MOF： An R Function to Detect Outlier Microarray

We developed an R function named ＂microarray outlier filter＂ （MOF） to assist in the identification of faUed arrays. In sorting a group of similar arrays by the likelihood of failure, two statistical indices were employed： the correlation coefficient and the percentage of outlier spots. MOF can be used to monitor the quality of microarray data for both trouble shooting, and to eliminate bad datasets from downstream analysis. The function is freely avaliable at http：//www.wriwindber.org/ applications/mof/.     

Resource and hardware options for microarray-based experimentation.

DNA microarray technology permits the study of biological systems and processes on a genome-wide scale. Arrays based on cDNA clones, oligonucleotides and genomic clones have been developed for investigations of gene expression, genetic analysis and genomic changes associated with disease. Over the past 3-4 years, microarrays have become more widely available to the research community. This has occurred through increased commercial availability of custom and generic arrays and the development of robotic equipment that has enabled array printing and analysis facilities to be established in academic research institutions. This brief review examines the public and commercial resources, the microarray fabrication and data capture and analysis equipment currently available to the user.     

基因芯片数据分析过程:从原始数据到生物学意义

基因芯片实验要得到可靠的生物学结论,必须基于优化的实验设计和科学的数据分析。讨论了与基因芯片数据分析方法相关的实验设计方面的几个问题,简述了差异表达分析、聚类分析及功能富集分析等分析方法及其进展,并介绍了部分软件及应用。     

DNA microarray technology: a new tool for the epidemiological typing of bacterial pathogens?

Genomic hybridization on whole genome arrays detects the presence or absence of similar DNA regions in sufficiently related microorganisms, allowing genome-wide comparison of their genetic contents. A whole genome array is based on a sequenced bacterial isolate, and is a collection of DNA probes fixed on a solid support. In a single hybridization experiment, the absence/presence status of all genes of the sequenced microbe in the queried isolate can be examined. The objective of this minireview is to summarize the past usage of DNA microarray technology for microbial strain characterizations, and to estimate its future utilization in epidemiological studies and molecular typing of bacterial pathogens. The studies reviewed here confirm the usefulness of microarray technology for the detection of genetic polymorphisms. However, the construction or purchase of DNA microarrays and the performance of strain to strain hybridization experiments are still prohibitively expensive for routine application. Future use of arrays in epidemiology is likely to depend on the development of more cost-effective protocols, more robust and simplified formats, and the adequate evaluation of their performance (efficacy) and convenience (efficiency) compared with other genotyping methods. It seems more likely that a more focused assay, concentrating on genomic regions of variability previously detected by genome-wide microarrays, will find broad application in routine bacterial epidemiology.     

基于夹心免疫分析的抗体微阵列的构建

目的：建立一种基于夹心免疫分析的抗体微阵列构建的优化方法。方法：将MCP-1的捕获抗体点样于修饰后的玻片,标准抗原加样覆盖所点阵列,生物素标记抗体和链酶亲和素-cy3依次加样孵育, 激光共聚焦扫描仪获取图象并进行数据分析。对捕获抗体浓度、封闭液种类、系统可重复性和定量检测能力、两种因子平行性检测对信号分析的影响及点样后玻片稳定性进行分析和评价。结果：随着捕获抗体浓度的升高,信号强度逐渐增加;2℅ BSA/PBS和5℅ 酪蛋白可作为本系统的封闭液;所构建系统具有较好的可重复性（组内变异 1.3%,组间变异8.7%）和定量分析能力（所建立的抗原浓度-相对信号强度标准曲线相关系数达0.9995）;并实现了两因子的平行性分析和点样后玻片的稳定性。结论：确立了基于夹心免疫分析的抗体微阵列构建的优化方法,为进一步构建多因子定量检测抗体微阵列奠定了基础。     

Propagating uncertainty in microarray data analysis

Microarray technology is associated with many sources of experimentaluncertainty. In this review we discuss a number of approachesfor dealing with this uncertainty in the processing of datafrom microarray experiments. We focus here on the analysis ofhigh-density oligonucleotide arrays, such as the popular AffymetrixGeneChip® array, which contain multiple probes for eachtarget. This set of probes can be used to determine an estimatefor the target concentration and can also be used to determinethe experimental uncertainty associated with this measurement.This measurement uncertainty can then be propagated throughthe downstream analysis using probabilistic methods. We giveexamples showing how these credibility intervals can be usedto help identify differential expression, to combine informationfrom replicated experiments and to improve the performance ofprincipal component analysis.      

Split-Plot Microarray Experiments

This article focuses on microarray experiments with two or more factors in which treatment combinations of the factors corresponding to the samples paired together onto arrays are not completely random. A main effect of one (or more) factor(s) is confounded with arrays (the experimental blocks). This is called a split-plot microarray experiment. We utilise an analysis of variance (ANOVA) model to assess differentially expressed genes for between-array and within-array comparisons that are generic under a split-plot microarray experiment. Instead of standard t- or F-test statistics that rely on mean square errors of the ANOVA model, we use a robust method, referred to as 'a pooled percentile estimator', to identify genes that are differentially expressed across different treatment conditions. We illustrate the design and analysis of split-plot microarray experiments based on a case application described by Jin et al. A brief discussion of power and sample size for split-plot microarray experiments is also presented.     

