How many differentially expressed genes: A perspective from the comparison of genotypic and phenotypic distances

Identifying differentially expressed genes is critical in microarray data analysis. Many methods have been developed by combining p-value, fold-change, and various statistical models to determine these genes. When using these methods, it is necessary to set up various pre-determined cutoff values. However, many of these cutoff values are somewhat arbitrary and may not have clear connections to biology. In this study, a genetic distance method based on gene expression level was developed to analyze eight sets of microarray data extracted from the GEO database. Since the genes used in distance calculation have been ranked by fold-change, the genetic distance becomes more stable when adding more genes in the calculation, indicating there is an optimal set of genes which are sufficient to characterize the stable difference between samples. This set of genes is differentially expressed genes representing both the genotypic and phenotypic differences between samples.     

Identification of differentially expressed genes in subcutaneous adipose tissue from subjects with familial combined hyperlipidemia

Subjects with familial combined hyperlipidemia (FCHL) are characterized by a complex metabolic phenotype with hyperlipidemia, insulin resistance, and central obesity. FCHL is due to impaired adipose tissue function superimposed on hepatic overproduction of lipoproteins. We investigated adipose tissue as an interesting target tissue for differential gene expression in FCHL. Human cDNA expression array analyses, in which adipose tissue from five FCHL patients was compared with that from four age, gender, and BMI matched controls, resulted in the identification of 22 up-regulated and three down-regulated genes. The genes differentially expressed imply activation of the adipocyte cell cycle genes. Furthermore, the differential expression of the genes coding for tumor necrosis factor alpha, interleukin 6, and intracellular adhesion molecule 1 support a role for adipose tissue in insulin resistance in FCHL subjects. The observed changes represent a primary genetic defect, an adaptive response, or a contribution of both.     

Identification of differentially expressed genes using multi-resolution wavelet transformation analysis combined with SAM

Although many statistical methods have been proposed for identifying differentially expressed genes, the optimal approach has still not been resolved. Therefore, it is necessary to develop more efficient methods of finding differentially expressed genes while accounting for noise and false discovery rate (FDR). We propose a method based on multi-resolution wavelet transformation analysis combined with SAM for identifying differentially expressed genes by adjusting the Δ and computing the FDR. This method was applied to a microarray expression dataset from adenoma patients and normal subjects. The number of differentially expressed genes gradually reduced with an increasing Δ value, and the FDR was reduced after wavelet transformation. At a given Δ value, the FDR was also reduced before and after wavelet transformation. In conclusion, a greater number and quality of differentially expressed genes were detected using the method when compared to non-transformed data, and the FDRs were notably more controlled and reduced.     

In search of differentially expressed genes and proteins

A great challenge for modern cell biology is the successful examination of the co-expression of thousands of genes under physiological or pathological conditions and how the expression patterns define the different states of a single cell, tissue or a microorganism. Gene expression can be analyzed today on a large scale by advanced technical approaches for differential screening of proteins and mRNAs. The identification of differentially expressed mRNAs has been successfully applied to understand gene function and the underlying molecular mechanism(-s) of differentiation, development and disease state. Analysis of gene expression by the systematic mapping of thousands of proteins present in a cell or tissue can be achieved by the use of two-dimensional (2D) gel electrophoresis, quantitative computer image analysis, and protein identification techniques. In this article, we comment on some of these techniques and try to stress their advantages and drawbacks. We show how data from RNA/DNA mapping, sequence information from genome projects and protein pattern profiling can be linked with each other and annotated. These comprehensive approaches permit the study of differential gene and protein expressions in cells or tissues.     

Identifying differentially expressed genes in cDNA microarray experiments.

A major goal of microarray experiments is to determine which genes are differentially expressed between samples. Differential expression has been assessed by taking ratios of expression levels of different samples at a spot on the array and flagging spots (genes) where the magnitude of the fold difference exceeds some threshold. More recent work has attempted to incorporate the fact that the variability of these ratios is not constant. Most methods are variants of Student's t-test. These variants standardize the ratios by dividing by an estimate of the standard deviation of that ratio; spots with large standardized values are flagged. Estimating these standard deviations requires replication of the measurements, either within a slide or between slides, or the use of a model describing what the standard deviation should be. Starting from considerations of the kinetics driving microarray hybridization, we derive models for the intensity of a replicated spot, when replication is performed within and between arrays. Replication within slides leads to a beta-binomial model, and replication between slides leads to a gamma-Poisson model. These models predict how the variance of a log ratio changes with the total intensity of the signal at the spot, independent of the identity of the gene. Ratios for genes with a small amount of total signal are highly variable, whereas ratios for genes with a large amount of total signal are fairly stable. Log ratios are scaled by the standard deviations given by these functions, giving model-based versions of Studentization. An example is given.     

