Expression profiling of genes regulated by TGF-beta: Differential regulation in normal and tumour cells

Background

The normalization of DNA microarrays allows comparison among samples by adjusting for individual hybridization intensities. The approaches most commonly used are global normalization methods that are based on the expression of all genes on the slide and on the modulation of a small proportion of genes. Alternative approaches must be developed for microarrays where the proportion of modulated genes and their distribution are unknown and they may be biased towards up- or down-modulated trends.

Results

The aim of the work is to study the use of spike-in controls to normalize low-density microarrays. Our test-array was designed to analyze gene modulation in response to hypoxia (a condition of low oxygen tension) in a macrophage cell line. RNA was extracted from controls and cells exposed to hypoxia, mixed with spike RNA, labeled and hybridized to our test-array. We used eight bacterial RNAs as source of spikes. The test-array contained the oligonucleotides specific for 178 mouse genes and those specific for the eight spikes. We assessed the quality of the spike signals, the reproducibility of the results and, in general, the nature of the variability. The small values of the coefficients of variation revealed high reproducibility of our platform either in replicated spots or in technical replicates. We demonstrated that the spike-in system was suitable for normalizing our platform and determining the threshold for discriminating the hypoxia modulated genes. We assessed the application of the spike-in normalization method to microarrays in which the distribution of the expression values was symmetric or asymmetric. We found that this system is accurate, reproducible and comparable to other normalization methods when the distribution of the expression values is symmetric. In contrast, we found that the use of the spike-in normalization method is superior and necessary when the distribution of the gene expression is asymmetric and biased towards up-regulated genes.

Conclusion

We demonstrate that spike-in controls based normalization is a reliable and reproducible method that has the major advantage to be applicable also to biased platform where the distribution of the up- and down-regulated genes is asymmetric as it may occur in diagnostic chips.     

Normalization of microarray data using a spatial mixed model analysis which includes splines

MOTIVATION: Microarray experiments with thousands of genes on a slide and multiple slides used in any experimental set represent a large body of data with many sources of variation. The identification of such sources of variation within microarray experimental sets is critical for correct deciphering of desired gene expression differences. RESULTS: We describe new methods for the normalization using spatial mixed models which include splines and analysis of two-colour spotted microarrays for within slide variation and for a series of slides. The model typically explains 45-85% of the variation on a slide with only approximately 1% of the total degrees of freedom. The results from our methods compare favourably with those from intensity dependent normalization loess methods where we accounted for twice as much uncontrolled and unwanted variation on the slides. We have also developed an index for each EST that combines the various measures of the differential response into a single value that researchers can use to rapidly assess the genes of interest.     

SynteBase/SynteView: a tool to visualize gene order conservation in prokaryotic genomes

Background

Most microarray studies are made using labelling with one or two dyes which allows the hybridization of one or two samples on the same slide. In such experiments, the most frequently used dyes areCy3 andCy5. Recent improvements in the technology (dye-labelling, scanner and, image analysis) allow hybridization up to four samples simultaneously. The two additional dyes areAlexa488 andAlexa494. The triple-target or four-target technology is very promising, since it allows more flexibility in the design of experiments, an increase in the statistical power when comparing gene expressions induced by different conditions and a scaled down number of slides. However, there have been few methods proposed for statistical analysis of such data. Moreover the lowess correction of the global dye effect is available for only two-color experiments, and even if its application can be derived, it does not allow simultaneous correction of the raw data.

Results

We propose a two-step normalization procedure for triple-target experiments. First the dye bleeding is evaluated and corrected if necessary. Then the signal in each channel is normalized using a generalized lowess procedure to correct a global dye bias. The normalization procedure is validated using triple-self experiments and by comparing the results of triple-target and two-color experiments. Although the focus is on triple-target microarrays, the proposed method can be used to normalizepdifferently labelled targets co-hybridized on a same array, for any value ofpgreater than 2.

Conclusion

The proposed normalization procedure is effective: the technical biases are reduced, the number of false positives is under control in the analysis of differentially expressed genes, and the triple-target experiments are more powerful than the corresponding two-color experiments. There is room for improving the microarray experiments by simultaneously hybridizing more than two samples.     

芯片数据标准化方法比较研究

在基因芯片实验中,基因表达水平之间的相关性在推断基因间相互关系时起到非常重要的作用.未经标准化处理的芯片数据基因之间往往都呈现出很强的相关性,这些高相关性一部分是由基因表达水平变化引起的,而另外一部分是由系统偏差引起的.对芯片数据进行标准化处理的目的之一是消除系统偏差引起的高相关性,同时保留由真正生物学原因引起的基因表达水平高相关性.虽然目前对标准化方法已经有了不少比较研究,但还较少有人研究标准化方法对基因之间相关系数的影响,以及哪种方法最有利于恢复基因之间的相关性结构.通过对基因表达水平数据的模拟,具体比较了几种常用标准化方法的效果,从而给出最有利于恢复基因之间相关性结构的那种标准化方法.     

