Statistical methods for analyzing microarray feature data with replications.

Expression levels in oligonucleotide microarray experiments depend on a potentially large number of factors, for example, treatment conditions, different probes, different arrays, and so on. To dissect the effects of these factors on expression levels, fixed-effects ANOVA methods have previously been proposed. Because we are not necessarily interested in estimating the specific effects of different probes and arrays, we propose to treat these as random effects. Then we only need to estimate their means and variances but not the effect of each of their levels; that is, we can work with a much reduced number of parameters and, consequently, higher precision for estimating expression levels. Thus, we developed a mixed-effects ANOVA model with some random and some fixed effects. It automatically accounts for local normalization between different arrays and for background correction. The method was applied to each of the 6,584 genes investigated in a microarray experiment on two mouse cell lines, PA6/S and PA6/8, where PA6/S enhances proliferation of Pre B cells in vitro but PA6/8 does not. To detect a set of differentially expressed genes (multiple testing problem), we applied the method of controlling the false discovery rate (FDR), which successfully identified 207 genes with significantly different expression levels.     

Platinum complexes for multi-parametric assays using microarray systems

Here, we present a two novel fluorescent dyes ethylenediaminechlorocholylglycinateplatinum(II), [Pt(CG)Cl(en)] complex 1and bisursodeoxycholate(ethylenediamine)platinum(II), [Pt(UDC)₂(en)] complex 2 based on well-known cis-platin chemistry. These platinum complexes contain cholylglycinate (CG) and ursodeoxycholate (UDC) as ligands. These compounds enable qualitative detection of double-helix DNA and quantitative detection (from pg to μg). These novel compounds have absorption and emission spectra in a difference range as the common ones (for example: cyanine dyes such as Cy3, Cy5, Cy7,…); therefore, it could allow the multi-parametric detection of DNA arrays, incrementing the capacity of experimental performance per one single array. As a consequence, it will increase the amount of data info obtained per chip.The combination of the intrinsic property of this compounds with the optical properties in different fluorescence channels, can allow introducing a new molecule with a wide range of possible applications in DNA arrays.     

Particle flow assays for fluorescent protein microarray applications

Microarray technology has brought a paradigmatic change in bioanalytics. However, highly sensitive and accurate assays are still needed for a real breakthrough. We present a simple and generic approach for fluorescent signal amplification with fluorescent microparticle labels. The assay principle was demonstrated using a reverse array model consisting of spots of bovine serum albumin with a small fraction of the proteins biotinylated. Specific binding of streptavidin coated fluorescent microparticles to the spots was promoted by applying a controlled continuous microparticle flow. The surface bound beads were visualized and quantified with confocal microscopy images. Comparison with standard fluorescent and flow discrimination assays has revealed several advantages of our approach. First, non-specific particle binding could be reduced to less than 1 particle/spot making therefore the visualization of single biomolecular bonds possible. Second, the amplification scheme presented here is generic and can be applied to any fluorescent microarray. Furthermore, this assay makes use of a biotin-streptavidin linkage and can therefore be applied to all kind of assays. Finally, single fluorescent microbeads can be easily visualized with standard optical equipments, so that no high performance equipment is required.     

Statistical designs for two-color spotted microarray experiments

Two-color cDNA or oligonucleotide-based spotted microarrays have been commonly used in measuring the expression levels of thousands of genes simultaneously. To realize the immense potential of this powerful new technology, budgeted within limited resources or other constraints, practical designs with high efficiencies are in demand. In this study, we address the design issue concerning the arrangement of the mRNA samples labeled with fluorescent dyes and hybridized on the slides. A normalization model is proposed to characterize major sources of systematic variation in a two-color microarray experiment. This normalization model establishes a connection between designs for two-color microarray experiments with a particular class of classical row-column designs. A heuristic algorithm for constructing A-optimal or highly efficient designs is provided. Statistical optimality results are found for some of the designs generated from the algorithm. It is believed that the constructed designs are the best or very close to the best possible for estimating the relative gene expression levels among the mRNA samples of interest.     

Cooperativity of paired oligonucleotide probes for microarray hybridization assays

Synthetic DNA probes attached to microarrays usually range in length from 25 to 70 nucleotides. There is a compromise between short probes with lower sensitivity, which can be accurately synthesized in higher yields, and long probes with greater sensitivity but lower synthesis yields. Described here are microarrays printed with spots containing a mixture of two short probes, each designed to hybridize at noncontiguous sites in the same targeted sequence. We have shown that, for a printed microarray, mixed probe spots containing a pair of 30mers show significantly greater hybridization than spots containing a single 30mer and can approach the amount of hybridization to spots containing a 60mer or a 70mer. These spots with mixed oligonucleotide probes display cooperative hybridization signals greater than those that can be achieved by either probe alone. Both the higher synthesis yields of short probes and the greater sensitivity of long oligonucleotides can be utilized. This strategy provides new design options for microarray hybridization assays to detect RNA abundance, RNA splice variants, or sequence polymorphisms.     

Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer

With the proliferation of related microarray studies by independent groups, a natural step in the analysis of these gene expression data is to combine the results across these studies. However, this raises a variety of issues in the analysis of such data. In this article, we discuss the statistical issues of combining data from multiple gene expression studies. This leads to more complications than those in standard meta-analyses, including different experimental platforms, duplicate spots and complex data structures. We illustrate these ideas using data from four prostate cancer profiling studies. In addition, we develop a simple approach for assessing differential expression using the LASSO method. A combination of the results and the pathway databases are then used to generate candidate biological pathways for cancer.     

