Detecting differentially expressed genes by relative entropy

DNA microarray experiments have generated large amount of gene expression measurements across different conditions. One crucial step in the analysis of these data is to detect differentially expressed genes. Some parametric methods, including the two-sample t-test (T-test) and variations of it, have been used. Alternatively, a class of non-parametric algorithms, such as the Wilcoxon rank sum test (WRST), significance analysis of microarrays (SAM) of Tusher et al. (2001), the empirical Bayesian (EB) method of Efron et al. (2001), etc., have been proposed. Most available popular methods are based on t-statistic. Due to the quality of the statistic that they used to describe the difference between groups of data, there are situations when these methods are inefficient, especially when the data follows multi-modal distributions. For example, some genes may display different expression patterns in the same cell type, say, tumor or normal, to form some subtypes. Most available methods are likely to miss these genes. We developed a new non-parametric method for selecting differentially expressed genes by relative entropy, called SDEGRE, to detect differentially expressed genes by combining relative entropy and kernel density estimation, which can detect all types of differences between two groups of samples. The significance of whether a gene is differentially expressed or not can be estimated by resampling-based permutations. We illustrate our method on two data sets from Golub et al. (1999) and Alon et al. (1999). Comparing the results with those of the T-test, the WRST and the SAM, we identified novel differentially expressed genes which are of biological significance through previous biological studies while they were not detected by the other three methods. The results also show that the genes selected by SDEGRE have a better capability to distinguish the two cell types.     

Identification of differentially expressed genes associated with 5-methyltryptophan resistance in rice mutants

5-Methyltryptophan (5MT), a tryptophan analog, resistant M4 rice mutants with high free amino acid contents were obtained through in vitro mutagenesis. To evaluate the 5MT resistance mechanism, a cDNA library was constructed by using the leaves and roots of the 5MT resistant mutant plants. Expressed sequenced tags (ESTs) of 1 019 randomly selected clones were analyzed and then assembled 588 unigens. A total of 389 unigenes had significant homologies with known protein sequences at the NCBI database and the remaining 199 unigenes were designated unidentified genes. These unigens were grouped into 13 categories according to their putative functions. Of the 233 randomly selected clones, 25 were identified as differentially expressed genes between 5MT resistant and 5MT sensitive wild type plants. For further study of the differential expression of the genes, expression patterns of 12 genes related to various biological functions were evaluated in response to 5MT treatment in both the resistant plants and sensitive plants. All of the tested 12 genes exhibited higher expression levels in mutant plants than wild type plants under the 5MT inhibition. These expression patterns of the 12 genes suggested that the genes related to 5MT resistance in the rice mutants have a variety of functions, and yield remarkably diverse expression patterns upon 5MT treatment. Many genes that were identified tend to be related to defense and stress responses, suggesting “cross-talking“ between biotic/abiotic stresses including the 5MT treatment. Therefore, 5MT resistant mutants might be of value for identifying genes related to plant defenses and stresses.     

Identification of differentially expressed genes in human heart with ventricular septal defect using suppression subtractive hybridization

Ventricular septal defect (VSD) accounts for the largest number of birth congenital heart defects in human, but the genetic programs that control ventricular septation are poorly understood. To identify differentially expressed genes between ventricular septal defect and normal ventricular septum myocardium, we have undertaken suppression subtractive hybridization (SSH) and generated reciprocal cDNA collections of representative mRNAs specific to human heart with ventricular septal defect versus normal control. Following SSH, 1378 clones were sequenced and found to derive from 551 different genes. These predominately expressed genes included genes involved in energy metabolism, cell cycle and growth, cytoskeleton and cell adhesion, LIM protein, zinc finger protein, and development. It is anticipated that further study of genes identified will provide insights into their specific roles in the etiology of VSD, even in cardiac development, aging, and disease.     

Identification of candidate SNPs for drug induced toxicity from differentially expressed genes in associated tissues

The growing collection of publicly available high-throughput data provides an invaluable resource for generating preliminary in silico data in support of novel hypotheses. In this study we used a cross-dataset meta-analysis strategy to identify novel candidate genes and genetic variations relevant to paclitaxel/carboplatin-induced myelosuppression and neuropathy. We identified genes affected by drug exposure and present in tissues associated with toxicity. From ten top-ranked genes 42 non-synonymous single nucleotide polymorphisms (SNPs) were identified in silico and genotyped in 94 cancer patients treated with carboplatin/paclitaxel. We observed variations in 11 SNPs, of which seven were present in a sufficient frequency for statistical evaluation. Of these seven SNPs, three were present in ABCA1 and ATM, and showed significant or borderline significant association with either myelosuppression or neuropathy. The strikingly high number of associations between genotype and clinically observed toxicity provides support for our data-driven computations strategy to identify biomarkers for drug toxicity.     

Identification of differentially expressed genes in omental adipose tissues of obese patients by suppression subtractive hybridization

To identify differentially expressed genes between obese individuals and normal control, we have undertaken suppression subtractive hybridization (SSH). Omental adipose tissues were obtained via abdominal surgery for appendicitis in both 13 obese subjects [BMI (body mass index) >30 kg/m2] and 13 normal subjects (BMI >18 and <25 kg/m2). Following SSH, about one thousand clones were sequenced and found to derive from 426 different genes. These predominately expressed genes included genes involved in lipid metabolism, cytokines, signal transduction, GLUT4 translocation, cell cycle and growth, cytoskeleton, and others. Although more detailed analyses are necessary, it is anticipated that further study of genes identified will provide insights into their specific roles in the etiology of obesity.     

Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

基因芯片技术筛选转SlNAC1基因拟南芥差异表达基因

研究已表明植物特有的一些NAC(NAM,ATAF1/2,CUC2)转录因子可提高植物抗逆性,利用基因芯片技术筛选转SlNAC1基因拟南芥与野生型拟南芥间差异表达基因,能够为研究转基因拟南芥非生物胁迫抗性相关基因提供依据。结果显示,在转SlNAC1基因拟南芥43 604个基因中有3 046个差异表达2倍以上的基因。对差异表达5倍以上基因经过GO富集度统计学分析表明,细胞组分相关基因占33.05%;分子功能相关基因占33.95%;生物学过程相关基因占33.00%。对差异表达2倍以上基因进行KEGG信号通路分析,结果表明有2 431个基因涉及到88个不同的信号通路。通过筛选获得转基因拟南芥非生物胁迫抗性相关候选基因,为后续研究NAC转录因子的下游基因及其调控网络的构建提供方向和理论支撑。     

鸡基因组差异表达基因翻译起始密码子上下游序列的比较研究

翻译起始调控是基因表达调控的一个关键步骤之一。本文以鸡为研究材料,比较研究了鸡基因组高表达基因和低表达基因翻译起始密码子上下游的碱基序列差异,旨在寻找影响鸡基因表达水平的特异性调控位点。全部3 020个单剪接基因完整的mRNA序列及有详细注释的5＇UTRs序列从Ensembl下载。编写计算机程序,读取每个基因mRNA起始密码子上下游各位点的碱基。研究发现,起始密码子上游-3、-2位点可能是鸡基因组基因表达起始密码子正确识别的关键位点。起始密码子上下游的碱基组成分析发现,高表达基因和低表达基因起始密码子的上游均倾向使用（G＋C）,高表达基因的使用偏倚尤为强烈。序列差异比较发现,高表达基因在-9、-6、-3、＋4位点显著偏向G,在-1、-2、-4、-5位点显著偏向C。低表达基因起始密码子上游使用A、U的频率显著高于低表达基因。在-19位点强烈偏向A,在＋1、＋11、＋14位点强烈偏向U。     

Investigation of differentially expressed genes during the development of mouse cerebellum

Before the discovery of DNA microarray and DNA chip technology, the expression of only a small number of genes could be analyzed at a time. Currently, such technology allows us the simultaneous analysis of a large number of genes to systematically monitor their expression patterns that may be associated with various biological phenomena. We utilized the Affymetrix GeneChip Mu11K to analyze the gene expression profile in developing mouse cerebellum to assist in the understanding of the genetic basis of cerebellar development in mice. Our analysis showed 81.6% (10,321/12,654) of the genes represented on the GeneChip were expressed in the postnatal cerebellum, and among those, 8.7% (897/10,321) were differentially expressed with more than a two-fold change in their maximum and minimum expression levels during the developmental time course. Further analysis of the differentially expressed genes that were clustered in terms of their expression patterns and the function of their encoded products revealed an aspect of the genetic foundation that lies beneath the cellular events and neural network formation that takes place during the development of the mouse cerebellum.     

Pyramiding multiple genes for resistance to soybean mosaic virus in soybean using molecular markers

Seven strains of Soybean mosaic virus (SMV) and three independent resistance loci (Rsv1, Rsv3, and Rsv4) have been identified in soybean. The objective of this research was to pyramid Rsv1, Rsv3, and Rsv4 for SMV resistance using molecular markers. J05 carrying Rsv1 and Rsv3 and V94-5152 carrying Rsv4 were used as the donor parents for gene pyramiding. A series of F_2:3, F_3:4, and F_4:5 lines derived from J05 × V94-5152 were developed for selecting individuals carrying all three genes. Eight PCR-based markers linked to the three SMV resistance genes were used for marker-assisted selection. Two SSR markers (Sat_154 and Satt510) and one gene-specific marker (Rsv1-f/r) were used for selecting plants containing Rsv1; Satt560 and Satt063 for Rsv3; and Satt266, AI856415, and AI856415-g for Rsv4. Five F_4:5 lines were homozygous for all eight marker alleles and presumably carry all three SMV resistance genes that would potentially provide multiple and durable resistance to SMV.     

Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology

In DNA microarray analysis, there is often interest in isolating a few genes that best discriminate between tissue types. This is especially important in cancer, where different clinicopathologic groups are known to vary in their outcomes and response to therapy. The identification of a small subset of gene expression patterns distinctive for tumor subtypes can help design treatment strategies and improve diagnosis. Toward this goal, we propose a methodology for the analysis of high-density oligonucleotide arrays. The gene expression measures are modeled as censored data to account for the quantification limits of the technology, and two gene selection criteria based on contrasts from an analysis of covariance (ANCOVA) model are presented. The model is formulated in a hierarchical Bayesian framework, which in addition to making the fit of the model straightforward and computationally efficient, allows us to borrow strength across genes. The elicitation of hierarchical priors, as well as issues related to parameter identifiability and posterior propriety, are discussed in detail. We examine the performance of our proposed method on simulated data, then present a detailed case study of an endometrial cancer dataset.