Technical evaluation of cDNA microarrays

The aims were to evaluate the common reference design approach in microarray experiments and to evaluate the technical performance and the normalisation of cDNA microarrays with a limited number of spots. Total RNA from 3 normal and 3 tumour sample biopsies were used for synthesis of amino-allyl labelled cRNA. Equal amounts of cRNA from all samples were mixed as reference. After conjugation of cRNA with fluorophores (Cy3/Cy5), each individual tumour cRNA was hybridised to a cDNA microarray together with reference cRNA, scanned and analysed. We show that our procedures for producing cDNA microarrays are reproducible. The concordance between duplicated spots and replicate hybridisation was found to be high. We have demonstrated that our cDNA microarrays are of a high technical quality. The majority of the cDNA microarrays had low local spot background levels. Despite the high background levels for some local spots, variation could be minimized by locally weighted scatter plot smooth normalisation (LOWESS), which we showed was also suitable for normalisation of cDNA microarrays with a limited number of probes.     

Hotelling's T2 multivariate profiling for detecting differential expression in microarrays

The most widely used statistical methods for finding differentially expressed genes (DEGs) are essentially univariate. In this study, we present a new T(2) statistic for analyzing microarray data. We implemented our method using a multiple forward search (MFS) algorithm that is designed for selecting a subset of feature vectors in high-dimensional microarray datasets. The proposed T2 statistic is a corollary to that originally developed for multivariate analyses and possesses two prominent statistical properties. First, our method takes into account multidimensional structure of microarray data. The utilization of the information hidden in gene interactions allows for finding genes whose differential expressions are not marginally detectable in univariate testing methods. Second, the statistic has a close relationship to discriminant analyses for classification of gene expression patterns. Our search algorithm sequentially maximizes gene expression difference/distance between two groups of genes. Including such a set of DEGs into initial feature variables may increase the power of classification rules. We validated our method by using a spike-in HGU95 dataset from Affymetrix. The utility of the new method was demonstrated by application to the analyses of gene expression patterns in human liver cancers and breast cancers. Extensive bioinformatics analyses and cross-validation of DEGs identified in the application datasets showed the significant advantages of our new algorithm.     

Observation of intermittency in gene expression on cDNA microarrays

We used scaled factorial moments to search for intermittency in the log expression ratios (LERs) for thousands of genes spotted on cDNA microarrays (gene chips). Results indicate varying levels of intermittency in gene expression. The observation of intermittency in the data analyzed provides a complimentary handle on moderately expressed genes, generally not tackled by conventional techniques.     

Normal uniform mixture differential gene expression detection for cDNA microarrays

Background  

One of the primary tasks in analysing gene expression data is finding genes that are differentially expressed in different samples. Multiple testing issues due to the thousands of tests run make some of the more popular methods for doing this problematic.     

Indirect measurements of differential gene expression with cDNA microarrays

The use of universal RNA reference sets is an increasingly common approach to molecular classification studies with cDNA microarrays. Here we evaluated the reliability of indirect measurements of fluorescence ratios with a common RNA reference as a means of identifying differentially expressed genes. Comparisons of direct and indirect measures of differential gene expression showed a strong overall correlation in fluorescence ratio measurements but also a high degree of false positives in our indirect measurements. These results indicated that the application of more stringent ratio filters may be required when assessing differential gene expression utilizing a common RNA reference in classification studies.     

Strategy for the design of custom cDNA microarrays

DNA microarrays are valuable but expensive tools for expression profiling of cells, tissues, and organs. The design of custom microarrays leads to cost reduction without necessarily compromising their biological value. Here we present a strategy for designing custom cDNA microarrays and constructed a microarray for mouse immunology research (ImmunoChip). The strategy used interrogates expressed sequence tag databases available in the public domain but overcomes many of the problems encountered. Immunologically relevant clusters were selected based on the expression of expressed sequence tags in relevant libraries. Selected clusters were organized in modules, and the best representative clones were identified. When tested, this microarray was found to have minimal clone identity errors or phage contamination and identified molecular signatures of lymphoid cell lines. Our proposed design of custom microarrays avoids probe redundancy, allows the organization of the chip to optimize chip production, and reduces microarray production costs. The strategy described is also useful for the design of oligonucleotide microarrays.     

