Single-channel Microarray Research Articles

Identification of biomarker genes for resistance to a pathogen by a novel method for meta-analysis of single-channel microarray datasets.

The search for fast and reliable methods allowing for extraction of biomarker genes, e.g. responsible for a plant resistance to a certain pathogen, is one of the most important and highly exploited data mining problem in bioinformatics. Here we describe a simple and efficient method suitable for combining results from multiple single-channel microarray experiments for meta-analysis. A new technique presented here makes use of the fuzzy set logic for the initial gene selection and of the machine learning algorithm AdaBoost to retrieve a set of genes where expression profiles are the most different between the resistant and susceptible classes. As a proof of concept, our method has been applied to the analysis of a gene expression dataset composed of many independent microarray experiments on wheat head tissue, to identify genes that are biomarkers of resistance to the fungus Fusarium graminearum. We used microarray data from many experiments performed on wheat lines of various resistance level. The resulting set of genes was validated by qPCR experiments.

PTH-100 A comparative gene expression study between individuals with apparent resistance, spontaneous clearance, or chronic infection from hcv

<h3>Introduction</h3> We have identified a group of high risk injection drug users who share needles, syringes and other injecting paraphernalia over a long period of time yet who remain uninfected by hepatitis C virus (HCV). These individuals appear to be resistant to HCV infection and we have termed them exposed uninfected (EU). Defining factors that may prevent HCV infection in these cohorts could give valuable insight into the mechanisms of natural resistance to HCV. <h3>Method</h3> Individuals at high risk of recurrent exposure to HCV infection from long term IDU were recruited in Plymouth and if HCV antibody and HCV RNA negative were termed exposed uninfected (EU). We have compared the transcriptional profiles of intravenous drug users (IDU) with 3 different outcomes following HCV exposure – chronic hepatitis C infection (CHCV; n = 6), spontaneously resolution of HCV (SR; HCV Ab positive but HCV RNA negative, n = 6) and exposed but uninfected (EU; n = 6). Agilent single channel microarray was performed on RNA isolated from peripheral blood mononuclear cells and the results were analysed on Ingenuity Pathway Analysis software (Qiagen). The identified targets were subsequently validated at a protein level. <h3>Results</h3> 1465 genes between the EU and CHCV group, 4377 between the CHCV and SR group and 4510 between the SR and EU group were significantly up or down regulated. Of the differentially regulated genes, the association with resistance to HCV was strongest for IL27 and CXCL7 which were significantly upregulated in EU (IL27 log fold change EU vs SR: 1.943, p = 1.72E-04 and CXCL7 log fold change EU vs CHCV 2.359, p = 6.04E-03) relative to the other 2 groups. At a protein level, the median IL27 serum level in the EU group (n = 13), detected by sandwich ELISA, was significantly higher (p = 0.008) compared to CHCV (n = 14), healthy control (HC; n = 8) and SR (n = 14) groups. The CD28 mediated T-helper cell signalling pathway was significantly upregulated in SR (ratio 0.348, p = 2.77E-05) relative to the 2 other groups. <h3>Conclusion</h3> Significant differences in gene expression were found in injection drug users with different outcomes from exposure to HCV infection. EU and SR exhibit differences related to innate and adaptive immunity compared to CHCV that likely contribute to natural resistance and spontaneous clearance of HCV. <h3>Disclosure of interest</h3> None Declared.

A centrosome clustering protein, HSET, as a potential biomarker for ovarian adenocarcinomas.

e16562 Background: Beyond their role as beacons of malignancy, supernumerary centrosomes drive aggressive disease course. Paradoxically, amplified centrosomes might compromise cell viability by consigning mitotic cells to a multipolar configuration resulting in high-grade death-inducing aneuploidy. Nonetheless, cancer cells employ clever tricks to suppress multipolarity by huddling their extra centrosomes by deploying clustering mechanisms to maintain pseudobipolarity associated with low-grade aneuploidy, an edge to their survival. HSET, a nonessential minus end-directed motor of the kinesin-14 family, is a centrosome clustering molecule essential for viability of extra centrosome-bearing cancer cells. Given that ovarian cancers robustly display amplified centrosomes in this study we examined the selective overexpression of HSET in human ovarian tumor. Methods: We interrogated single-channel microarray data from publically-available Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases for HSET expression levels in serous ovarian adenocarcinoma (n = 1090) and normal ovarian (n = 38) tissue. To verify in silico results, we immunostained tissue microarrays with ovarian adenocarcinomas (n = 129) and normal ovarian (n = 13) samples, using an anti-HSET antibody. Results: Our in silico data indicated higher average (7.72 vs 5.86) HSET expression in serous ovarian carcinoma (n = 1090) compared to normal ovarian (n = 38) tissue (p < 0.05). Also, HSET expression increased with tumor grade (Grade 1-6.53, Grade 2-7.85, Grade 3-8.25) (p < 0.001). Immunohistochemical data corroborated with our in silico findings; ovarian adenocarcinomas exhibited (i) significantly higher (p < 0.005) nuclear HSET expression in comparison to the normal tissues, and (ii) a grade-wise increase in HSET expression (p = 0.0183). Conclusions: An increase in HSET levels was associated with increasing grade suggesting that an aggressive disease course in ovarian adenocarcinoma patients can be attributed to high levels of this centrosomal clustering molecule, thus, emphasizing the usefulness of HSET as a prognostic biomarker in ovarian cancers.

RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.

With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter’s disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.

A single-sample microarray normalization method to facilitate personalized-medicine workflows

Gene-expression microarrays allow researchers to characterize biological phenomena in a high-throughput fashion but are subject to technological biases and inevitable variabilities that arise during sample collection and processing. Normalization techniques aim to correct such biases. Most existing methods require multiple samples to be processed in aggregate; consequently, each sample's output is influenced by other samples processed jointly. However, in personalized-medicine workflows, samples may arrive serially, so renormalizing all samples upon each new arrival would be impractical. We have developed Single Channel Array Normalization (SCAN), a single-sample technique that models the effects of probe-nucleotide composition on fluorescence intensity and corrects for such effects, dramatically increasing the signal-to-noise ratio within individual samples while decreasing variation across samples. In various benchmark comparisons, we show that SCAN performs as well as or better than competing methods yet has no dependence on external reference samples and can be applied to any single-channel microarray platform.

High-throughput processing and normalization of one-color microarrays for transcriptional meta-analyses

BackgroundMicroarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e.g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, directly comparable format will enable easier and more reliable access to microarray data to conduct meta-analyses.MethodsWe present a straightforward, simple but robust against potential outliers method for automatic quality control and pre-processing of tens of thousands of single-channel microarray data files. GEO GDS files are quality checked by comparing parametric distributions and quantile normalized to enable direct comparison of expression level for subsequent meta-analyses.Results13,000 human 1-color experiments were processed to create a single gene expression matrix that subsets can be extracted from to conduct meta-analyses. Interestingly, we found that when conducting a global meta-analysis of gene-gene co-expression patterns across all 13,000 experiments to predict gene function, normalization had minimal improvement over using the raw data.ConclusionsNormalization of microarray data appears to be of minimal importance on analyses based on co-expression patterns when the sample size is on the order of thousands microarray datasets. Smaller subsets, however, are more prone to aberrations and artefacts, and effective means of automating normalization procedures not only empowers meta-analytic approaches, but aids in reproducibility by providing a standard way of approaching the problem.Data availability: matrix containing normalized expression of 20,813 genes across 13,000 experiments is available for download at . Source code for GDS files pre-processing is available from the authors upon request.

Gene Network Landscape of the Ciliate Tetrahymena thermophila

BackgroundGenome-wide expression data of gene microarrays can be used to infer gene networks. At a cellular level, a gene network provides a picture of the modules in which genes are densely connected, and of the hub genes, which are highly connected with other genes. A gene network is useful to identify the genes involved in the same pathway, in a protein complex or that are co-regulated. In this study, we used different methods to find gene networks in the ciliate Tetrahymena thermophila, and describe some important properties of this network, such as modules and hubs.Methodology/Principal FindingsUsing 67 single channel microarrays, we constructed the Tetrahymena gene network (TGN) using three methods: the Pearson correlation coefficient (PCC), the Spearman correlation coefficient (SCC) and the context likelihood of relatedness (CLR) algorithm. The accuracy and coverage of the three networks were evaluated using four conserved protein complexes in yeast. The CLR network with a Z-score threshold 3.49 was determined to be the most robust. The TGN was partitioned, and 55 modules were found. In addition, analysis of the arbitrarily determined 1200 hubs showed that these hubs could be sorted into six groups according to their expression profiles. We also investigated human disease orthologs in Tetrahymena that are missing in yeast and provide evidence indicating that some of these are involved in the same process in Tetrahymena as in human.Conclusions/SignificanceThis study constructed a Tetrahymena gene network, provided new insights to the properties of this biological network, and presents an important resource to study Tetrahymena genes at the pathway level.

Abstract 1142: A specific miRNA signature characterizes metastasis in renal cell carcinoma

Abstract Background: MicroRNAs (miRNAs) as small non-coding RNAs are regulators of gene expression in tumor development and progression. However, its specific role in metastasis of clear cell renal cell carcinoma (ccRCC) is almost unknown. Therefore, we aimed in defining specific miRNA expression pattern of metastatic ccRCC and identifying cellular functions of specific miRNAs deregulated in metastatic process. Methods: We isolated total RNA of 29 ccRCC samples (including 13 non-metastatic and 16 metastatic tumors) and from 16 metastases using snap frozen tissue. Single channel microarray analysis was performed for a global miRNA expression profiling in 22 ccRCC samples. The validation of the differentially expressed miRNAs in metastatic ccRCC compared to non-metastatic tumor samples and furthermore in metastases of ccRCC was performed by using qRT-PCR. Results: We identified a miRNA expression pattern that distinguishes between metastatic and non-metastatic ccRCC. Furthermore, we found a group of miRNAs with altered expression in early-metastatic compared to late metastatic and non-metastatic tumors. These microarray results were verified by qRT-PCR. By analyzing the expression of four of these differentially expressed miRNAs (26a, 30c, 126, 451) in metastases tissue by qRT-PCR we detected the same expression behavior as in the primary metastatic tumor counterparts. Conclusion: Our findings indicate that miRNAs are involved in the metastatic process. Thus, we were able to identify a specific miRNA expression pattern in metastatic ccRCC. Metastases of ccRCC are characterized by the same expression value of specific miRNAs as the primary metastatic tumor tissue. These investigations showed that specific miRNAs are possible prognostic markers for metastatic disease in RCC patients. Further studies have to define the functional role of the miRNAs in the metastatic process. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1142. doi:10.1158/1538-7445.AM2011-1142

ANAIS: Analysis of NimbleGen Arrays Interface

ANAIS is a user-friendly web-based tool for the processing of NimbleGen expression data. The interface reads single-channel microarray files generated by NimbleGen platforms and produces easily interpretable graphical and numerical results. It provides biologists six turnkey analysis modules-normalization, probe to gene, quality controls, differential expression, detection, queries and clustering-to explore quickly, freely and without the need for computer programming, NimbleGen transcriptome data. http://anais.versailles.inra.fr.

Construction and use of gene expression covariation matrix

BackgroundOne essential step in the massive analysis of transcriptomic profiles is the calculation of the correlation coefficient, a value used to select pairs of genes with similar or inverse transcriptional profiles across a large fraction of the biological conditions examined. Until now, the choice between the two available methods for calculating the coefficient has been dictated mainly by technological considerations. Specifically, in analyses based on double-channel techniques, researchers have been required to use covariation correlation, i.e. the correlation between gene expression changes measured between several pairs of biological conditions, expressed for example as fold-change. In contrast, in analyses of single-channel techniques scientists have been restricted to the use of coexpression correlation, i.e. correlation between gene expression levels. To our knowledge, nobody has ever examined the possible benefits of using covariation instead of coexpression in massive analyses of single channel microarray results.ResultsWe describe here how single-channel techniques can be treated like double-channel techniques and used to generate both gene expression changes and covariation measures. We also present a new method that allows the calculation of both positive and negative correlation coefficients between genes. First, we perform systematic comparisons between two given biological conditions and classify, for each comparison, genes as increased (I), decreased (D), or not changed (N). As a result, the original series of n gene expression level measures assigned to each gene is replaced by an ordered string of n(n-1)/2 symbols, e.g. IDDNNIDID....DNNNNNNID, with the length of the string corresponding to the number of comparisons. In a second step, positive and negative covariation matrices (CVM) are constructed by calculating statistically significant positive or negative correlation scores for any pair of genes by comparing their strings of symbols.ConclusionThis new method, applied to four different large data sets, has allowed us to construct distinct covariation matrices with similar properties. We have also developed a technique to translate these covariation networks into graphical 3D representations and found that the local assignation of the probe sets was conserved across the four chip set models used which encompass three different species (humans, mice, and rats). The application of adapted clustering methods succeeded in delineating six conserved functional regions that we characterized using Gene Ontology information.

