Selection of control genes for quantitative RT-PCR based on microarray data

Use of internal reference gene(s) is necessary for adequate quantification of target gene expression by RT-PCR. Herein, we elaborated a strategy of control gene selection based on microarray data and illustrated it by analyzing endomyocardial biopsies with acute cardiac rejection and infection. Using order statistics and binomial distribution we evaluated the probability of finding low-varying genes by chance. For analysis, the microarray data were divided into two sample subsets. Among the first 10% of genes with the lowest standard deviations, we found 14 genes common to both subsets. After normalization using two selected genes, high correlation was observed between expression of target genes evaluated by microarray and RT-PCR, and in independent dataset by RT-PCR (r = 0.9, p < 0.001). In conclusion, we showed a simple and reliable strategy of selection and validation of control genes for RT-PCR from microarray data that can be easily applied for different experimental designs and tissues.     

cRNA target preparation for microarrays: comparison of gene expression profiles generated with different amplification procedures

Microarray technology has become a standard tool for generation of gene expression profiles to explore human disease processes. Being able to start from minute amounts of RNA extends the fields of application to core needle biopsies, laser capture microdissected cells, and flow-sorted cells. Several RNA amplification methods have been developed, but no extensive comparability and concordance studies of gene expression profiles are available. Different amplification methods may produce differences in gene expression patterns. Therefore, we compared profiles processed by a standard microarray protocol with three different types of RNA amplification: (i) two rounds of linear target amplification, (ii) random amplification, and (iii) amplification based on a template switching mechanism. The latter two methods accomplish target amplification in a nonlinear way using PCR technology. Starting from as little as 50 ng of total RNA, the yield of labeled cRNA was sufficient for hybridization to Affymetrix HG-U133A GeneChip array using the respective methods. Replicate experiments were highly reproducible for each method. In comparison with the standard protocol, all three approaches are less sensitive and introduced a minor but clearly detectable bias of the detection call. In conclusion, the three amplification protocols used are applicable for GeneChip analysis of small tissue samples.     

基因芯片在衣原体研究中的应用

基因芯片又称为DNA微阵列,是指将大量核酸片段以预先设计的方式固定在载体上组成密集分子阵列,与荧光素或其他方式标记的样品进行杂交,通过检测杂交信号的强弱来判断样品中有无靶分子以及对靶分子进行定量,是一种研究生物大分子功能的新技术。在衣原体研究方面,基因芯片主要应用于衣原体的检测与分型、感染机制的研究、特定基因作用分析、毒力及耐药基因的筛选等。     

Comparison of commercial probe labeling kits for microarray: towards quality assurance and consistency of reactions

Microarray technology is readily available to scientists interested in gene expression. Commensurate with this availability is the growing market in accessory products offering convenience but potentially variable performance. Here we evaluate seven commercial kits for probe labeling against a human apoptosis oligonucleotide array. All kits were found to label probes successfully using the manufacturers' instructions. The Stratagene Fairplay Microarray Labeling Kit was the most sensitive, with an overall call rate of 74% and the lowest rate of indeterminant calls for the HEK and HepG2 cell lines. The Invitrogen SuperScript Indirect cDNA Labeling System showed the most reproducible gene expression pattern and the least technical variation, both in terms of signal strength and between replicates on each array. The Promega Pronto! Plus System showed the least dye bias however, a higher level of variation between replicates was observed. Pairwise comparisons revealed that the Promega Pronto! Plus System and Invitrogen SuperScript Indirect cDNA Labeling System had the most similarity in their patterns of gene expression. Results obtained suggest variability in the performance of commercial kits between different manufacturers. This study supports the need to conduct comparative evaluations of commercial microarray probe labeling kits and the need for validation prior to use.     

Gene profiling for determining pluripotent genes in a time course microarray experiment

In microarray experiments, it is often of interest to identifygenes which have a prespecified gene expression profile withrespect to time. Methods available in the literature are, however,typically not stringent enough in identifying such genes, particularlywhen the profile requires equivalence of gene expression levelsat certain time points. In this paper, the authors introducea new methodology, called gene profiling, that uses simultaneousdifferential and equivalent gene expression level testing torank genes according to a prespecified gene expression profile.Gene profiling treats the vector of true gene expression levelsas a linear combination of appropriate vectors, for example,vectors that give the required criteria for the profile. Thisgene profile model is fitted to the data, and the resultingparameter estimates are summarized in a single test statisticthat is then used to rank the genes. The theoretical underpinningsof gene profiling (equivalence testing, intersection–uniontests) are discussed in this paper, and the gene profiling methodologyis applied to our motivating stem-cell experiment.     

A new geometric biclustering algorithm based on the Hough transform for analysis of large-scale microarray data

Biclustering is an important tool in microarray analysis when only a subset of genes co-regulates in a subset of conditions. Different from standard clustering analyses, biclustering performs simultaneous classification in both gene and condition directions in a microarray data matrix. However, the biclustering problem is inherently intractable and computationally complex. In this paper, we present a new biclustering algorithm based on the geometrical viewpoint of coherent gene expression profiles. In this method, we perform pattern identification based on the Hough transform in a column-pair space. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our studies show that the approach can discover significant biclusters with respect to the increased noise level and regulatory complexity. Furthermore, we also test the ability of our method to locate biologically verifiable biclusters within an annotated set of genes.     

Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis

An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.     

Optimization of fragmentation conditions for microarray analysis of viral RNA

An important consideration in microarray analysis of nucleic acids is the efficiency with which the target molecule is captured by, or hybridized to, surface-immobilized oligos. For RNA, secondary and tertiary structure of the target strand can significantly decrease capture efficiency. To overcome this limitation, RNA is often fragmented to reduce structural effects. In this study, the metal ion-catalyzed base hydrolysis fragmentation conditions for viral RNA extracted from influenza viruses were evaluated and the hybridization efficiency of the resulting fragments was determined as a function of fragment length. The amount of RNA captured was evaluated qualitatively by fluorescence intensity normalized to an internal standard. Optimized conditions for influenza RNA were determined to include a fragmentation time of 20-30 min at 75 degrees C. These conditions resulted in a maximum concentration of fragments between 38 and 150 nt in length and a maximum in the capture and label efficiency.     

Outlier sums for differential gene expression analysis

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).     

Microarray gene expression analysis free of reverse transcription and dye labeling

A new microarray system has been developed for gene expression analysis using cationic gold nanoparticles with diameters of 250 nm as a target detection reagent. The approach utilizes nonlabeled target molecules hybridizing with complementary probes on the array, followed by incubation in a colloidal gold solution. The hybridization signal results from the precipitation of nanogold particles on the hybridized spots due to the electrostatic attraction of the cationic gold particles and the anionic phosphate groups in the target DNA backbone. In contrast to conventional fluorescent detection, this nanoparticle-based detection system eliminates the target labeling procedure. The visualization of hybridization signals can be accomplished with a flatbed scanner instead of a confocal laser scanner, which greatly simplifies the process and reduces the cost. The sensitivity is estimated to be less than 2 pg of DNA molecules captured on the array surface. The signal from hybridized spots quantitatively represents the amount of captured target DNA and therefore permits quantitative gene expression analysis. Cross-array reproducibility is adequate for detecting twofold or less signal changes across two microarray experiments.     

Using ANOVA for gene selection from microarray studies of the nervous system

Methods are presented for detecting differential expression using statistical hypothesis testing methods including analysis of variance (ANOVA). Practicalities of experimental design, power, and sample size are discussed. Methods for multiple testing correction and their application are described. Instructions for running typical analyses are given in the R programming environment. R code and the sample data set used to generate the examples are available at http://microarray.cpmc.columbia.edu/pavlidis/pub/aovmethods/.     

Assessing co-regulation of directly linked genes in biological networks using microarray time series analysis

Differential expression of genes detected with the analysis of high throughput genomic experiments is a commonly used intermediate step for the identification of signaling pathways involved in the response to different biological conditions. The impact analysis was the first approach for the analysis of signaling pathways involved in a certain biological process that was able to take into account not only the magnitude of the expression change of the genes but also the topology of signaling pathways including the type of each interactions between the genes. In the impact analysis, signaling pathways are represented as weighted directed graphs with genes as nodes and the interactions between genes as edges. Edges weights are represented by a β factor, the regulatory efficiency, which is assumed to be equal to 1 in inductive interactions between genes and equal to −1 in repressive interactions. This study presents a similarity analysis between gene expression time series aimed to find correspondences with the regulatory efficiency, i.e. the β factor as found in a widely used pathway database. Here, we focused on correlations among genes directly connected in signaling pathways, assuming that the expression variations of upstream genes impact immediately downstream genes in a short time interval and without significant influences by the interactions with other genes. Time series were processed using three different similarity metrics. The first metric is based on the bit string matching; the second one is a specific application of the Dynamic Time Warping to detect similarities even in presence of stretching and delays; the third one is a quantitative comparative analysis resulting by an evaluation of frequency domain representation of time series: the similarity metric is the correlation between dominant spectral components. These three approaches are tested on real data and pathways, and a comparison is performed using Information Retrieval benchmark tools, indicating the frequency approach as the best similarity metric among the three, for its ability to detect the correlation based on the correspondence of the most significant frequency components.