Objective method of comparing DNA microarray image analysis systems

Many image analysis systems are available for processing the images produced by laser scanning of DNA microarrays. The image processing system takes pixel-level intensity data and converts it to a set of gene-level expression or copy number summaries that will be used in further analyses. Image analysis systems currently in use differ with regard to the specific algorithms they implement, ease of use, and cost. Thus, it would be desirable to have an objective means of comparing systems. Here we describe a systematic method of comparing image processing results produced by different image analysis systems using a series of replicate microarray experiments. We demonstrate the method with a comparison of cDNA microarray data generated by the UCSF Spot and the GenePix image processing systems.     

Empirical evaluation of data transformations and ranking statistics for microarray analysis

There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics outperform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.     

Expression of class I knotted1-like homeobox genes in the storage roots of sweetpotato (Ipomoea batatas)

As a first step in clarifying the involvement of class I knotted1-like homeobox (KNOXI) genes in the storage root development of sweetpotato (Ipomoea batatas), we isolated three KNOXI genes, named Ibkn1, Ibkn2 and Ibkn3, expressed in the storage roots. Phylogenetic analysis showed that Ibkn1 was homologous to the SHOOT MERISTEMLESS (STM) gene of Arabidopsis, while Ibkn2 and Ibkn3 were homologous to the BREVIPEDICELLUS (BP) gene. Of these, expression of Ibkn1 and Ibkn2 were upregulated in developing and mature storage roots compared with fibrous roots. Ibkn1 and Ibkn2 showed different expression patterns in the storage roots. Ibkn1 was preferentially expressed at the proximal end and around the primary vascular cambium, while Ibkn2 expression was highest in the thickest part and lower in both the proximal and distal ends. In contrast to Ibkn1 and Ibkn2, expression of Ibkn3 in roots was not consistent among sweetpotato cultivars. The distribution of endogenous trans-zeatin riboside (t-ZR) in sweetpotato roots showed a similarity to the expression pattern of KNOXI genes, supporting the idea that KNOXI genes control cytokinin levels in the storage roots. The physiological functions of these KNOXI genes in storage root development are discussed.     

Finding disease specific alterations in the co-expression of genes

MOTIVATION: Standard analysis routines for microarray data aim at differentially expressed genes. In this paper, we address the complementary problem of detecting sets of differentially co-expressed genes in two phenotypically distinct sets of expression profiles. RESULTS: We introduce a score for differential co-expression and suggest a computationally efficient algorithm for finding high scoring sets of genes. The use of our novel method is demonstrated in the context of simulations and on real expression data from a clinical study.     

The use of microarray technology for the analysis of Streptococcus pneumoniae

Streptococcus pneumoniae is an important human pathogen associated with pneumonia, septicaemia, meningitis and otitis media. It is estimated to result in over 3 million child deaths worldwide every year and an even greater number of deaths among the elderly. Prior to the complete sequencing of the genomes of S. pneumoniae TIGR4 (serotype 4) and S. pneumoniae R6 (serotype 2), we designed a custom miniarray consisting of 497 pneumococcal genes. The overall objectives of our microarray investigations were, first, to assess the genetic diversity between different S. pneumoniae serotypes, clinical isolates and also different Streptococcus species; second, we aimed to use microarray technology to examine the mechanisms by which environmental factors influence pneumococcal gene expression, and ultimately to further the understanding of how these changes in gene expression are achieved and how they may alter the virulence of the organism.     

Bayesian Joint Analysis of Gene Expression Data and Gene Functional Annotations

Identifying which genes and which gene sets are differentially expressed (DE) under two experimental conditions are both key questions in microarray analysis. Although closely related and seemingly similar, they cannot replace each other, due to their own importance and merits in scientific discoveries. Existing approaches have been developed to address only one of the two questions. Further, most of the methods for detecting DE genes purely rely on gene expression analysis, without using the information about gene functional grouping. Methods for detecting altered gene sets often use a two-step procedure, of which the first step conducts differential expression analysis using expression data only, and the second step takes results from the first step and tries to examine whether each predefined gene set is overrepresented by DE genes through some testing procedure. Such a sequential manner in analysis might cause information loss by just focusing on summary results without using the entire expression data in the second step. Here, we propose a Bayesian joint modeling approach to address the two key questions in parallel, which incorporates the information of functional annotations into expression data analysis and meanwhile infer the enrichment of functional groups. Simulation results and analysis of experimental data obtained for E.?coli show improved statistical power of our integrated approach in both identifying DE genes and altered gene sets, when compared to conventional methods.     

