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PubMed2006

Feature selection using Haar wavelet power spectrum.

BACKGROUND: Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum. RESULTS: Haar wavelet power spectra of genes were analysed and it was observed to be different in different diagnostic categories. This difference in trend and magnitude of the spectrum may be utilized in gene selection. Most of the genes selected by earlier complex methods were selected by the very simple present method. Each earlier works proved only few genes are quite enough to approach the classification problem 1. Hence the present method may be tried in conjunction with other classification methods. The technique was applied without removing the noise in data to validate the robustness of the method against the noise or outliers in the data. No special software or complex implementation is needed. The qualities of the genes selected by the present method were analysed through their gene expression data. Most of them were observed to be related to solve the classification issue since they were dominant in the diagnostic category of the dataset for which they were selected as features. CONCLUSION: In the present paper, the problem of feature selection of microarray gene expression data was considered. We analyzed the wavelet power spectrum of genes and proposed a clustering and feature selection method useful for classification based on Haar wavelet power spectrum. Application of this technique in this area is novel, simple, and faster than other methods, fit for a wide range of data types. The results are encouraging and throw light into the possibility of using this technique for problem domains like disease classification, gene network identification and personalized drug design.

Artificial Intelligence↗
PubMed2006

In search of functional association from time-series microarray data based on the change trend and level of gene expression.

BACKGROUND: The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. RESULTS: In this work we present a new method based on extracting main features of the change trend and level of gene expression between consecutive time points. The method, termed as trend correlation (TC), includes two major steps: 1, calculating a maximal local alignment of change trend score by dynamic programming and a change trend correlation coefficient between the maximal matched change levels of each gene pair; 2, inferring relationships of gene pairs based on two statistical extraction procedures. The new method considers time shifts and inverted relationships in a similar way as the local clustering (LC) method but the latter is merely based on a point-to-point comparison. The TC method is demonstrated with data from yeast cell cycle and compared with the LC method and the widely used Pearson correlation coefficient (PCC) based clustering method. The biological significance of the gene pairs is examined with several large-scale yeast databases. Although the TC method predicts an overall lower number of gene pairs than the other two methods at a same p-value threshold, the additional number of gene pairs inferred by the TC method is considerable: e.g. 20.5% compared with the LC method and 49.6% with the PCC method for a p-value threshold of 2.7E-3. Moreover, the percentage of the inferred gene pairs consistent with databases by our method is generally higher than the LC method and similar to the PCC method. A significant number of the gene pairs only inferred by the TC method are process-identity or function-similarity pairs or have well-documented biological interactions, including 443 known protein interactions and some known cell cycle related regulatory interactions. It should be emphasized that the overlapping of gene pairs detected by the three methods is normally not very high, indicating a necessity of combining the different methods in search of functional association of genes from time-series data. For a p-value threshold of 1E-5 the percentage of process-identity and function-similarity gene pairs among the shared part of the three methods reaches 60.2% and 55.6% respectively, building a good basis for further experimental and functional study. Furthermore, the combined use of methods is important to infer more complete regulatory circuits and network as exemplified in this study. CONCLUSION: The TC method can significantly augment the current major methods to infer functional linkages and biological network and is well suitable for exploring temporal relationships of gene expression in time-series data.

Algorithms↗
PubMed2005

Evaluation of the similarity of gene expression data estimated with SAGE and Affymetrix GeneChips.