Current issues for DNA microarrays: platform comparison, double linear amplification, and universal RNA reference

DNA microarray technology has been widely used to simultaneously determine the expression levels of thousands of genes. A variety of approaches have been used, both in the implementation of this technology and in the analysis of the large amount of expression data. However, several practical issues still have not been resolved in a satisfactory manner, and among the most critical is the lack of agreement in the results obtained in different array platforms. In this study, we present a comparison of several microarray platforms [Affymetrix oligonucleotide arrays, custom complementary DNA (cDNA) arrays, and custom oligo arrays printed with oligonucleotides from three different sources] as well as analysis of various methods used for microarray target preparation and the reference design. The results indicate that the pairwise correlations of expression levels between platforms are relative low overall but that the log ratios of the highly expressed genes are strongly correlated, especially between Affymetrix and cDNA arrays. The microarray measurements were compared with quantitative real-time-polymerase chain reaction (QRT-PCR) results for 23 genes, and the varying degrees of agreement for each platform were characterized. We have also developed and tested a double amplification method which allows the use of smaller amounts of starting material. The added round of amplification produced reproducible results as compared to the arrays hybridized with single round amplified targets. Finally, the reliability of using a universal RNA reference for two-channel microarrays was tested and the results suggest that comparisons of multiple experimental conditions using the same control can be accurate.     

基因芯片及其在环境微生物研究中的应用

基因芯片因其具有高密度、高灵敏度、快速 (实时 )检测、经济、自动化和低背景水平等特点 ,而广泛应用于不同的研究领域。目前 ,应用于环境微生物研究的基因芯片主要有功能基因芯片 (FGAs)、系统发育的寡核苷酸芯片 (POAs)和群落基因组芯片 (CGAs)。综述了基因芯片在环境微生物研究中的应用 ,包括自然环境中微生物的基因表达分析、比较基因组分析和混合微生物群落的分析等。讨论了基因芯片面临的挑战和前景展望     

D-MaPs - DNA-microarray projects: Web-based software for multi-platform microarray analysis

The web application D-Maps provides a user-friendly interface to researchers performing studies based on microarrays. The program was developed to manage and process one- or two-color microarray data obtained from several platforms (currently, GeneTAC, ScanArray, CodeLink, NimbleGen and Affymetrix). Despite the availability of many algorithms and many software programs designed to perform microarray analysis on the internet, these usually require sophisticated knowledge of mathematics, statistics and computation. D-maps was developed to overcome the requirement of high performance computers or programming experience. D-Maps performs raw data processing, normalization and statistical analysis, allowing access to the analyzed data in text or graphical format. An original feature presented by D-Maps is GEO (Gene Expression Omnibus) submission format service. The D-MaPs application was already used for analysis of oligonucleotide microarrays and PCR-spotted arrays (one- and two-color, laser and light scanner). In conclusion, D-Maps is a valuable tool for microarray research community, especially in the case of groups without a bioinformatic core.     

Integrating biological information into the statistical analysis and design of microarray experiments

Microarray technology is a powerful tool for animal functional genomics studies, with applications spanning from gene identification and mapping, to function and control of gene expression. Microarray assays, however, are complex and costly, and hence generally performed with relatively small number of animals. Nevertheless, they generate data sets of unprecedented complexity and dimensionality. Therefore, such trials require careful planning and experimental design, in addition to tailored statistical and computational tools for their appropriate data mining. In this review, we discuss experimental design and data analysis strategies, which incorporate prior genomic and biological knowledge, such as genotypes and gene function and pathway membership. We focus the discussion on the design of genetical genomics studies, and on significance testing for detection of differential expression. It is shown that the use of prior biological information can improve the efficiency of microarray experiments.     

Strategies for microarray analysis of limiting amounts of RNA.

One of the critical limitations of current microarray technologies for use in expression analyses is the relatively large amount of input RNA required to generate labelled cDNA populations for array analysis. In situations where RNA is limiting, the options for expression profiling are to increase cDNA labelling and hybridisation efficiency, or to use an amplification strategy to generate enough RNA/cDNA for use with a standard labelling method. Sample amplification approaches must preserve the representation of the relative abundances of the different RNAs within the starting population and must also be highly reproducible. This review evaluates current signal and sample amplification technologies, including those that can be used to generate labelled cDNA populations for array analysis from as little as a single cell.     

基于组合探针识别的大规模细菌分类16 S rRNA基因芯片设计

随着16 S rRNA序列资源的不断丰富,以及寡核苷酸微阵列基因芯片技术的不断进步,检测复杂微生物菌落中的微生物种群构成成为可能．现有的序列特异性探针设计算法缺乏足够的覆盖度、灵活性以及效率,不能满足大规模细菌检测基因芯片的设计要求．很多组特异性探针设计算法的思路多局限于针对某个目标序列组设计唯一的组特异性探针．在很多应用场合,设计单个探针检测组内所有目标序列的目标是很难达到的．因此,设计多个探针通过组合方式进行检测是很有必要的．每个探针能特异性地检测组内一部分目标序列,通过组合就能提高覆盖率．然而,在所有可能的探针组合中找到一个优化的探针组合是很耗时的．提出了一个可行的基于相对熵和遗传算法的组合探针设计算法．