Normality of oligonucleotide microarray data and implications for parametric statistical analyses

MOTIVATION: Experimental limitations have resulted in the popularity of parametric statistical tests as a method for identifying differentially regulated genes in microarray data sets. However, these tests assume that the data follow a normal distribution. To date, the assumption that replicate expression values for any gene are normally distributed, has not been critically addressed for Affymetrix GeneChip data. RESULTS: The normality of the expression values calculated using four different commercial and academic software packages was investigated using a data set consisting of the same target RNA applied to 59 human Affymetrix U95A GeneChips using a combination of statistical tests and visualization techniques. For the majority of probe sets obtained from each analysis suite, the expression data showed a good correlation with normality. The exception was a large number of low-expressed genes in the data set produced using Affymetrix Microarray Suite 5.0, which showed a striking non-normal distribution. In summary, our data provide strong support for the application of parametric tests to GeneChip data sets without the need for data transformation.     

Bayesian hierarchical error model for analysis of gene expression data

MOTIVATION: Analysis of genome-wide microarray data requires the estimation of a large number of genetic parameters for individual genes and their interaction expression patterns under multiple biological conditions. The sources of microarray error variability comprises various biological and experimental factors, such as biological and individual replication, sample preparation, hybridization and image processing. Moreover, the same gene often shows quite heterogeneous error variability under different biological and experimental conditions, which must be estimated separately for evaluating the statistical significance of differential expression patterns. Widely used linear modeling approaches are limited because they do not allow simultaneous modeling and inference on the large number of these genetic parameters and heterogeneous error components on different genes, different biological and experimental conditions, and varying intensity ranges in microarray data. RESULTS: We propose a Bayesian hierarchical error model (HEM) to overcome the above restrictions. HEM accounts for heterogeneous error variability in an oligonucleotide microarray experiment. The error variability is decomposed into two components (experimental and biological errors) when both biological and experimental replicates are available. Our HEM inference is based on Markov chain Monte Carlo to estimate a large number of parameters from a single-likelihood function for all genes. An F-like summary statistic is proposed to identify differentially expressed genes under multiple conditions based on the HEM estimation. The performance of HEM and its F-like statistic was examined with simulated data and two published microarray datasets-primate brain data and mouse B-cell development data. HEM was also compared with ANOVA using simulated data. AVAILABILITY: The software for the HEM is available from the authors upon request.     

Gene Expression Analysis Platform (GEAP): A highly customizable, fast,versatile and ready-to-use microarray analysis platform

There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.     

A Combinational Clustering Based Method for cDNA Microarray Image Segmentation

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi’s individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods.     

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.     

实时荧光定量PCR分析中毛果杨内参基因的筛选和验证

实时荧光定量PCR(qRT-PCR)技术具有高灵敏性、高保真性和高特异性, 被广泛应用于基因表达的分析。在数据处理过程中, 选用稳定表达的基因作为内参基因对准确分析实验结果非常关键。以毛果杨(Populus trichocarpa)的不同组织以及锌胁迫下的组培苗为材料, 使用荧光定量PCR方法分析了TUA8、TUB6、ubiquitin、GAPDH、actin、18S rRNA和EF1α 7个看家基因的表达情况。通过geNorm、NormFinder和BestKeeper 3个程序的综合分析, 发现actin、ubiquitin、EF1α和18S rRNA的稳定性较好, 可用作毛果杨基因表达研究的内参基因; 而TUB6在不同组织中稳定性最差; GAPDH在锌胁迫下的组织中稳定性最差, 因此不适宜作为内参基因。毛果杨NAC基因的表达分析, 进一步验证了上述结果。该研究对采用qRT-PCR方法分析毛果杨基因表达过程中内参基因的选择具有指导作用, 同时对揭示NAC基因的功能也有一定的意义。     

植物乳杆菌KLDS1.0728质粒p141的分子分析

对分离自植物乳杆菌KLDS1.0728的质粒p141进行了全序列测定,结果显示,该质粒全长为3597bp,平均G+Cmol%值为38%,并利用DNAMAN6.0软件得到该质粒限制性内切酶图谱。经NCBI网站ORFFinder软件分析确定其编码序列即ORF为15个,通过与公共数据库比对,发现可以识别功能的ORF有2个,其中包括质粒复制所必需的rep基因,p141的rep基因与已知序列的植物乳杆菌WCFS1内源质粒pWCFS101以及植物乳杆菌内源质粒pM4的复制蛋白基因相似性高达91%。根据rep基因的相似性比较,判断p141的复制模式归属于RCR模式的GroupIII组,即pC194家族。另外,还发现质粒p141中存在mob基因,表明该质粒具有水平转移能力。但没有发现Tn4430转座子以及转座酶基因topl和topA,所以可以判断该质粒的基因比较稳定。此外,还发现该质粒序列中存在一定的与质粒复制和转移调控以及蛋白质表达等有关的重复序列,其对调控质粒的拷贝数有一定意义。