Reliability of gene expression ratios for cDNA microarrays in multiconditional experiments with a reference design

In a typical gene expression profiling experiment with multiple conditions, a common reference sample is used for co-hybridization with the samples to yield expression ratios. Differential expression for any other sample pair can then be calculated by assembling the ratios from their hybridizations with the reference. In this study we test the validity of this approach. Differential expression of a sample pair (i, j) was obtained in two ways: directly, by hybridizations of sample i versus j, and indirectly, by multiplying the expression ratios for hybridizations of sample i versus pool and pool versus sample j. We performed gene expression profiling using amphibian embryos (Xenopus laevis). Every sample combination of four different stages and a pool was profiled. Direct and indirect values were compared and used as the quality criterion for the data. Based on this criterion, 82% of all ratios were found to be sufficiently accurate. To increase the reliability of the signals, several widely used filtering techniques were tested. Filtering by differences of repeated hybridizations was found to be the optimal filter. Finally, we compared microarray-based gene expression profiles with the corresponding expression patterns obtained by whole-mount in situ hybridizations, resulting in a 90% correspondence.     

Mixture models for detecting differentially expressed genes in microarrays

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.     

Cloning of cDNA for UDP-glucose pyrophosphorylase and the expression of mRNA in rice endosperm

Rice endosperm UDP-glucose pyrophosphorylase (UGPase) cDNA clones were isolated by screening a lambda ZAP II library prepared from poly (A(+)) RNA of japonica rice (cv Sasanishiki) endosperm with a probe of potato UGPase cDNA. One cDNA clone, possessing about 1,700 nucleotides, contained the complete open reading frame of rice UGPase. At the nucleotide-sequence level, the UGPase cDNA of rice endosperm had high homology with the UGPase cDNA of barley endosperm (84%) and potato tuber (71%). The calculated molecular weight (50 kDa) agrees with the value determined by SDS-PAGE (51 kDa). At the amino-acid sequence level, rice UGPase has high homology with the UGPase of barley (92%) and potato (85%). The enzyme contained conserved sequence elements which are thought to be involved in substrate binding and catalytic activity. A Southern-blot analysis indicated that the gene existed as a single copy. Expression of the enzyme in rice endosperm examined by Northern-blot analysis was high at 10-15 days after heading.     

Correcting log ratios for signal saturation in cDNA microarrays

MOTIVATION: Pixel saturation occurs when the pixel intensity exceeds a threshold and the recorded pixel intensity is truncated. Microarray experiments are commonly afflicted with saturated pixels. As a result, estimators of gene expression are biased, with the amount of bias increasing as a function of the proportion of pixels saturated. Saturation is directly related to the photomultiplier tube (PMT) voltage settings and RNA abundance and is not necessarily associated with poor array or poor spot quality. When choosing PMT settings, higher PMT settings are desired because of improved signal-to-noise ratios of low-intensity spots. This improved signal is somewhat offset by saturation of high-intensity spots. In practice, spots with saturated pixels are discarded or the biased value is used. Neither of these approaches is appealing, particularly the former approach when a highly expressed gene is discarded because of saturation. RESULTS: We present a method to correct for saturation using pixel-level data. The method is based on a censored regression model. Evaluations on several arrays indicate that the method performs well. Simulation studies suggest that the method is robust under certain model violations.     

Use of a cDNA library for the study of mRNA changes during muscle differentiation

A cDNA library was used to measure changes in many individual mRNAs during muscle differentiation in culture. A library of 1000 clones was constructed from total myofiber poly(A) RNA. About 23% of these clones gave a detectable colony hybridization signal using end-labeled myofiber mRNA, the remainder containing muscle sequences too rare to be detected with this assay. The 230 positive clones were grouped into four classes based on relative visual intensity. Reconstruction experiments using pure globin mRNA enable us to determine the approximate percentage of total RNA made up by each mRNA hybridizing to a cDNA clone. Those clones containing sequences complementary to developmentally regulated mRNAs were identified by a differential hybridization procedure. The cDNA library was screened with end-labeled mRNA from both undifferentiated myoblasts and differentiated myofibers. Although the bulk of the clones hybridized essentially the same with both RNA populations, several dozen were found which hybridized differentially. Some clones contained sequences which were not present at all in myoblasts and present in very high quantities in myofibers. Others contained sequences found in both myoblasts and myofibers but in increased quantities in the differentiated cells. Still others contained sequences which decreased in quantity during muscle differentiation. The clones in the first group were chosen for immediate analysis since they likely contain contractile protein mRNA sequences. However, all the characterized cDNA clones can now be used as probes to study the chromosomal organization and developmental expression of genes active during muscle differentiation.     

Analysis of repeatability in spotted cDNA microarrays

We report a strategy for analysis of data quality in cDNA microarrays based on the repeatability of repeatedly spotted clones. We describe how repeatability can be used to control data quality by developing adaptive filtering criteria for microarray data containing clones spotted in multiple spots. We have applied the method on five publicly available cDNA microarray data sets and one previously unpublished data set from our own laboratory. The results demonstrate the feasibility of the approach as a foundation for data filtering, and indicate a high degree of variation in data quality, both across the data sets and between arrays within data sets.