Divergent fructokinase genes are differentially expressed in tomato.

Two cDNA clones (Frk1 and Frk2) encoding fructokinase (EC 2.7.1.4) were isolated from tomato (Lycopersicon esculentum). The Frk2 cDNA encoded a deduced protein of 328 amino acids that was more than 90% identical with a previously characterized potato (Solanum tuberosum) fructokinase. In contrast, the Frk1 cDNA encoded a deduced protein of 347 amino acids that shared only 55% amino acid identity with Frk2. Both deduced proteins possessed and ATP-binding motif and putative substrate recognition site sequences identified in bacterial fructokinases. The Frk1 cDNA was expressed in a mutant yeast (Saccharomyces cerevisiae) line, which lacks the ability to phosphorylate glucose and fructose and is unable to grow on glucose or fructose. Mutant cells expressing Frk1 were complemented to grow on fructose but not glucose, indicating that Frk1 phosphorylates fructose but not glucose, and this activity was verified in extracts of transformed yeast. The mRNA corresponding to Frk2 accumulated to high levels in young, developing tomato fruit, whereas the Frk1 mRNA accumulated to higher levels late in fruit development. The results indicate that fructokinase in tomato is encoded by two divergent genes, which exhibit a differential pattern of expression during fruit development.     

Identifying differentially expressed genes from microarray experiments via statistic synthesis

MOTIVATION: A common objective of microarray experiments is the detection of differential gene expression between samples obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice. RESULTS: Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in datasets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best individual statistics, while achieving robustness properties lacked by the individual statistics. We further evaluate performance on one other microarray study.     

Tumor promoter-inducible genes are differentially expressed in the developing mouse.

TIS genes are rapidly and transiently induced by tetradecanoyl phorbol acetate in 3T3 cells. We analyzed the developmental appearance of a number of the TIS genes to determine whether, in a normal physiological context, these genes have common or distinct mechanisms of regulation. Each TIS gene has a distinct tissue specificity and/or developmental profile.     

Identification of genes differentially expressed in benign prostatic hyperplasia.

Differences between benign prostatic hyperplasia (BPH) and normal prostate tissue at the level of mRNA expression provide an opportunity to identify candidate genes for this disease. A cDNA subtraction procedure was used to isolate differentially expressed genes in BPH. The subtraction was done by solution hybridization of BPH cDNA against excess normal prostate cDNA. We identified known, EST, and novel genes by sequence and database analysis of the subtracted cDNAs. Several of these cDNAs were used as probes in Northern blotting analysis to confirm over-expression of their corresponding mRNAs in BPH tissues. One highly upregulated sequence of interest shared identity with a known mRNA encoding human NELL2, a protein containing epidermal growth factor-like domains. NELL2 was not previously reported to be expressed in prostate and may code for a novel prostatic growth factor. In situ hybridization analysis of hyperplastic prostate specimens demonstrated that NELL2 mRNA expression is predominantly localized in basal cells of the epithelium. Disease-related changes in the levels of NELL2 may contribute to alterations in epithelial-stromal homeostasis in BPH. (J Histochem Cytochem 49:669-670, 2001)     

脱发相关差异表达基因的生物信息学分析

利用生物信息学方法分析脱发相关差异表达基因,有望帮助了解脱发发生发展的分子机制。本研究从NCBI的子数据库GEO中选择基因表达谱GSE45512和GSE45513数据集,利用R语言limma工具包,筛选出两个物种斑秃样本与正常样本的共同显著差异表达基因。对这部分基因进行功能注释和蛋白互作网络分析,同时对全部差异表达基因进行基因集富集分析。结果发现,人头皮斑秃样本共筛选出225个差异表达基因;C3H/HeJ小鼠自发斑秃皮肤样本共筛选出337个差异表达基因;两个物种的共同显著差异表达基因有23个。GO功能富集分析和蛋白互作网络分析显示,这部分差异基因显著富集于免疫相关功能,并且彼此间存在蛋白互作关系。基因集富集分析显示两个物种的差异基因都能显著富集到趋化因子信号通路、细胞因子受体相互作用、金葡菌感染及抗原加工与呈递通路;而且人的下调差异基因不仅映射到了人类表型数据库的脱发表型,也映射到皮肤附属物病理相关表型。综上所述,本研究通过生物信息方法分析脱发皮肤组织与正常皮肤组织的差异表达基因,最终筛选出23个在人和小鼠中共同存在的显著差异表达基因;此外,分析发现脱发与免疫过程及皮肤附属物病变密切相关,这些结果为脱发的诊断和治疗提供了新思路。     

中华卵索线虫雌雄寄生后期幼虫基因差异表达分析

索科线虫是一类具有很大生防潜力的昆虫(如棉铃虫等)天敌资源,但由于其体外培养尚未获得成功,阻碍了商业化生产和大规模应用,究其原因主要是体外培养的线虫不能完成雌雄性别分化。因此,研究该类线虫性别分化机制成为近年本领域的热点课题。该文以中华卵索线虫为实验材料,采用mRNA差异显示的方法,分析了中华卵索线虫性别分化关键时期雌、雄寄生后期幼线虫的基因表达差异。在雌雄线虫体内得到具有表达差异的基因片段20条。其中8条在雄虫体内特异性表达,12条在雌虫体内特异性表达。利用信息生物学技术对所分离到的差异片段进行了序列分析。其中,Ensembl分析发现4个片段与秀丽线虫X染色体具有可匹配片段,推测这些片段可能是影响中华卵索线虫性别分化的重要基因。这些结果将为后续研究提供思路。     

CIT: identification of differentially expressed clusters of genes from microarray data

Cluster Identification Tool (CIT) is a microarray analysis program that identifies differentially expressed genes. Following division of experimental samples based on a parameter of interest, CIT uses a statistical discrimination metric and permutation analysis to identify clusters of genes or individual genes that best differentiate between the experimental groups. CIT integrates with the freely available CLUSTER and TREEVIEW programs to form a more complete microarray analysis package.     

Density based pruning for identification of differentially expressed genes from microarray data

Motivation

Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes.

Results

We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB Pruning) is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO) with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change.

Conclusions

Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://mleg.cse.sc.edu/degprune

     

Radiation hybrid mapping of 70 rat genes from a data set of differentially expressed genes

The spontaneously hypertensive rat (SHR) is a model of human essential hypertension. Increased blood pressure in SHR is associated with other risk factors associated with cardiovascular disease, including insulin resistance and dyslipidemia. DNA microarray studies identified over 200 differentially expressed genes and ESTs between SHR and normotensive control rats. These clones represent candidate genes that may underlie previously detected QTLs in SHR. This study made use of the publication of two whole-genome maps to identify positional QTL candidates. Radiation hybrid (RH) mapping was used to determine the chromosomal locations of 70 rat genes and ESTs from this dataset. Most of the locations are novel, but in five cases we identified a definitive map location for genes previously mapped by somatic cell hybrids and/or linkage analysis. Genes for which the mouse genome map location was already determined mapped to syntenic segments in the rat genome map, except for two rat genes whose map locations confirmed previous findings. Where synteny comparisons could be made only with the human, 74% of the genes mapped in this study lay in a conserved syntenic segment. Chromosomal localisation of these mouse and human orthologs to syntenic segments produces a high level of confidence in the data presented in this study. The data provide new map locations for rat genes and will aid efforts to advance the rat genome map. The data may also be used to prioritize candidate QTL genes in SHR and other rat strains on the basis of their map location.     

A theoretical analysis of the selection of differentially expressed genes

A great deal of recent research has focused on the challenging task of selecting differentially expressed genes from microarray data ("gene selection"). Numerous gene selection algorithms have been proposed in the literature, but it is often unclear exactly how these algorithms respond to conditions like small sample sizes or differing variances. Choosing an appropriate algorithm can therefore be difficult in many cases. In this paper we propose a theoretical analysis of gene selection, in which the probability of successfully selecting differentially expressed genes, using a given ranking function, is explicitly calculated in terms of population parameters. The theory developed is applicable to any ranking function which has a known sampling distribution, or one which can be approximated analytically. In contrast to methods based on simulation, the approach presented here is computationally efficient and can be used to examine the behavior of gene selection algorithms under a wide variety of conditions, even when the number of genes involved runs into the tens of thousands. The utility of our approach is illustrated by comparing three widely-used gene selection methods.     

筛选差异表达基因方法的新进展

了解不同细胞或同类细胞在不同发育阶段、不同生理状态下的基因表达状况,可以为研究生命活动过程提供重要信息。以差别筛选、削减杂交等基本方法为出发点,研究基因表达差异的方法不断完善,先后出现了DDRT—PCR、RDA、SSH、cDNA微阵列(基因芯片)、基因表达的系统分析(SAGE)等技术。本着重对这些方法的优缺点及改进进行论述和评介,并对技术的发展趋势进行了分析。