Normalization, testing, and false discovery rate estimation for RNA-sequencing data

We discuss the identification of genes that are associated with an outcome in RNA sequencing and other sequence-based comparative genomic experiments. RNA-sequencing data take the form of counts, so models based on the Gaussian distribution are unsuitable. Moreover, normalization is challenging because different sequencing experiments may generate quite different total numbers of reads. To overcome these difficulties, we use a log-linear model with a new approach to normalization. We derive a novel procedure to estimate the false discovery rate (FDR). Our method can be applied to data with quantitative, two-class, or multiple-class outcomes, and the computation is fast even for large data sets. We study the accuracy of our approaches for significance calculation and FDR estimation, and we demonstrate that our method has potential advantages over existing methods that are based on a Poisson or negative binomial model. In summary, this work provides a pipeline for the significance analysis of sequencing data.     

An associative analysis of gene expression array data

MOTIVATION: We face the absence of optimized standards to guide normalization, comparative analysis, and interpretation of data sets. One aspect of this is that current methods of statistical analysis do not adequately utilize the information inherent in the large data sets generated in a microarray experiment and require a tradeoff between detection sensitivity and specificity. RESULTS: We present a multistep procedure for analysis of mRNA expression data obtained from cDNA array methods. To identify and classify differentially expressed genes, results from standard paired t-test of normalized data are compared with those from a novel method, denoted an associative analysis. This method associates experimental gene expressions presented as residuals in regression analysis against control averaged expressions to a common standard-the family of similarly computed residuals for low variability genes derived from control experiments. By associating changes in expression of a given gene to a large family of equally expressed genes of the control group, this method utilizes the large data sets inherent in microarray experiments to increase both specificity and sensitivity. The overall procedure is illustrated by tabulation of genes whose expression differs significantly between Snell dwarf mice (dw/dw) and their phenotypically normal littermates (dw/+, +/+). Of the 2,352 genes examined only 450-500 were expressed above the background levels observed in nonexpressed genes and of these 120 were established as differentially expressed in dwarf mice at a significance level that excludes appearance of false positive determinations.     

Validation of alternative methods of data normalization in gene co-expression studies

MOTIVATION: Clusters of genes encoding proteins with related functions, or in the same regulatory network, often exhibit expression patterns that are correlated over a large number of conditions. Protein associations and gene regulatory networks can be modelled from expression data. We address the question of which of several normalization methods is optimal prior to computing the correlation of the expression profiles between every pair of genes. RESULTS: We use gene expression data from five experiments with a total of 78 hybridizations and 23 diverse conditions. Nine methods of data normalization are explored based on all possible combinations of normalization techniques according to between and within gene and experiment variation. We compare the resulting empirical distribution of gene x gene correlations with the expectations and apply cross-validation to test the performance of each method in predicting accurate functional annotation. We conclude that normalization methods based on mixed-model equations are optimal.     

Genomic DNA standards for gene expression profiling in Mycobacterium tuberculosis

A fundamental problem in DNA microarray analysis is the lack of a common standard to compare the expression levels of different samples. Several normalization protocols have been proposed to overcome variables inherent in this technology. As yet, there are no satisfactory methods to exchange gene expression data among different research groups or to compare gene expression values under different stimulus–response profiles. We have tested a normalization procedure based on comparing gene expression levels to the signals generated from hybridizing genomic DNA (genomic normalization). This procedure was applied to DNA microarrays of Mycobacterium tuberculosis using RNA extracted from cultures growing to the logarithmic and stationary phases. The applied normalization procedure generated reproducible measurements of expression level for 98% of the putative mycobacterial ORFs, among which 5.2% were significantly changed comparing the logarithmic to stationary growth phase. Additionally, analysis of expression levels of a subset of genes by real time PCR technology revealed an agreement in expression of 90% of the examined genes when genomic DNA normalization was applied instead of 29–68% agreement when RNA normalization was used to measure the expression levels in the same set of RNA samples. Further examination of microarray expression levels displayed clusters of genes differentially expressed between the logarithmic, early stationary and late stationary growth phases. We conclude that genomic DNA standards offer advantages over conventional RNA normalization procedures and can be adapted for the investigation of microbial genomes.     

Expressed Repeat Elements Improve RT-qPCR Normalization across a Wide Range of Zebrafish Gene Expression Studies

The selection and validation of stably expressed reference genes is a critical issue for proper RT-qPCR data normalization. In zebrafish expression studies, many commonly used reference genes are not generally applicable given their variability in expression levels under a variety of experimental conditions. Inappropriate use of these reference genes may lead to false interpretation of expression data and unreliable conclusions. In this study, we evaluated a novel normalization method in zebrafish using expressed repetitive elements (ERE) as reference targets, instead of specific protein coding mRNA targets. We assessed and compared the expression stability of a number of EREs to that of commonly used zebrafish reference genes in a diverse set of experimental conditions including a developmental time series, a set of different organs from adult fish and different treatments of zebrafish embryos including morpholino injections and administration of chemicals. Using geNorm and rank aggregation analysis we demonstrated that EREs have a higher overall expression stability compared to the commonly used reference genes. Moreover, we propose a limited set of ERE reference targets (hatn10, dna15ta1 and loopern4), that show stable expression throughout the wide range of experiments in this study, as strong candidates for inclusion as reference targets for qPCR normalization in future zebrafish expression studies. Our applied strategy to find and evaluate candidate expressed repeat elements for RT-qPCR data normalization has high potential to be used also for other species.     

A Bayesian Partition Method for Detecting Pleiotropic and Epistatic eQTL Modules

Studies of the relationship between DNA variation and gene expression variation, often referred to as “expression quantitative trait loci (eQTL) mapping”, have been conducted in many species and resulted in many significant findings. Because of the large number of genes and genetic markers in such analyses, it is extremely challenging to discover how a small number of eQTLs interact with each other to affect mRNA expression levels for a set of co-regulated genes. We present a Bayesian method to facilitate the task, in which co-expressed genes mapped to a common set of markers are treated as a module characterized by latent indicator variables. A Markov chain Monte Carlo algorithm is designed to search simultaneously for the module genes and their linked markers. We show by simulations that this method is more powerful for detecting true eQTLs and their target genes than traditional QTL mapping methods. We applied the procedure to a data set consisting of gene expression and genotypes for 112 segregants of S. cerevisiae. Our method identified modules containing genes mapped to previously reported eQTL hot spots, and dissected these large eQTL hot spots into several modules corresponding to possibly different biological functions or primary and secondary responses to regulatory perturbations. In addition, we identified nine modules associated with pairs of eQTLs, of which two have been previously reported. We demonstrated that one of the novel modules containing many daughter-cell expressed genes is regulated by AMN1 and BPH1. In conclusion, the Bayesian partition method which simultaneously considers all traits and all markers is more powerful for detecting both pleiotropic and epistatic effects based on both simulated and empirical data.     

A normalization strategy for comparing tag count data

Background

High-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data.

Results

We describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset.

Conclusion

Our results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.     

Evaluation of Reference Genes for RT-qPCR Expression Studies in Hop (Humulus lupulus L.) during Infection with Vascular Pathogen Verticillium albo-atrum

Hop plant (Humulus lupulus L.), cultivated primarily for its use in the brewing industry, is faced with a variety of diseases, including severe vascular diseases, such as Verticillium wilt, against which no effective protection is available. The understanding of disease resistance with tools such as differentially expressed gene studies is an important objective of plant defense mechanisms. In this study, we evaluated twenty-three reference genes for RT-qPCR expression studies on hop under biotic stress conditions. The candidate genes were validated on susceptible and resistant hop cultivars sampled at three different time points after infection with Verticillium albo-atrum. The stability of expression and the number of genes required for accurate normalization were assessed by three different Excel-based approaches (geNorm v.3.5 software, NormFinder, and RefFinder). High consistency was found among them, identifying the same six best reference genes (YLS8, DRH1, TIP41, CAC, POAC and SAND) and five least stably expressed genes (CYCL, UBQ11, POACT, GAPDH and NADH). The candidate genes in different experimental subsets/conditions resulted in different rankings. A combination of the two best reference genes, YLS8 and DRH1, was used for normalization of RT-qPCR data of the gene of interest (PR-1) implicated in biotic stress of hop. We outlined the differences between normalized and non-normalized values and the importance of RT-qPCR data normalization. The high correlation obtained among data standardized with different sets of reference genes confirms the suitability of the reference genes selected for normalization. Lower correlations between normalized and non-normalized data may reflect different quantity and/or quality of RNA samples used in RT-qPCR analyses.     

A robust neural networks approach for spatial and intensity-dependent normalization of cDNA microarray data

MOTIVATION: Microarray experiments are affected by numerous sources of non-biological variation that contribute systematic bias to the resulting data. In a dual-label (two-color) cDNA or long-oligonucleotide microarray, these systematic biases are often manifested as an imbalance of measured fluorescent intensities corresponding to Sample A versus those corresponding to Sample B. Systematic biases also affect between-slide comparisons. Making effective corrections for these systematic biases is a requisite for detecting the underlying biological variation between samples. Effective data normalization is therefore an essential step in the confident identification of biologically relevant differences in gene expression profiles. Several normalization methods for the correction of systemic bias have been described. While many of these methods have addressed intensity-dependent bias, few have addressed both intensity-dependent and spatiality-dependent bias. RESULTS: We present a neural network-based normalization method for correcting the intensity- and spatiality-dependent bias in cDNA microarray datasets. In this normalization method, the dependence of the log-intensity ratio (M) on the average log-intensity (A) as well as on the spatial coordinates (X,Y) of spots is approximated with a feed-forward neural network function. Resistance to outliers is provided by assigning weights to each spot based on how distant their M values is from the median over the spots whose A values are similar, as well as by using pseudospatial coordinates instead of spot row and column indices. A comparison of the robust neural network method with other published methods demonstrates its potential in reducing both intensity-dependent bias and spatial-dependent bias, which translates to more reliable identification of truly regulated genes.