Yee Hwa Yang

Design issues for cDNA microarray experiments

Microarray experiments are used to quantify and compare gene expression on a large scale. As with all large-scale experiments, they can be costly in terms of equipment, consumables and time. Therefore, careful design is particularly important if the resulting experiment is to be maximally informative, given the effort and the resources. What then are the issues that need to be addressed when planning microarray experiments? Which features of an experiment have the most impact on the accuracy and precision of the resulting measurements? How do we balance the different components of experimental design to reach a decision? For example, should we replicate, and if so, how?

Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids

Airway inflammation and epithelial remodeling are two key features of asthma. IL-13 and other cytokines produced during T helper type 2 cell-driven allergic inflammation contribute to airway epithelial goblet cell metaplasia and may alter epithelial–mesenchymal signaling, leading to increased subepithelial fibrosis or hyperplasia of smooth muscle. The beneficial effects of corticosteroids in asthma could relate to their ability to directly or indirectly decrease epithelial cell activation by inflammatory cells and cytokines. To identify markers of epithelial cell dysfunction and the effects of corticosteroids on epithelial cells in asthma, we studied airway epithelial cells collected from asthmatic subjects enrolled in a randomized controlled trial of inhaled corticosteroids, from healthy subjects and from smokers (disease control). By using gene expression microarrays, we found that chloride channel, calcium-activated, family member 1 (CLCA1), periostin, and serine peptidase inhibitor, clade B (ovalbumin), member 2 (serpinB2) were up-regulated in asthma but not in smokers. Corticosteroid treatment down-regulated expression of these three genes and markedly up-regulated expression of FK506-binding protein 51 (FKBP51). Whereas high baseline expression of CLCA1, periostin, and serpinB2 was associated with a good clinical response to corticosteroids, high expression of FKBP51 was associated with a poor response. By using airway epithelial cells in culture, we found that IL-13 increased expression of CLCA1, periostin, and serpinB2, an effect that was suppressed by corticosteroids. Corticosteroids also induced expression of FKBP51. Taken together, our findings show that airway epithelial cells in asthma have a distinct activation profile and identify direct and cell-autonomous effects of corticosteroid treatment on airway epithelial cells that relate to treatment responses and can now be the focus of specific mechanistic studies.

Comparison of Methods for Image Analysis on cDNA Microarray Data

Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels for thousands of genes simultaneously. Image analysis is an important aspect of microarray experiments, one that can have a potentially large impact on subsequent analyses such as clustering or the identification of differentially expressed genes. This article reviews a number of existing image analysis approaches for cDNA microarray experiments and proposes new addressing, segmentation, and background correction methods for extracting information from microarray scanned images. The segmentation component uses a seeded region growing algorithm which makes provision for spots of different shapes and sizes. The background estimation approach is based on an image analysis technique known as morphological opening. These new image analysis procedures are implemented in a software package named Spot, built on the R environment for statistical computing. The statistical properties of the different segmentation and background adjustment methods are examined using microarray data from a study of lipid metabolism in mice. It is shown that in some cases background adjustment can substantially reduce the precision—that is, increase the variability—of low-intensity spot values. In contrast, the choice of segmentation procedure has a smaller impact. The comparison further suggests that seeded region growing segmentation with morphological background correction provides precise and accurate estimates of foreground and background intensities.

Statistical issues in cDNA microarray data analysis.

Statistical considerations are frequently to the fore in the analysis of microarray data, as researchers sift through massive amounts of data and adjust for various sources of variability in order to identify the important genes amongst the many which are measured. This article summarizes some of the issues involved and provides a brief review of the analysis tools which are available to researchers to deal with them. Any microarray experiment involves a number of distinct stages. Firstly there is the design of the experiment. The researchers must decide which genes are to be printed on the arrays, which sources of RNA are to be hybridized to the arrays and on how many arrays the hybridizations will be replicated. Secondly, after hybridization, there follows a number of data-cleaning steps or `low-level analysis’ of the microarray data. The microarray images must be processed to acquire red and green foreground and background intensities for each spot. The acquired red/green ratios must be normalized to adjust for dye-bias and for any systematic variation other than that due to the differences between the RNA samples being studied. Thirdly, the normalized ratios are analyzed by various graphical and numerical means to select differentially expressed (DE) genes or to find groups of genes whose expression profiles can reliably classify the different RNA sources into meaningful groups. The sections of this article correspond roughly to the various analysis steps. The following notation will be used throughout the article. The foreground red and green

A Review of Ensemble Methods in Bioinformatics

Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is two-fold. First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed.

