbara A. Methé, Kelly P. Nevin, Derek R. Lovley

Erin M. Conlon

Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model. Our Bayesian model borrows information from other genes within the same operon to improve estimation of gene expression. The model produces the gene-specific posterior probability of differential expression, which is the basis for inference. We found in simulations and in biological studies that incorporating co-regulation information improves upon the independence model. …

A Bayesian Model For Pooling Gene Expression Studies That Incorporates Co-Regulation Information, Erin Conlon, Bradley L. Postier, Barbara Methé, Kelly Nevin, Derek Lovley

Microbiology Department Faculty Publication Series

Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model. Our Bayesian model borrows information from other genes within the same operon to improve estimation of gene expression. The model produces the gene-specific posterior probability of differential expression, which is the basis for inference. We found in simulations and in biological studies that incorporating co-regulation information improves upon the independence model. …

Rapid Changes In Gene Expression Dynamics In Response To Superoxide Reveal Soxrs-Dependent And Independent Transcriptional Networks, Jeffrey L. Blanchard, Wei-Yun Wholey, Erin M. Conlon, Pablo J. Pomposiello

Erin M. Conlon

Background

SoxR and SoxS constitute an intracellular signal response system that rapidly detects changes in superoxide levels and modulates gene expression in E. coli. A time series microarray design was used to identify co-regulated SoxRS-dependent and independent genes modulated by superoxide minutes after exposure to stress.

Methodology/Principal Findings

soxS mRNA levels surged to near maximal levels within the first few minutes of exposure to paraquat, a superoxide-producing compound, followed by a rise in mRNA levels of known SoxS-regulated genes. Based on a new method for determining the biological significance of clustering results, a total of 138 genic regions, including several …

Bayesian Meta-Analysis Models For Microarray Data: A Comparative Study, Erin M. Conlon, Joon J. Song, Anna Liu

Erin M. Conlon

Background With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods. Results Two Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce …

Bayesian Models For Pooling Microarray Studies With Multiple Sources Of Replications, Erin M. Conlon, Joon J. Song, Jun S. Liu

Erin M. Conlon

Background Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently. Results We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the gene-specific posterior probability of differential expression, which …

Program Of Gene Transcription For A Single Differentiating Cell Type During Sporulation In Bacillus Subtilis, Patrick Eichenberger, Masaya Fujita, Shane T. Jensen, Erin M. Conlon, David Z. Rudner, Stephanie T. Want, Caitlin Ferguson, Koki Haga, Txutomu Sato, Jun S. Liu, Richard Losick

Erin M. Conlon

Asymmetric division during sporulation by Bacillus subtilis generates a mother cell that undergoes a 5-h program of differentiation. The program is governed by a hierarchical cascade consisting of the transcription factors: σE, σK, GerE, GerR, and SpoIIID. The program consists of the activation and repression of 383 genes. The σE factor turns on 262 genes, including those for GerR and SpoIIID. These DNA-binding proteins downregulate almost half of the genes in the σE regulon. In addition, SpoIIID turns on ten genes, including genes involved in the appearance of σK. Next, σK activates 75 additional genes, including that for GerE. This …

The Program Of Gene Transcription For A Single Differentiating Cell Type During Sporulation In Bacillus Subtilis, Patrick Eichenberger, Masaya Fujita, Shane T. Jensen, Erin M. Conlon, David Z. Rudner, Stephanie T. Wang, Caitlin Ferguson, Koki Haga, Tsutomu Sato, Jun S. Liu, Richard Losick

Erin M. Conlon

Asymmetric division during sporulation by Bacillus subtilis generates a mother cell that undergoes a 5-h program of differentiation. The program is governed by a hierarchical cascade consisting of the transcription factors: σE, σK, GerE, GerR, and SpoIIID. The program consists of the activation and repression of 383 genes. The σE factor turns on 262 genes, including those for GerR and SpoIIID. These DNA-binding proteins downregulate almost half of the genes in the σE regulon. In addition, SpoIIID turns on ten genes, including genes involved in the appearance of σK. Next, σK activates 75 additional genes, including that for GerE. This …

Statistical Issues In The Clustering Of Gene Expression Data, Darlene R. Goldstein, Debashis Ghosh, Erin M. Conlon

Erin M. Conlon

This paper illustrates some of the problems which can occur in any data set when clustering samples of gene expression profiles. These include a possible high degree of dependence of results on choice of clustering algorithm, further dependence of results on the choices of genes and samples to be included in the clustering (for example, whether or not to include control samples), and difficulty in assessing the validity of the grouping. We also demonstrate the use of Cox regression as a tool to identify genes influencing survival.