BACKGROUND: Serial Analysis of Gene Expression (SAGE) and microarrays have found a widespread application, but much ambiguity exists regarding the evaluation of these technologies. Cross-platform utilization of gene expression data from the SAGE and microarray technology could reduce the need for duplicate experiments and facilitate a more extensive exchange of data within the research community. This requires a measure for the correspondence of the different gene expression platforms. To date, a number of cross-platform evaluations (including a few studies using SAGE and Affymetrix GeneChips) have been conducted showing a variable, but overall low, concordance. This study evaluates these overall measures and introduces the between-ratio difference as a concordance measure pergene. RESULTS: In this study, gene expression measurements of Unigene clusters represented by both Affymetrix GeneChips HG-U133A and SAGE were compared using two independent RNA samples. After matching of the data sets the final comparison contains a small data set of 1094 unique Unigene clusters, which is unbiased with respect to expression level. Different overall correlation approaches, like Up/Down classification, contingency tables and correlation coefficients were used to compare both platforms. In addition, we introduce a novel approach to compare two platforms based on the calculation of differences between expression ratios observed in each platform for each individual transcript. This approach results in a concordance measure per gene (with statistical probability value), as opposed to the commonly used overall concordance measures between platforms. CONCLUSION: We can conclude that intra-platform correlations are generally good, but that overall agreement between the two platforms is modest. This might be due to the binomially distributed sampling variation in SAGE tag counts, SAGE annotation errors and the intensity variation between probe sets of a single gene in Affymetrix GeneChips. We cannot identify or advice which platform performs better since both have their (dis)-advantages. Therefore it is strongly recommended to perform follow-up studies of interesting genes using additional techniques. The newly introduced between-ratio difference is a filtering-independent measure for between-platform concordance. Moreover, the between-ratio difference per gene can be used to detect transcripts with similar regulation on both platforms.

Cell Line, Tumor↗
PubMed2006

Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification.

PURPOSE: To develop a gene expression-based classifier for neuroblastoma patients that reliably predicts courses of the disease. PATIENTS AND METHODS: Two hundred fifty-one neuroblastoma specimens were analyzed using a customized oligonucleotide microarray comprising 10,163 probes for transcripts with differential expression in clinical subgroups of the disease. Subsequently, the prediction analysis for microarrays (PAM) was applied to a first set of patients with maximally divergent clinical courses (n = 77). The classification accuracy was estimated by a complete 10-times-repeated 10-fold cross validation, and a 144-gene predictor was constructed from this set. This classifier's predictive power was evaluated in an independent second set (n = 174) by comparing results of the gene expression-based classification with those of risk stratification systems of current trials from Germany, Japan, and the United States. RESULTS: The first set of patients was accurately predicted by PAM (cross-validated accuracy, 99%). Within the second set, the PAM classifier significantly separated cohorts with distinct courses (3-year event-free survival [EFS] 0.86 +/- 0.03 [favorable; n = 115] v 0.52 +/- 0.07 [unfavorable; n = 59] and 3-year overall survival 0.99 +/- 0.01 v 0.84 +/- 0.05; both P < .0001) and separated risk groups of current neuroblastoma trials into subgroups with divergent outcome (NB2004: low-risk 3-year EFS 0.86 +/- 0.04 v 0.25 +/- 0.15, P < .0001; intermediate-risk 1.00 v 0.57 +/- 0.19, P = .018; high-risk 0.81 +/- 0.10 v 0.56 +/- 0.08, P = .06). In a multivariate Cox regression model, the PAM predictor classified patients of the second set more accurately than risk stratification of current trials from Germany, Japan, and the United States (P < .001; hazard ratio, 4.756 [95% CI, 2.544 to 8.893]). CONCLUSION: Integration of gene expression-based class prediction of neuroblastoma patients may improve risk estimation of current neuroblastoma trials.

Biomarkers, Tumor↗
PubMed2005

Growing applications and advancements in microarray technology and analysis tools.

In today's field of genomics, traditional gene-by-gene approach is not adequate to meet the demand of processing information generated from mapping the complex biology of the human genome. More global views of analyzing the magnitude of information are necessary, such as with microarrays. Microarray technology today is rapidly uncovering broad patterns of genetic activity and showing insight into gene functions, processes, and pathways. With the growing technology, imminent knowledge is being generated looking into transcriptional processes and biological mechanisms from many different organisms and phylogeny. Many tools are being developed to assist with the analysis of such high-throughput data, many applications are being utilized by this technology, and the field is growing and expanding rapidly to accommodate the expanding genomics era.

Algorithms↗
PubMed2006

Temperature effects on DNA chip experiments from surface plasmon resonance imaging: isotherms and melting curves.