BRAF Mutation, NRAS Mutation, and the Absence of an Immune-Related Expressed Gene Profile Predict Poor Outcome in Patients with Stage III Melanoma

Prediction of outcome for melanoma patients with surgically resected macroscopic nodal metastases is very imprecise. We performed a comprehensive clinico-pathologic assessment of fresh-frozen macroscopic nodal metastases and the preceding primary melanoma, somatic mutation profiling, and gene expression profiling to identify determinants of outcome in 79 melanoma patients. In addition to disease stage <II at initial presentation, the following clinical and pathologic factors were independent predictors of improved outcome (odds ratios for survival >4 years, 90% confidence interval): the presence of a nodular component in the primary melanoma (6.8, 0.6-76.0), and small cell size (11.1, 0.8-100.0) or low pigmentation (3.0, 0.8-100.0) in the nodal metastases. Absence of BRAF mutation (20.0, 1.0-1000.0) or NRAS mutation (16.7, 0.6-1000.0) were both favorable prognostic factors. A 46-gene expression signature with strong overrepresentation of immune response genes was predictive of better survival (10.9, 0.4-325.6); in the full cohort, median survival was >100 months in those with the signature, but 10 months in those without. This relationship was validated in two previously published independent stage III melanoma data sets. We conclude that the presence of BRAF mutation, NRAS mutation, and the absence of an immune-related expressed gene profile predict poor outcome in melanoma patients with macroscopic stage III disease.

Molecular Analysis of Gene Expression in the Developing Pontocerebellar Projection System

As an approach toward understanding the molecular mechanisms of neuronal differentiation, we utilized DNA microarrays to elucidate global patterns of gene expression during pontocerebellar development. Through this analysis, we identified groups of genes specific to neuronal precursor cells, associated with axon outgrowth, and regulated in response to contact with synaptic target cells. In the cerebellum, we identified a phase of granule cell differentiation that is independent of interactions with other cerebellar cell types. Analysis of pontine gene expression revealed that distinct programs of gene expression, correlated with axon outgrowth and synapse formation, can be decoupled and are likely influenced by different cells in the cerebellar target environment. Our approach provides insight into the genetic programs underlying the differentiation of specific cell types in the pontocerebellar projection system.

Identifying differentially expressed genes from microarray experiments via statistic synthesis

MOTIVATION A common objective of microarray experiments is the detection of differential gene expression between samples obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice. RESULTS Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in datasets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best individual statistics, while achieving robustness properties lacked by the individual statistics. We further evaluate performance on one other microarray study.

Normalization for cDNA microarry data

There are many sources of systematic variation in microarray experiments which affect the measured gene expression levels. Normalization is the term used to describe the process of removing such variation, e.g. for differences in labeling efficiency between the two fluorescent dyes. In this case, a constant adjustment is commonly used to force the distribution of the log-ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dy biases can depend on spot overall intensity and location on the array (print-tip effects). This paper describes normalization methods that account for intensity and spatial dependence in the dye biases for different types of cDNA microarray experiments, including dye-swap experiments. In addition, the choice of the subset of genes to use fo normalization is discussed. The subset selected may be different for experiments where only a few genes are expected to be differentially expressed and those where a majority of genes are expected to change. The proposed approaches are illustrated using gene expression data from a study of lipid metabolism in mice.

Comparison study of microarray meta-analysis methods

BackgroundMeta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods.ResultsWe compare eight meta-analysis methods, five existing methods, two naive methods and a novel approach (mDEDS). Comparisons are performed using simulated data and two biological case studies with varying degrees of meta-analysis complexity. The performance of meta-analysis methods is assessed via ROC curves and prediction accuracy where applicable.ConclusionsExisting meta-analysis methods vary in their ability to perform successful meta-analysis. This success is very dependent on the complexity of the data and type of analysis. Our proposed method, mDEDS, performs competitively as a meta-analysis tool even as complexity increases. Because of the varying abilities of compared meta-analysis methods, care should be taken when considering the meta-analysis method used for particular research.