The statistics of identifying differentially expressed genes in Expresso and TM4: a comparison

Background  

Analysis of DNA microarray data takes as input spot intensity measurements from scanner software and returns differential expression of genes between two conditions, together with a statistical significance assessment. This process typically consists of two steps: data normalization and identification of differentially expressed genes through statistical analysis. The Expresso microarray experiment management system implements these steps with a two-stage, log-linear ANOVA mixed model technique, tailored to individual experimental designs. The complement of tools in TM4, on the other hand, is based on a number of preset design choices that limit its flexibility. In the TM4 microarray analysis suite, normalization, filter, and analysis methods form an analysis pipeline. TM4 computes integrated intensity values (IIV) from the average intensities and spot pixel counts returned by the scanner software as input to its normalization steps. By contrast, Expresso can use either IIV data or median intensity values (MIV). Here, we compare Expresso and TM4 analysis of two experiments and assess the results against qRT-PCR data.     

Development of a toxicological gene array and quantitative assessment of this technology

High-density arrays of DNA bound to solid substrates offer a powerful approach to identifying changes in gene expression in response to toxicants. While DNA arrays have been used to explore qualitative changes in gene regulation, less attention has focused on the quantitative aspects of this technology. Arrays containing expressed sequence tags for xenobiotic metabolizing enzymes, proteins associated with glutathione regulation, DNA repair enzymes, heat shock proteins, and housekeeping genes were used to examine gene expression in response to beta-naphthoflavone (beta-NF). Upregulation of cytochrome P4501a1 (Cyp1a1) and 1a2 in mouse liver was maximal 8 h after beta-NF administration. Significant upregulation of Cyp1a2 was noted at beta-NF doses as low as 0.62 and 1.2 mg/kg when gene expression was measured by microarray or Northern blotting, respectively. Maximal Cyp1a2 induction is 5-fold by Northern analysis and 10-fold by microarray. Induction of Cyp1a1 was 15- and 20-fold by Northern and microarray analysis, respectively. The coefficient of variation for spot to spot and slide to slide comparisons was <15%; this variability was smaller than interanimal variability (18-60%). Comparison of mRNA expression in control animals indicated that there are differences in labeling/detection associated with Cy3/Cy5 dyes; accordingly, experiments must include methods for establishing baseline signals for all genes. We conclude that the dynamic range and sensitivity of DNA microarrays on glass slides is comparable to Northern blotting analysis and that variability of the data introduced during spotting and hybridization is less than the interanimal variability.     

DNA microarray normalization methods can remove bias from differential protein expression analysis of 2D difference gel electrophoresis results

MOTIVATION: Two-dimensional Difference Gel Electrophoresis (DIGE) measures expression differences for thousands of proteins in parallel. In contrast to DNA microarray analysis, however, there have been few systematic studies on the validity of differential protein expression analysis, and the effects of normalization methods have not yet been investigated. To address this need, we assessed a series of same-same comparisons, evaluating how random experimental variance influenced differential expression analysis. RESULTS: The strong fluctuations observed were reflected in large discrepancies between the distributions of the spot intensities for different gels. Correct normalization for pooling of multiple gels for analysis is, therefore, essential. We show that both dye-specific background levels and the differences in scale of the spot intensity distributions must be accounted for. A variance stabilizing transform that had been developed for DNA microarray analysis combined with a robust Z-score allowed the determination of gel-independent signal thresholds based on the empirical distributions from same-same comparisons. In contrast, similar thresholds holding up to cross-validation could not be proposed for data normalized using methods established in the field of proteomics. AVAILABILITY: Software is available on request from the authors. SUPPLEMENTARY INFORMATION: There is supplementary material available online at http://www.flychip.org.uk/kreil/pub/2dgels/     

Genome-wide expression analysis of plant cell cycle modulated genes

Genome-wide expression analysis is rapidly becoming an essential tool for identifying and analysing genes involved in, or controlling, various biological processes ranging from development to responses to environmental cues. The control of cell division involves the temporal expression of different sets of genes, allowing the dividing cell to progress through the different phases of the cell cycle. A landmark study using DNA microarrays to follow the patterns of gene expression in synchronously dividing yeast cells has allowed the identification of several hundreds of genes that are involved in the cell cycle. Although DNA microarrays provide a convenient tool for genome-wide expression analysis, their use is limited to organisms for which the complete genome sequence or a large cDNA collection is available. For other organisms, including most plant species, DNA fragment analysis based methods, such as cDNA-AFLP, provide a more appropriate tool for genome-wide expression analysis. Furthermore, cDNA-AFLP exhibits properties that complement DNA microarrays and, hence, constitutes a useful tool for gene discovery.     