Statistical process control for large scale microarray experiments

MOTIVATION: Maintaining and controlling data quality is a key problem in large scale microarray studies. In particular systematic changes in experimental conditions across multiple chips can seriously affect quality and even lead to false biological conclusions. Traditionally the influence of these effects can be minimized only by expensive repeated measurements, because a detailed understanding of all process relevant parameters seems impossible. RESULTS: We introduce a novel method for microarray process control that estimates quality based solely on the distribution of the actual measurements without requiring repeated experiments. A robust version of principle component analysis detects single outlier microarrays and thereby enables the use of techniques from multivariate statistical process control. In particular, the T(2) control chart reliably tracks undesired changes in process relevant parameters. This can be used to improve the microarray process itself, limits necessary repetitions to only affected samples and therefore maintains quality in a cost effective way. We prove the power of the approach on 3 large sets of DNA methylation microarray data.     

Statistical methods and software for the analysis of highthroughput reverse genetic assays using flow cytometry readouts

Highthroughput cell-based assays with flow cytometric readout provide a powerful technique for identifying components of biologic pathways and their interactors. Interpretation of these large datasets requires effective computational methods. We present a new approach that includes data pre-processing, visualization, quality assessment, and statistical inference. The software is freely available in the Bioconductor package prada. The method permits analysis of large screens to detect the effects of molecular interventions in cellular systems.     

Application of Fluolid-Orange-labeled probes for DNA microarray and immunological assays

The usefulness of Fluolid-Orange, a novel fluorescent dye, for DNA microarray and immunological assays has been examined. Fluolid-Orange-labeled probes (DNA and IgG) were stable as examined by laser-photo-bleaching and under heat and dry conditions. Statistical analyses were performed to evaluate the reproducibility of the microarray assay, while stage-specific immunostaining of marker proteins, Kank1 and calretinin, was performed for renal cancers, both giving satisfactory results. The stability of the dye should provide advantages for storing fluorescently labeled probes and re-examining the specimens later in genetic and pathological diagnostics.     

Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer

MOTIVATION: It is a common practice in cancer microarray experiments that a normal tissue is collected from the same individual from whom the tumor tissue was taken. The indirect design is usually adopted for the experiment that uses a common reference RNA hybridized both to normal and tumor tissues. However, it is often the case that the test material is not large enough for the experimenter to extract enough RNA to conduct the microarray experiment. Hence, collecting n cases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n1, and two independent samples of sizes n2 and n3, respectively, for 'reference versus normal tissue only' and 'reference versus tumor tissue only' hybridizations (n=n1 + n2 + n3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed dataset. RESULTS: We propose a new test statistic, t3, as a means of combining all the information in the mixed dataset for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended receiver operating characteristic approach to the mixed dataset. We devised a measure of disagreement between a RT-PCR experiment and a microarray experiment. Hotelling's T2 statistic is employed to detect a set of DE genes and its prediction rate is compared with the prediction rate of a univariate procedure. We observe that Hotelling's T2 statistic detects DE genes more efficiently than a univariate procedure and that further research is warranted on the formal test procedure using Hotelling's T2 statistic. CONTACT: bskim@yonsei.ac.kr.     

Statistical methodology for the analysis of dye-switch microarray experiments

Background  

In individually dye-balanced microarray designs, each biological sample is hybridized on two different slides, once with Cy3 and once with Cy5. While this strategy ensures an automatic correction of the gene-specific labelling bias, it also induces dependencies between log-ratio measurements that must be taken into account in the statistical analysis.     

Statistical tests for differential expression in cDNA microarray experiments

Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation.     

Statistical implications of pooling RNA samples for microarray experiments

Background  

Microarray technology has become a very important tool for studying gene expression profiles under various conditions. Biologists often pool RNA samples extracted from different subjects onto a single microarray chip to help defray the cost of microarray experiments as well as to correct for the technical difficulty in getting sufficient RNA from a single subject. However, the statistical, technical and financial implications of pooling have not been explicitly investigated.     

Clustering methods for microarray gene expression data

Within the field of genomics, microarray technologies have become a powerful technique for simultaneously monitoring the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods to identify groups of genes that manifest similar expression patterns and are activated by similar conditions. The corresponding analysis problem is to cluster multi-condition gene expression data. The purpose of this paper is to present a general view of clustering techniques used in microarray gene expression data analysis.     

Statistical analysis of oligonucleotide microarray data

Microchip arrays have become one of the most rapidly growing techniques for monitoring gene expression at the genomic level and thereby gaining valuable insight about various important biological mechanisms. Examples of such mechanisms are: identifying disease-causing genes, genes involved in the regulation of some aspect of the cell cycle, etc. In this article, we discuss the problem of estimating gene expression based on a proper statistical model. More precisely, we show how the model introduced by Li and Wong can be used in its full bivariate generality to provide a new measure of gene expression from high-density oligonucleotide arrays. We also present a second gene expression index based on a new way of reducing the model into a simpler univariate model. In both cases, the gene expression indices are shown to be unbiased and to have lower variance than the established ones. Moreover, we present a bootstrap method aiming at providing non-parametric confidence intervals for the expression index.     

Evaluation of normalization methods for microarray data

Background

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.

Results

In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.

Conclusions

Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

     

Comparing transformation methods for DNA microarray data

Background  

When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer.