可识别多种癌症的基因功能模块

在应用基因芯片技术研究包括癌症在内的多因素疾病时, 筛选差异表达基因是最重要的问题之一. 然而, 目前基因芯片研究的样本量较小, 不能完全反映在癌症中广泛的基因表达改变的特点, 导致基于统计学方法筛选的差异表达基因在不同的研究中的重复性很低. 本文通过分析7套癌症数据, 发现每种癌症中都有大范围的功能模块发生了基因表达的改变, 因此这些模块有很强的疾病识别能力. 其中7个功能模块能够有效识别多种癌症的疾病样本, 提示在不同癌症的发生与发展过程中存在着共同的基因表达改变机制. 因此, 功能模块可以作为疾病的功能性的标记来取代在基因芯片应用中难以重复发现的单个基因, 用于研究癌症的核心机制和建立更稳定的诊断分类器.     

遗传与基因表达数据的整合—— eQTL的方法及应用

高通量的基因型分析和芯片技术的发展使人们能够进一步研究哪些遗传差异最终影响基因的表达。通过表达数量性状座位(eQTL)作图方法可对基因表达水平的遗传基础进行解析。与传统的QTL分析方法一样, eQTL的主要目标是鉴别表达性状座位所在的染色体区域。但由于表达谱数据成千上万, 而传统的QTL分析方法最多分析几十个性状, 因此需要考虑这类实验设计的特点以及统计分析方法。本文详细介绍了eQTL定位过程及其研究方法, 重点从个体选择、基因芯片实验设计、基因表达数据的获得与标准化、作图方法及结果分析等方面进行了综述, 指出了当前eQTL研究存在的问题和局限性。最后介绍了eQTL研究在估计基因表达遗传率、挖掘候选基因、构建基因调控网络、理解基因间及基因与环境的互作的应用进展。     

基因芯片技术检测3种肠道病原微生物方法的建立

目的:建立一种运用多重PCR和基因芯片技术检测和鉴定伤寒沙门氏菌、痢疾杆菌和单核细胞增生利斯特菌的方法。方法:分别选取伤寒沙门氏菌染色体ViaB区域中编码调控Vi抗原表达的基因(vipR)、痢疾杆菌编码侵袭质粒抗原H基因(ipaH)和单核细胞增生利斯特菌溶血素基因(hlyA)设计引物和探针,探针3'端进行氨基修饰,下游引物标记荧光素Cy3。在优化的PCR和杂交反应条件下,进行三重PCR扩增,产物与包括3种致病菌特异性探针的基因芯片杂交。在评价基因芯片的特异性和灵敏度之后,对临床样本进行检测。结果:只有3种目的致病菌的PCR产物在相应探针位置出现特异性信号,其他阴性细菌均无信号出现;3种致病菌的检测灵敏度均可达到103CFU/mL;检测30例临床样本的结果与常规细菌学培养结果一致。结论:所建立的可同时检测伤寒沙门氏菌、痢疾杆菌和单核细胞增生利斯特菌的基因芯片方法快速、准确,特异性高,重复性好,为3种肠道致病菌的快速检测和鉴定提供了新方法和新思路。     

基因芯片技术在检测肠道致病菌方面的应用

基因芯片技术具有高通量、自动化、快速检测等特点,因此被广泛地应用于各种研究领域,如细菌分子流行病学、细菌基因鉴定、致病分子机理、基因突变及多态性分析、表达谱分析、DNA测序和药物筛选等。现介绍基因芯片检测肠道致病菌方面的国外研究进展,基因芯片应用于检测肠道致病菌的3个方面:结合多重PCR对致病菌的毒力因子或者特异性基因进行鉴定;直接检测细菌的DNA或者RNA;以致病细菌核糖体RNA作为检测的靶基因同时检测多种肠道致病菌。由于其检测的高效率,该技术要优于其他分子生物学检测方法。基因芯片技术在肠道致病菌检测中有着巨大的应用价值,具有广阔的应用前景。     

