Assessing probe-specific dye and slide biases in two-color microarray data

A primary reason for using two-color microarrays is that the use of two samples labeled with different dyes on the same slide, that bind to probes on the same spot, is supposed to adjust for many factors that introduce noise and errors into the analysis. Most users assume that any differences between the dyes can be adjusted out by standard methods of normalization, so that measures such as log ratios on the same slide are reliable measures of comparative expression. However, even after the normalization, there are still probe specific dye and slide variation among the data. We define a method to quantify the amount of the dye-by-probe and slide-by-probe interaction. This serves as a diagnostic, both visual and numeric, of the existence of probe-specific dye bias. We show how this improved the performance of two-color array analysis for arrays for genomic analysis of biological samples ranging from rice to human tissue.We develop a procedure for quantifying the extent of probe-specific dye and slide bias in two-color microarrays. The primary output is a graphical diagnostic of the extent of the bias which called ECDF (Empirical Cumulative Distribution Function), though numerical results are also obtained.We show that the dye and slide biases were high for human and rice genomic arrays in two gene expression facilities, even after the standard intensity-based normalization, and describe how this diagnostic allowed the problems causing the probe-specific bias to be addressed, and resulted in important improvements in performance. The R package LMGene which contains the method described in this paper has been available to download from Bioconductor

What Is the Best Reference RNA? And Other Questions Regarding the Design and Analysis of Two-Color Microarray Experiments

The reference design is a practical and popular choice for microarray studies using two-color platforms. In the reference design, the reference RNA uses half of all array resources, leading investigators to ask: What is the best reference RNA? We propose a novel method for evaluating reference RNAs and present the results of an experiment that was specially designed to evaluate three common choices of reference RNA. We found no compelling evidence in favor of any particular reference. In particular, a commercial reference showed no advantage in our data. Our experimental design also enabled a new way to test the effectiveness of pre-processing methods for two-color arrays. Our results favor using an intensity-normalization and foregoing background-subtraction. Finally, we evaluate the sensitivity and specificity of data quality filters, and propose a new filter that can be applied to any experimental design and does not rely on replicate hybridizations

Designing Toxicogenomics Studies that use DNA Array Technology

Background: Bioassays are routinely used to evaluate the toxicity of test agents. Experimental designs for bioassays are largely encompassed by fixed effects linear models. In toxicogenomics studies where DNA arrays measure mRNA levels, the tissue samples are typically generated in a bioassay. These measurements introduce additional sources of variation, which must be properly managed to obtain valid tests of treatment effects.Results: An analysis of covariance model is developed which combines a fixed-effects linear model for the bioassay with important variance components associated with DNA array measurements. These models can accommodate the dominant characteristics of measurements from DNA arrays, and they account for technical variation associated with normalization, spots, dyes, and batches as well as the biological variation associated with the bioassay. An example illustrates how the model is used to identify valid designs and to compare competing designs.Conclusions: Many toxicogenomics studies are bioassays which measure gene expression using DNA arrays. These studies can be designed and analyzed using standard methods with a few modifications to account for characteristics of array measurements, such as multiple endpoints and normalization. As much as possible, technical variation associated with probes, dyes, and batches are managed by blocking treatments within these sources of variation. An example shows how some practical constraints can be accommodated by this modelling and how it allows one to objectively compare competing designs

SIMAGE: SImulation of DNA-MicroArray Gene Expression data

Background: Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-processing and processing methods for best analysis of a given DNA microarray experiment, (iii) educating students, lab-workers and other researchers by making them aware of the many factors influencing DNA microarray experiments. Results: Our model has multiple layers of factors influencing the experiment. The relative influence of such factors can differ significantly between labs, experiments within labs, etc. Therefore, we have added a module to roughly estimate their parameters from a given data set. This guarantees that our simulated data mimics real data as closely as possible. Conclusions: We introduce a model for the simulation of dual-dye cDNA-microarray data closely resembling real data and coin the model and its software implementation SIMAGE which stands for simulation of microarray gene expression data. The software is freely accessible at: http://bioinformatics.biol.rug.nl/websoftware/simag

Microarray Data Preprocessing: From Experimental Design to Differential Analysis

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.Peer reviewe

The application of gene expression profiling in the characterization of physiological effects of genetically modified feed components in rats

The study was conducted to evaluate the adequacy of expression profiling for the characterization of potential physiological side effects of genetically modified (GM) feed components. Feeding experiments with rats fed either GM or non-GM feed components were conducted and a comparative expression profiling, using DNA-chip-technology, was done. As a prerequisite for these expression studies data analysis parameters were optimized. Diet-associated expression differences between the two feeding groups were observed in spleen, small intestine and liver. It was shown that expression profiling provides great sensitivity in monitoring physiological reactions of an organism to such diets.In der vorliegenden Arbeit wurde die Eignung von "Genexpressionsprofiling" zur Charakterisierung physiologischer Nebeneffekte genetisch veränderter (GV) Futtermittel untersucht. In Fütterungsversuchen wurden Ratten entweder GV- oder Nicht-GV-Futterkomponenten verabreicht und ihre Expressionsprofile mittels DNA-chip-Technologie verglichen. Als Vorraussetzung für diese Expressionsanalysen erfolgte vorab eine Optimierung der Datenauswertung. In Milz, Leber und Dünndarm ergaben sich Diät assoziierte Expressionsunterschiede zwischen den Fütterungsgruppen. Insgesamt zeigte sich, dass „Genexpressionsprofiling“ eine hohe Sensitivität zur Erfassung physiologischer Reaktionen eines Organismus auf solche Diäten ermöglicht

