1) Gene set enrichment analysis for non-monotone association and multiple experimental categories ? ? Identifying differentially expressed genes or biological pathways remains an important task for microarray studies. Recently, gene set enrichment analysis (GSEA) has gained recognition as a way to identify biological pathways/processes that are associated with a phenotypic endpoint. In GSEA, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then GSEA combines these gene-specific local statistics to evaluate association for pre-selected sets of genes. Commonly used local statistics include t statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Herein we propose to use as the local statistic the square of multiple correlation coefficient R2 from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA framework to identify significant gene sets. Furthermore, we describe a procedure for inference across multiple GSEA analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes that are associated with liver injury manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Our method is general and can be viewed as extending the current GSEA methodology.? ? 2) Optimization of a position weight matrix by a genetic algorithm using ChIP data? ? Position weight matrices (PMWs) are simple models commonly used in motif finding algorithms to identify short functional elements, such as cis-regulatory motifs, on genes. When few experimentally verified motifs are available, estimation of the PWM may be poor. The resultant PWM may not reliably discriminate a true motif from a false one. While experimentally identifying such motifs remains time consuming and expensive, low-resolution binding data from techniques such as ChIP-on-chip and ChIP-PET have become available. We propose a novel but simple method to improve a poorly estimated PWM using ChIP data. Starting from an existing PWM, a set of ChIP sequences, and a set of background sequences, our method, GAPWM, derives an improved PWM via a genetic algorithm that maximizes the area under the receiver operating characteristic (ROC) curve. GAPWM can easily incorporate prior information such as base conservation. We tested our method on two PMWs (Oct4/Sox2 and p53) using three recently published ChIP data sets (human Oct4, mouse Oct4, and human p53). GAPWM substantially increased the sensitivity/specificity of a poorly estimated PWM and further improved the quality of a good PWM. Furthermore, it still functioned when the starting PWM contained a major error. The ROC performance of GAPWM compared favorably with that of MEME and others. With increasing availability of ChIP data, our method provides an alternative for obtaining high-quality PWMs for genome-wide identification of transcription factor binding sites. The C source code and all data used in this report are available at http://dir.niehs.nih.gov/dirbb/gapwm? ? 3) Identifying cis-elements by an EM algorithm coupled with false discovery rate control? ? We propose a novel approach that selects as many binding sites as possible in a set of sequences while controlling a user-specified false discovery rate (FDR), defined as the expected proportion of non-motif subsequences falsely declared as binding sites. Our method takes multiple PWMs as the starting estimates for the EM algorithm and automatically runs one at a time. We are currently comparing our method with leading alternatives such as multiple EM for motif elucidation (MEME) using sequence data from ChIP experiments.? ? 4) Major collaborative projects? we are interested in the roles of microRNA-302 on embryonic stem cell self-renewal. Using both computational and experimental approaches, we are investigating transcriptional regulation of microRNA-302 expression and the downstream targets of microRNA-302.
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