MAPK信号通路基因在小鼠肝再生中的表达谱变化

为了分析丝裂原活化蛋白激酶(Mitogen-Activated Protein Kinases,MAPK)信号通路基因在肝再生中的表达图谱,以及探讨MAPK信号通路在肝再生中的作用,文章利用四氯化碳(Carbon Tetrachloride,CCl4)诱导的小鼠肝损伤再生模型对MAPK信号通路基因的表达进行检测.首先,采用CCl4腹腔注射的方法建立小鼠肝损伤再生模型,通过肝脏切片HE染色和测定血清中谷丙转氨酶活性确认模型的质量,然后,在注射CCl4后的第0、0.5、1.5、4.5、7 d分别采集小鼠肝脏样本,应用Affymetrix公司的小鼠基因表达芯片,检测MAPK信号通路中93个基因的差异表达图谱,并用荧光实时定量PCR法验证芯片检测的结果.结果表明,在芯片检测到的93个MAPK信号通路基因中,有31个在肝再生中有不同程度差异表达,且经荧光实时定量RT-PCR检测的结果与基因芯片的结果相符合.基因表达谱芯片技术可以筛选出肝再生中差异表达的基因,在小鼠肝再生中的第0.5和1.5 d,MAPK信号通路中表达水平上调的基因增多,而在第4.5和7 d,则表达水平下调的基因明显增多.这一结果表明MAPK信号通路对肝再生不同阶段的双重调控作用.     

根据基因功能表达谱研究抑郁症模型及安佳欣胶囊的抗抑郁症机制

由基因芯片检测正常大鼠、模型大鼠和服用安佳欣胶囊进行治疗后的大鼠各8例的基因表达谱,在正常组和模型组之间,筛选得到207个差异表达基因和25个差异表达基因功能模块,在模型组和给药组之间,筛选得到860个差异表达基因和24个差异表达基因功能模块。比较两次结果,得到信号传导通路、蛋白质转运等价相同的功能模块,预测此功能模块可能为安佳欣胶囊发挥抗抑郁作用的一部分靶点。在这9个模块中寻找在模型组出现差异表达而在给药组表达趋于正常水平的基因,从基因水平分析药物的治疗机制。     

黄芪甲甙干预大鼠肺纤维化相关基因的时空表达

目的:通过基因芯片技术研究大鼠肺纤维化不同时间点和应用黄芪甲甙干预后的基因差异表达,寻找肺纤维化的致病基因和应用黄芪甲甙进行干预治疗相关的靶基因.方法:用含41000个基因的安捷伦大鼠芯片同模型组7天和模型组28天以及BLM+黄芪甲甙组28天的大鼠肺组织进行杂交.利用安捷伦基因扫描仪扫描杂交图像.模型组7天和BLM+黄芪甲甙组与模型组28天进行比较,筛选Ratio值大于2的差异基因进行分析,重复3次.结果:模型组28天对比7天共有2063个基因表达差异,筛选109个基因,43个上调,66个下调.黄芪甲甙28天组对比模型28天组4269个基因表达差异,筛选68个基因,45个上调,23个下调.通过GO和PATHWAY分析软件,提示有不同的功能分类和信号传导途径.结论:基因芯片为了解肺纤维化不同时间点的基因表达的异常,以及黄芪甲甙治疗肺纤维化的可能机制和药物靶基因的提供了理论基础.     

喉癌发生相关特异基因筛选与验证

 为了检测喉鳞状细胞癌相关的基因表达变化特征,筛选与喉癌发生相关的特异基因. 从3名患者体内分别取正常喉上皮组织和喉鳞状细胞癌组织.应用基因芯片技术进行基因表达差异分析及系统聚类分析,并进行半定量RT PCR验证部分基因芯片结果.本实验基因芯片包括7 26条探针,其中,在3对样本中,表达发生显著差异的基因共有94条,有31条上调,63条下调,并且系统聚类分析将正常和癌组织各分为一类,半定量RT-PCR结果与芯片结果一致.实验表明,细胞的代谢、生长、信号传递相关基因(如RAN、PDCD10、zyxin、TACSTD1等)参与了喉癌的发生、发展,并可能扮演了重要角色     

丙酮醛诱导细胞凋亡相关基因SHMT2L的生物信息学分析

基因芯片是一种高通量地同时研究多个基因表达的有力工具.在用基因芯片技术检测丙酮醛诱导人牙周膜成纤维细胞凋亡的基因表达研究中.发现一个新基因SHMT2L,其表达为显著下调,并推测该基因与细胞凋亡密切相关.利用生物信息学方法对SHMT2L基因及其蛋白产物进行各种分析,结果发现SHMT2L基因与线粒体丝氨酸羟甲基转移酶具有较高的相似性.SHMT2L在细胞中低表达与丙酮醛造成DNA损伤所引发的细胞凋亡相关.     

应用基因芯片筛选慢性乙型肝炎相关差异性基因

目的：应用基因芯片技术筛选不同病毒载量的慢乙肝病人及健康人差异性表达的基因。方法：选用含有48000位点的人类表达谱cDNA基因芯片,筛选4例慢乙肝病人与2例健康人外周血差异性表达的基因。结果：与健康人相比,慢乙肝患者有838个差异表达的基因,其中高表达的基因有150个,低表达的基因有688个。结论：用表达谱基因芯片可有效地研究高、低病毒载量的慢乙肝患者间,以及它们与健康人之间基因表达的差异,通过进一步分析有望筛选出与慢性乙型肝炎相关的新基因靶点。     

Powers of Multiple-Testing Procedures for Identification of Genes Significantly Differentially Expressed in Microarray Experiments

Because of the high operation costs involved in microarray experiments, the determination of the number of replicates required to detect a gene significantly differentially expressed in a given multiple-testing procedure is of considerable significance. Calculation of power/replicate numbers required in multiple-testing procedures provides design guidance for microarray experiments. Based on this model and by choice of a multiple-testing procedure, expression noises based on permutation resampling can be considerably minimized. The method for mixture distribution model is suitable to various microarray data types obtained from single noise sources, or from multiple noise sources. By using the biological replicate number required in microarray experiments for a given power or by determining the power required to detect a gene significantly differentially expressed, given the sample size, or the best multiple-testing method can be chosen. As an example, a single-distribution model of t-statistic was fitted to an observed microarray dataset of 3 000 genes responsive to stroke in rat, and then used to calculate powers of four popular multiple-testing procedures to detect a gene of an expression change D. The results show that the B-procedure had the lowest power to detect a gene of small change among the multiple-testing procedures, whereas the BH-procedure had the highest power. However, all multiple-testing procedures had the same power to identify a gene having the largest change. Similar to a single test, the power of the BH-procedure to detect a small change does not vary as the number of genes increases, but powers of the other three multiple-testing procedures decline as the number of genes increases.     

How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach

Background  

It has been recognized that replicates of arrays (or spots) may be necessary for reliably detecting differentially expressed genes in microarray experiments. However, the often-asked question of how many replicates are required has barely been addressed in the literature. In general, the answer depends on several factors: a given magnitude of expression change, a desired statistical power (that is, probability) to detect it, a specified Type I error rate, and the statistical method being used to detect the change. Here, we discuss how to calculate the number of replicates in the context of applying a nonparametric statistical method, the normal mixture model approach, to detect changes in gene expression.     

Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments

MOTIVATION: We present statistical methods for determining the number of per gene replicate spots required in microarray experiments. The purpose of these methods is to obtain an estimate of the sampling variability present in microarray data, and to determine the number of replicate spots required to achieve a high probability of detecting a significant fold change in gene expression, while maintaining a low error rate. Our approach is based on data from control microarrays, and involves the use of standard statistical estimation techniques. RESULTS: After analyzing two experimental data sets containing control array data, we were able to determine the statistical power available for the detection of significant differential expression given differing levels of replication. The inclusion of replicate spots on microarrays not only allows more accurate estimation of the variability present in an experiment, but more importantly increases the probability of detecting genes undergoing significant fold changes in expression, while substantially decreasing the probability of observing fold changes due to chance rather than true differential expression.     

Microarray segmentation methods significantly influence data precision

Little consideration has been given to the effect of different segmentation methods on the variability of data derived from microarray images. Previous work has suggested that the significant source of variability from microarray image analysis is from estimation of local background. In this study, we used Analysis of Variance (ANOVA) models to investigate the effect of methods of segmentation on the precision of measurements obtained from replicate microarray experiments. We used four different methods of spot segmentation (adaptive, fixed circle, histogram and GenePix) to analyse a total number of 156 172 spots from 12 microarray experiments. Using a two-way ANOVA model and the coefficient of repeatability, we show that the method of segmentation significantly affects the precision of the microarray data. The histogram method gave the lowest variability across replicate spots compared to other methods, and had the lowest pixel-to-pixel variability within spots. This effect on precision was independent of background subtraction. We show that these findings have direct, practical implications as the variability in precision between the four methods resulted in different numbers of genes being identified as differentially expressed. Segmentation method is an important source of variability in microarray data that directly affects precision and the identification of differentially expressed genes.     

EVE (external variance estimation) increases statistical power for detecting differentially expressed genes

Accurately identifying differentially expressed genes from microarray data is not a trivial task, partly because of poor variance estimates of gene expression signals. Here, after analyzing 380 replicated microarray experiments, we found that probesets have typical, distinct variances that can be estimated based on a large number of microarray experiments. These probeset-specific variances depend at least in part on the function of the probed gene: genes for ribosomal or structural proteins often have a small variance, while genes implicated in stress responses often have large variances. We used these variance estimates to develop a statistical test for differentially expressed genes called EVE (external variance estimation). The EVE algorithm performs better than the t-test and LIMMA on some real-world data, where external information from appropriate databases is available. Thus, EVE helps to maximize the information gained from a typical microarray experiment. Nonetheless, only a large number of replicates will guarantee to identify nearly all truly differentially expressed genes. However, our simulation studies suggest that even limited numbers of replicates will usually result in good coverage of strongly differentially expressed genes.     

Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays

MOTIVATION: A crucial step in microarray data analysis is the selection of subsets of interesting genes from the initial set of genes. In many cases, especially when comparing a specific condition to a reference, the genes of interest are those which are differentially expressed. Two common methods for gene selection are: (a) selection by fold difference (at least n fold variation) and (b) selection by altered ratio (at least n standard deviations away from the mean ratio). RESULTS: The novel method proposed here is based on ANOVA and uses replicate spots to estimate an empirical distribution of the noise. The measured intensity range is divided in a number of intervals. A noise distribution is constructed for each such interval. Bootstrapping is used to map the desired confidence levels from the noise distribution corresponding to a given interval to the measured log ratios in that interval. If the method is applied on individual arrays having replicate spots, the method can calculate an overall width of the noise distribution which can be used as an indicator of the array quality. We compared this method with the fold change and unusual ratio method. We also discuss the relationship with an ANOVA model proposed by Churchill et al. In silico experiments were performed while controlling the degree of regulation as well as the amount of noise. Such experiments show the performance of the classical methods can be very unsatisfactory. We also compared the results of the 2-fold method with the results of the noise sampling method using pre and post immortalization cell lines derived from the MDAH041 fibroblasts hybridized on Affymetrix GeneChip arrays. The 2-fold method reported 198 genes as upregulated and 493 genes as downregulated. The noise sampling method reported 98 gene upregulated and 240 genes downregulated at the 99.99% confidence level. The methods agreed on 221 genes downregulated and 66 genes upregulated. Fourteen genes from the subset of genes reported by both methods were all confirmed by Q-RT-PCR. Alternative assays on various subsets of genes on which the two methods disagreed suggested that the noise sampling method is likely to provide fewer false positives.     

Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis

MOTIVATION: The field of microarray data analysis is shifting emphasis from methods for identifying differentially expressed genes to methods for identifying differentially expressed gene categories. The latter approaches utilize a priori information about genes to group genes into categories and enhance the interpretation of experiments aimed at identifying expression differences across treatments. While almost all of the existing approaches for identifying differentially expressed gene categories are practically useful, they suffer from a variety of drawbacks. Perhaps most notably, many popular tools are based exclusively on gene-specific statistics that cannot detect many types of multivariate expression change. RESULTS: We have developed a nonparametric multivariate method for identifying gene categories whose multivariate expression distribution differs across two or more conditions. We illustrate our approach and compare its performance to several existing procedures via the analysis of a real data set and a unique data-based simulation study designed to capture the challenges and complexities of practical data analysis. We show that our method has good power for differentiating between differentially expressed and non-differentially expressed gene categories, and we utilize a resampling based strategy for controlling the false discovery rate when testing multiple categories. AVAILABILITY: R code (www.r-project.org) for implementing our approach is available from the first author by request.     

Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments

One of the main objectives in the analysis of microarray experiments is the identification of genes that are differentially expressed under two experimental conditions. This task is complicated by the noisiness of the data and the large number of genes that are examined simultaneously. Here, we present a novel technique for identifying differentially expressed genes that does not originate from a sophisticated statistical model but rather from an analysis of biological reasoning. The new technique, which is based on calculating rank products (RP) from replicate experiments, is fast and simple. At the same time, it provides a straightforward and statistically stringent way to determine the significance level for each gene and allows for the flexible control of the false-detection rate and familywise error rate in the multiple testing situation of a microarray experiment. We use the RP technique on three biological data sets and show that in each case it performs more reliably and consistently than the non-parametric t-test variant implemented in Tusher et al.'s significance analysis of microarrays (SAM). We also show that the RP results are reliable in highly noisy data. An analysis of the physiological function of the identified genes indicates that the RP approach is powerful for identifying biologically relevant expression changes. In addition, using RP can lead to a sharp reduction in the number of replicate experiments needed to obtain reproducible results.     

A Regression-Based Differential Expression Detection Algorithm for Microarray Studies with Ultra-Low Sample Size

Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.     

Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions

Genome-scale microarray experiments for comparative analysis of gene expressions produce massive amounts of information. Traditional statistical approaches fail to achieve the required accuracy in sensitivity and specificity of the analysis. Since the problem can be resolved neither by increasing the number of replicates nor by manipulating thresholds, one needs a novel approach to the analysis. This article describes methods to improve the power of microarray analyses by defining internal standards to characterize features of the biological system being studied and the technological processes underlying the microarray experiments. Applying these methods, internal standards are identified and then the obtained parameters are used to define (i) genes that are distinct in their expression from background; (ii) genes that are differentially expressed; and finally (iii) genes that have similar dynamical behavior.