Genetic algorithm-neural network: feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data

Microarray analysis of GFP-expressing mouse Dopamine neurons isolated by laser capture microdissection

The Central Nervous System (CNS) contains an enormous variety of cell types which organize in complex networks. The lack of adequate markers to discern unequivocally among this cellular heterogeneity make the task of dissecting out such neural networks and the cells that comprise them very challenging. The present study represents a \u201cbottom-up\u201d approach that entails a description of A9 and A10 nuclei, which are components of the mesencephalic dopaminergic system, and the identification of their molecular make-up through microarray analysis of their gene expression profiles. These mesencephalic dopaminergic nuclei give rise to the mesocortical and mesostriatal projections and are well known for their roles in initiation of movement, reward behaviour and neurobiology of addiction. Moreover, in post mortem brains of Parkinson Disease patients a specific topographic pattern of degeneration of these neurons, also recapitulated in experimental animal models, is noted, with A9 neurons presenting with a higher vulnerability to degeneration with respect to A10 cells among which, neuron loss is almost negligible. Molecular differences may be at the basis of this different susceptibility. In this study we have optimized a protocol for laser-assisted microdissection of fluorescent-expressing cells and have taken advantage of a line of transgenic mice TH-GFP/21-31, which express GFP under the TH promoter in all CA cells, to guide laser capture microdissection of A9 and A10 mDA neurons for differential informative cDNA microarray profiling. Results show that our optimized method retains the GFP-fluorescence of DA cells and achieves good tissue morphology visualization. Moreover, RNA of high quality and good reproducibility of hybridizations support the validity of the protocol. Many of the genes that resulted differentially expressed from this analysis were found to be genes previously known to specifically define the different identities of the two DA neuronal nuclei. Transcripts were verified for expression, in DA neurons, using the collection of in situ hybridization in the Allen Brain Atlas. We have identified 592 differentially expressed transcripts (less than 8%) of which 242 showing higher expression in A9 and 350 showing higher expression in A10. Categorical analysis showed that transcripts associated with mitochondria and energy production were enriched in A9, while transcripts involved in redox homeostasis and stress response resulted enriched in A10. Of all the differentially expressed genes, eight transcripts (Mif, Hnt, Ndufa10, Aurka, Cs, enriched in A9 neurons and Pdia5, Whrn, and Gpx3 enriched in A10 neurons), verified with the Allen Brain Atlas and not noted or confirmed as differentially expressed before, emerged from this analysis. These and other selected genes are discussed

Can subtle changes in gene expression be consistently detected with different microarray platforms?

Background: The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. Results: Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms. Conclusion: The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression

Dexamethasone- and age-sensitive genes in neonatal small intestine

Regulation of GLUT5 is dependent on the presence of its substrate, fructose, but it is also correlated with the developmental (aging) process of the intestine. To identify fructose-responsive genes whose expression also changes with age, intestines of 10 and 20 d old pups were perfused, by Ferraris et al., with fructose and then compared by microarray analysis. From this, a gene clustering analysis revealed that some age- and fructose-specific genes are regulated by corticosterones, which normally increase in pups ≥ 14 d old. Subsequent work indicated that priming the gut with Dexamethasone (Dex, a glucocorticoid analog) allowed fructose to precociously stimulate GLUT5 even in suckling pups \u3c 14 d old. This suggests that the effect of Dex on GLUT5 is similar to the effect of age; both allow fructose to stimulate GLUT5. It is not known: (1) if this similar effect of Dex and age is specific to GLUT5 or if it could be extended to a larger family of genes, and (2) is Dex and age are acting through the same or different signaling mechanisms. In this study I tested the hypothesis that Dex allows fructose to stimulate GLUT5 by inducing the same genes as those induced by age. I therefore, determined by microarray analysis in 10 old suckling pups, the identity of genes that are regulated solely by fructose, solely by Dex and by Dex under fructose conditions. Genes regulated by Dex under fructose conditions were compared to genes regulated by age in the presence of fructose, which were identified in a previous microarray experiment by Douard et al. Microarray results revealed 29 genes up-regulated by Dex, 14 by fructose, and 10 by Dex under fructose conditions. There were 64 genes down-regulated by fructose, 18 by Dex and 2 by Dex under Fructose conditions. Of these 12 Dex- under fructose conditions- sensitive genes there were no genes that were also age and fructose sensitive. There were however, four genes that were regulated by both age and Dex, independently of fructose. Hence, while there are common regulatory factors between age and Dex, the different populations of Dex- and age- sensitive intermediates, under fructose influence, suggest that there are alternative signaling pathways leading to the same outcome: an earlier onset of GLUT5 in the brushborder membrane of the small intestine