OneChannelGUI: a graphical interface to Bioconductor tools, designed for life scientists who are not familiar with R language

OneChannelGUI is an add-on Bioconductor package providing a new set of functions extending the capability of the affylmGUI package. This library provides a graphical interface (GUI) for Bioconductor libraries to be used for quality control, normalization, filtering, statistical validation and data mining for single channel microarrays. Affymetrix 3' expression (IVT) arrays as well as the new whole transcript expression arrays, i.e. gene/exon 1.0 ST, are actually implemented. oneChannelGUI is available for most platforms on which R runs, i.e. Windows and Unix-like machines. http://www.bioconductor.org/packages/2.0/bioc/html/oneChannelGUI.html

Post hoc pattern matching: assigning significance to statistically defined expression patterns in single channel microarray data

BackgroundResearchers using RNA expression microarrays in experimental designs with more than two treatment groups often identify statistically significant genes with ANOVA approaches. However, the ANOVA test does not discriminate which of the multiple treatment groups differ from one another. Thus, post hoc tests, such as linear contrasts, template correlations, and pairwise comparisons are used. Linear contrasts and template correlations work extremely well, especially when the researcher has a priori information pointing to a particular pattern/template among the different treatment groups. Further, all pairwise comparisons can be used to identify particular, treatment group-dependent patterns of gene expression. However, these approaches are biased by the researcher's assumptions, and some treatment-based patterns may fail to be detected using these approaches. Finally, different patterns may have different probabilities of occurring by chance, importantly influencing researchers' conclusions about a pattern and its constituent genes.ResultsWe developed a four step, post hoc pattern matching (PPM) algorithm to automate single channel gene expression pattern identification/significance. First, 1-Way Analysis of Variance (ANOVA), coupled with post hoc 'all pairwise' comparisons are calculated for all genes. Second, for each ANOVA-significant gene, all pairwise contrast results are encoded to create unique pattern ID numbers. The # genes found in each pattern in the data is identified as that pattern's 'actual' frequency. Third, using Monte Carlo simulations, those patterns' frequencies are estimated in random data ('random' gene pattern frequency). Fourth, a Z-score for overrepresentation of the pattern is calculated ('actual' against 'random' gene pattern frequencies). We wrote a Visual Basic program (StatiGen) that automates PPM procedure, constructs an Excel workbook with standardized graphs of overrepresented patterns, and lists of the genes comprising each pattern. The visual basic code, installation files for StatiGen, and sample data are available as supplementary material.ConclusionThe PPM procedure is designed to augment current microarray analysis procedures by allowing researchers to incorporate all of the information from post hoc tests to establish unique, overarching gene expression patterns in which there is no overlap in gene membership. In our hands, PPM works well for studies using from three to six treatment groups in which the researcher is interested in treatment-related patterns of gene expression. Hardware/software limitations and extreme number of theoretical expression patterns limit utility for larger numbers of treatment groups. Applied to a published microarray experiment, the StatiGen program successfully flagged patterns that had been manually assigned in prior work, and further identified other gene expression patterns that may be of interest. Thus, over a moderate range of treatment groups, PPM appears to work well. It allows researchers to assign statistical probabilities to patterns of gene expression that fit a priori expectations/hypotheses, it preserves the data's ability to show the researcher interesting, yet unanticipated gene expression patterns, and assigns the majority of ANOVA-significant genes to non-overlapping patterns.

Simulation of microarray data with realistic characteristics.

BackgroundMicroarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerous different algorithms have been proposed for analyzing the data. However, an objective evaluation of the proposed algorithms is not possible due to the lack of biological ground truth information. To overcome this fundamental problem, the use of simulated microarray data for algorithm validation has been proposed.ResultsWe present a microarray simulation model which can be used to validate different kinds of data analysis algorithms. The proposed model is unique in the sense that it includes all the steps that affect the quality of real microarray data. These steps include the simulation of biological ground truth data, applying biological and measurement technology specific error models, and finally simulating the microarray slide manufacturing and hybridization. After all these steps are taken into account, the simulated data has realistic biological and statistical characteristics. The applicability of the proposed model is demonstrated by several examples.ConclusionThe proposed microarray simulation model is modular and can be used in different kinds of applications. It includes several error models that have been proposed earlier and it can be used with different types of input data. The model can be used to simulate both spotted two-channel and oligonucleotide based single-channel microarrays. All this makes the model a valuable tool for example in validation of data analysis algorithms.

Effects on gene expression and viral load of a medicinal extract from Agaricus blazei in patients with chronic hepatitis C infection

Extracts from the mushroom Agaricus blazei Murill (AbM) are used extensively as a non-prescription remedy against cancer and infections, including hepatitis. We previously demonstrated a potent immunomodulating effect of a particular preparation on monocytes in vitro, and a protective effect on bacterial infections in mice. Here we report the effect on gene expression in peripheral blood cells from four chronic hepatitis C patients, using global (29 k) oligo-based, single channel microarrays. The viral load was slightly, but not significantly, decreased after 1 week of AbM treatment. The cytokine genes most strongly induced in vitro were not induced in vivo. The more notable changes in mRNA levels were related to genes involved in the G-protein coupled receptor signalling pathway, in cell cycling, and in transcriptional regulation. The results suggest that the β-glucans of the extract, which presumably are responsible for cytokine induction, did not readily enter the blood, while other components, such as substances proposed to have anticancer effects, were active in the blood.

AffylmGUI: a graphical user interface for linear modeling of single channel microarray data

affylmGUI is a graphical user interface (GUI) to an integrated workflow for Affymetrix microarray data. The user is able to proceed from raw data (CEL files) to QC and pre-processing, and eventually to analysis of differential expression using linear models with empirical Bayes smoothing. Output of the analysis (tables and figures) can be exported to an HTML report. The GUI provides user-friendly access to state-of-the-art methods embodied in the Bioconductor software repository. affylmGUI is an R package freely available from http://www.bioconductor.org. It requires R version 1.9.0 or later and tcl/tk 8.3 or later and has been successfully tested on Windows 2000, Windows XP, Linux (RedHat and Fedora distributions) and Mac OS/X with X11. Further documentation is available at http://bioinf.wehi.edu.au/affylmGUI CONTACT: keith@wehi.edu.au.

Can Zipf's law be adapted to normalize microarrays?

BackgroundNormalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for varying organisms and tissues have shown that the majority of expressed genes exhibit a power-law distribution with an exponent close to -1 (i.e. obey Zipf's law). Based on the observation that our single channel and two channel microarray data sets also followed a power-law distribution, we were motivated to develop a normalization method based on this law, and examine how it compares with existing published techniques. A computationally simple and intuitively appealing technique based on this observation is presented.ResultsUsing pairwise comparisons using MA plots (log ratio vs. log intensity), we compared this novel method to previously published normalization techniques, namely global normalization to the mean, the quantile method, and a variation on the loess normalization method designed specifically for boutique microarrays. Results indicated that, for single channel microarrays, the quantile method was superior with regard to eliminating intensity-dependent effects (banana curves), but Zipf's law normalization does minimize this effect by rotating the data distribution such that the maximal number of data points lie on the zero of the log ratio axis. For two channel boutique microarrays, the Zipf's law normalizations performed as well as, or better than existing techniques.ConclusionZipf's law normalization is a useful tool where the Quantile method cannot be applied, as is the case with microarrays containing functionally specific gene sets (boutique arrays).