Processing and quality control of DNA array hybridization data

MOTIVATION: The technology of hybridization to DNA arrays is used to obtain the expression levels of many different genes simultaneously. It enables searching for genes that are expressed specifically under certain conditions. However, the technology produces large amounts of data demanding computational methods for their analysis. It is necessary to find ways to compare data from different experiments and to consider the quality and reproducibility of the data. RESULTS: Data analyzed in this paper have been generated by hybridization of radioactively labeled targets to DNA arrays spotted on nylon membranes. We introduce methods to compare the intensity values of several hybridization experiments. This is essential to find differentially expressed genes or to do pattern analysis. We also discuss possibilities for quality control of the acquired data. AVAILABILITY: http://www.dkfz.de/tbi CONTACT: M.Vingron@dkfz-heidelberg.de     

Mapping physiological states from microarray expression measurements

MOTIVATION: The increasing use of DNA microarrays to probe cell physiology requires methods for visualizing different expression phenotypes and explicitly connecting individual genes to discriminating expression features. Such methods should be robust and maintain biological interpretability. RESULTS: We propose a method for the mapping of the physiological state of cells and tissues from multidimensional expression data such as those obtained with DNA microarrays. The method uses Fisher discriminant analysis to create a linear projection of gene expression measurements that maximizes the separation of different sample classes. Relative to other typical classification methods, this method provides insights into the discriminating characteristics of expression measurements in terms of the contribution of individual genes to the definition of distinct physiological states. This projection method also facilitates visualization of classification results in a reduced dimensional space. Examples from four different cases demonstrate the ability of the method to produce well-separated groups in the projection space and to identify important genes for defining physiological states. The method can be augmented to also include data from the proteomic and metabolic phenotypes and can be useful in disease diagnosis, drug screening and bioprocessing applications.     

Segmentation and detection of fluorescent 3D spots

The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in-situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top-hat filtering, and intensity thresholding, followed by 3D region-growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots as either true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques.     

Partial Genome Scale Analysis of Gene Expression in Human Adipose Tissue Using DNA Array

Objective: Large scale analysis of gene expression in adipose tissue provides a basis for the identification of novel candidate genes involved in the pathophysiology of obesity. Our goal was to explore gene expression in human adipose tissue at a partial genome scale using DNA array. Research Methods and Procedures: Labeled cDNA, derived from human adipose tissue poly(A⁺) RNA, was hybridized to a DNA array containing over 18,000 human expressed sequence‐tagged (EST) clones. The results were analyzed by database searches. Results: Homology searches of the 300 EST clones with highest hybridization signals revealed that 145 contained DNA sequences identical to known genes and 79 could be linked to UniGene clusters. Of the 145 identified genes, 136 were nonredundant and subsequently characterized with respect to function and chromosomal localization by searching MEDLINE, UniGene, GeneMap, OMIM, SWISS‐PROT, the Genome Database, and the Location Data Base. The identified genes were grouped according to their putative functions; cell/organism defense (9.6%), cell division (5.1%), cell signaling/communication (19.8%), cell structure/motility (12.5%), gene/protein expression (16.9%), metabolism (16.2%), and unclassified (19.8%). Less than 50% of these genes have previously been reported to be expressed in adipose tissue. The chromosomal localization of 268 genes strongly expressed in adipose tissue showed that their relative abundance was significantly increased on chromosomes 11, 19, and 22 compared to the expected distribution of the same number of random genes. Discussion: Our study resulted in the identification of numerous genes previously not reported to be expressed in adipose tissue. These results suggest that DNA array is a powerful tool in the search for novel regulatory pathways within adipose tissue on a scale that is not possible using conventional methods.     

Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation

MOTIVATION: The whole genomes submitted to GenBank contain valuable information about the function of genes as well as the upstream sequences and whole cell expression provides valuable information on gene regulation. To utilize these large amounts of data for a biological understanding of the regulation of gene expression, new automatic methods for pattern finding are needed. RESULTS: Two word-analysis algorithms for automatic discovery of regulatory sequence elements have been developed. We show that sequence patterns correlated to whole cell expression data can be found using Kolmogorov-Smirnov tests on the raw data, thereby eliminating the need for clustering co-regulated genes. Regulatory elements have also been identified by systematic calculations of the significance of correlations between words found in the functional annotation of genes and DNA words occurring in their promoter regions. Application of these algorithms to the Saccharomyces cerevisiae genome and publicly available DNA array data sets revealed a highly conserved 9-mer occurring in the upstream regions of genes coding for proteasomal subunits. Several other putative and known regulatory elements were also found. AVAILABILITY: Upon request.     

Effect of irradiation on the expression of DNA repair genes studied in human fibroblasts by real-time qPCR using three methods of reference gene validation

The aim of this study was to determine the effects of ionizing radiation on gene expression by using for a first time a qPCR platform specifically established for the detection of 94 DNA repair genes but also to test the robustness of these results by using three analytical methods (global pattern recognition, ΔΔCq/Normfinder and ΔΔCq/Genorm). Study was focused on these genes because DNA repair is known primarily to determine the radiation response. Six strains of normal human fibroblasts were exposed to 2 Gy, and changes in gene expression were analyzed 24 h thereafter. A significant change in gene expression was found for only few genes, but the genes detected were mostly different for the three analytical methods used. For GPR, a significant change was found for four genes, in contrast to the eight or nine genes when applying ΔΔCq/Genorm or ΔΔCq/Normfinder, respectively. When using all three methods, a significant change in expression was only seen for GADD45A and PCNA. These data demonstrate that (1) the genes identified to show an altered expression upon irradiation strongly depend on the analytical method applied, and that (2) overall GADD45A and PCNA appear to play a central role in this response, while no significant change is induced for any of the other DNA repair genes tested.     

基因网络中基因关系图谱的构建及其应用

尽快破解基因组所包含基因的功能是一项费力但又很重要的工作。一个基因功能的实现依赖于该基因与其它基因间的相互作用。基因网络是一组基因的集合体,这些基因通过相互协作来控制生物体重要的生命过程。通过基因敲除、RNA干扰或其它方法改变基因网络中某个基因的表达水平,将会引起该网络中其它基因表达水平的变化。而这种变化可以方便地通过基因表达差异显示技术检测相应mRNA含量变化来反映。因此,将这两类方法组合在一起,可以在基因组水平上有效地检测出基因网络中的基因关系。这种策略对基因功能研究方法是一个重要补充。     

金黄色葡萄球菌基因表达的DNA扣除法

[目的]金黄色葡萄球菌作为一种分布广泛的致病微生物和研究革兰氏阳性菌遗传背景的模式菌株,利用real-time RT PCR对相关毒素及调控基因进行表达定量分析,在生物、医学、食品检测等领域具有较大研究价值.[方法]对制备好的反转录(RT,含有cDNA和DNA)和非反转录(RTˉ,仅含DNA)样品进行Real-time PCR检测,根据经典(1 E)ˉ△△Ct相对定量算法并结合PCR效率公式建立一种基因表达相对定量分析的DNA扣除法,将得到的Ct值转换为各样品含量,从RT样品中扣除RTˉ样品的量,无需DNaseⅠ酶解处理就可以去除DNA的影响,RTˉ样品的检测结果还可同时作为稳定的DNA内参.[结果]采用以上方法分析金黄色葡萄球菌肠毒素A基因(sea)、16S rRNA和RNA Ⅲ的表达情况,在含有葡萄糖的NB培养基中sea的相对转录水平随着葡萄糖浓度的增大而升高,RNAⅢ的相对转录水平随葡萄糖浓度的变化而产生小幅度的波动,16S rRNA在菌体生长初期时的表达量较为稳定;与绝对定量法比较,结果差异较小(均小于15%),且差异不显著(p＞0.05).[结论]这种基于DNA扣除法的Real-time RT PCR相对定量方法可以有效的对金黄色葡萄球菌的基因表达进行分析.     

Muscle-mediated gene therapy

Transfer of therapeutic genes into muscle tissue holds promise for the treatment of a variety of muscular dystrophies, serum protein deficiencies and vascular proliferative disorders. Recent progress achieved in development of improved vectors allowed prolonged and efficient expression of genes encoding therapeutic proteins in muscle cells. The most important advances include: novel plasmid DNA vectors and methods for their efficient transfection in vivo, helper-dependent adenoviral vectors, allowing long-term gene expression and effective readministration in immunocompetent hosts and adeno-associated vectors. On the other hand, recent progress in this field has been facilitated by development of systems that enable regulated therapeutic gene expression in muscle tissue.