We present an analysis of hybridization experiments on a DNA chip studied by surface plasmon resonance imaging. The reaction constants at various temperatures and for different probe lengths are obtained from Langmuir isotherms and hybridization kinetics. The melting curves from temperature scans are also obtained without any labeling of the targets. The effects of the probe length on the hybridization thermodynamics, deduced from the temperature dependence of the reaction constants as well as from the melting curves, suggest dispersion in the length of the hybridization segments of the probes accessible to the targets. Those are, however, sufficient to suggest efficient point mutation detection from temperature scans.

Artifacts↗
PubMed2004

Characterization of an inexpensive, nontoxic, and highly sensitive microarray substrate.

An agarose film has been proposed as an efficient substrate for producing microarrays. The original film preparation procedure was simplified significantly by grafting the agarose layer directly onto unmodified microscope glass slides instead of aminated glass slides, and the blocking procedure was replaced with a wash in 0.1x standard saline citrate (SSC) and 0.5% sodium dodecyl sulfate (SDS) without decreasing the performance of the produced microarrays. Characterization of the grafted agarose film using atomic force microscopy (AFM) and scanning electron microscopy (SEM) showed that the agarose film had a 10-fold increase in surface roughness compared to glass and that the interior of the agarose film was porous, with pore sizes between 100-500 nm. A comparison of hybridization on aldehyde-activated agarose-coated microarray slides and commercial amino-reactive microarray slides showed that aldehyde-activated agarose-coated slides had the highest signal-to-noise ratio of 850, suggesting that the aldehyde-activated agarose microarray slides are suitable in applications where analytes have a wide concentration range. By immobilizing the DNA probes using ultraviolet (UV) light, the signal-to-noise ratio was further increased to 3000 on the agarose microarray slides. The specificity of the UV cross-linked DNA probes was demonstrated using 21 and 25 bp long capture probes, enabling discrimination of target molecules differing in only one base.

Adsorption↗
PubMed2005

Improved real-time RT-PCR method for high-throughput measurements using second derivative calculation and double correction.

Quantification of mRNA expression levels using real-time reverse transcription PCR (RT-PCR) is increasingly used to validate results of DNA microarrays or GeneChips. It requires an improved method that is more robust and more suitable for high-throughput measurements. In this report, we compare a user non-influent, second derivative method with that of a user influent, fit point method that is widely used in the literature. We also describe the advantage of using a double correction: one correction using the expression levels of a housekeeping gene of an experiment as an internal standard and a second using reference expression levels of the same housekeeping gene in the tissue or cells. The first correction permits one to decrease errors due to sample preparation and handling, while the second correction permits one to avoid the variation of the results with the variability of housekeeping in each tissue, especially in experiments using various treatments. The data indicate that the real-time PCR method is highly efficient with an efficiency coefficient close to the theoretical value of two. The results also show that the second derivative method is more accurate than the fit point method in quantifying low gene expression levels. Using triplicate experiments, we show that measurement variations using our method are low with a mean of variation coefficients of <1%.

Algorithms↗
PubMed2005

Diagnostic plots for detecting outlying slides in a cDNA microarray experiment.

Different sources of systematic and random error variations are often observed in cDNA microarray experiments. A simple scatter plot is commonly used to examine outlying slides that have unusual expression patterns or larger variability than other slides. These outlying slides tend to have large impacts on the subsequent analyses, such as identification of differentially expressed genes and clustering analysis. However, it is difficult to select outlying slides rigorously and consistently based on subjective human pattern recognition on their scatter plots. A graphical method and a rigorous diagnostic measure are proposed to detect outlying slides. The proposed graphical method is easy to implement and shown to be quite effective in detecting outlying slides in real microarray data sets. This diagnostic measure is also informative to compare variability among slides. Two cDNA microarray data sets are carefully examined to illustrate the proposed approach. A 3840-gene microarray experiment for neuronal differentiation of cortical stem cells and a 2076-gene microarray experiment for anticancer compound time-course expression of the NCI-60 cancer cell lines.

Algorithms↗
PubMed2006

Alternative mRNA polyadenylation can potentially affect detection of gene expression by affymetrix genechip arrays.

DNA microarrays have been widely used to examine gene expression. The Affymetrix GeneChip is one of the most commonly used platforms, employing DNA probes of 25 nucleotides designed to hybridise to different regions of target mRNA. The targeted region is often biased toward the 3' end of mRNA, which can lead to biases in detection. A large number of mammalian genes can undergo alternative polyadenylation under different cellular conditions. Multiple polyadenylation sites can lead to variable transcripts with different hybridisation properties. Here, we surveyed probes on human, mouse and rat GeneChip arrays and found that the detection of a significant proportion of mRNAs can potentially be affected by alternative polyadenylation. This could lead to inaccurate interpretation of GeneChip data when the changes of expression values actually result from alternative use of polyadenylation sites.

Artifacts↗
PubMedDate not supplied

[Use of the technology of the genetic chip to study of the nitric oxide synthase].

INTRODUCTION: The end of our century has been characterized by a quick scientific and technological growth. Among other, the brain, the human genome project and the computer science, they are some of the numerous fields that characterize this revolution. DEVELOPMENT: In the following work, we will show as these three fields of the knowledge have converged in a new area of the science denominated technology of the gene chip or microarrays. Although this methodology would allow the study of thousands of genes in one chip, in the following work we will center ourselves in its potential application for the study of the enzyme nitric oxide synthase (NOS), which is responsible for the production of the nitric oxide (NO). The technical base of this system is to be able to synthesize chains of DNA in a chip, which will be hybridized with the sample of interest that in turn are usually marked with fluorescent nucleotides. The results are analyzed using a sophisticated image analysis system. The relative estimate of the quantity of existent messenger in each sample will come determined by the intensity of the fluorescence, and for its comparison with internal controls. The discovery of at least three genes that are coded for NOS, as well as that of numerous allelic variations will allow a more exhaustive study of this molecule, which has been recognized by their vital importance. CONCLUSION: In this review we will put in perspective the utility of this technology in the genetic study of the enzyme NOS, and its utility for the understanding of the pathophysiology of some neurological illnesses.

Brain↗
PubMedDate not supplied

DNA methods: critical review of innovative approaches.

The presence of ingredients derived from genetically modified organisms (GMOs) in food products in the market place is subject to a number of European regulations that stipulate which product consisting of or containing GMO-derived ingredients should be labeled as such. In order to maintain these labeling requirements, a variety of different GMO detection methods have been developed to screen for either the presence of DNA or protein derived from (approved) GM varieties. Recent incidents where unapproved GM varieties entered the European market show that more powerful GMO detection and identification methods will be needed to maintain European labeling requirements in an adequate, efficient, and cost-effective way. This report discusses the current state-of-the-art as well as future developments in GMO detection.

DNA, Plant↗
PubMed2002

A new clustering method for microarray data analysis.

A novel clustering approach is introduced to overcome data missing and inconsistency of gene expression levels under different conditions in the stage of clustering. It is based on the so-called smooth score, which is defined for measuring the deviation of the expression level of a gene and the average expression level of all the genes involved under a condition. We present an efficient greedy algorithm for finding clusters with smooth score below a threshold after studying its computational complexity. The algorithm was tested intensively on random matrixes and a yeast data. It was shown to perform well in finding co-regulation patterns in a test with the yeast data.

Algorithms↗
PubMed2002

An adaptive meta-clustering approach: combining the information from different clustering results.

With the development of microarray techniques, there is an increasing need of information processing methods to analyze the high throughput data. Clustering is one of the most promising candidates because of its simplicity, flexibility and robustness. However, there is no "perfect" clustering approach outperforming its counterparts, and it is hard to evaluate and combine the results from different techniques, especially in a field without much prior knowledge, such as bioinformatics. This paper proposes a meta-clustering approach to extract the information from results of different clustering techniques, so that a better interpretation of the data distribution can be obtained. A special distance measure is defined to represent the statistical "signal" of each cluster produced by various clustering techniques. The algorithm is applied on both artificial and real data Simulations show that the proposed approach is able to extract the information efficiently and accurately from the input clustering structure.

Algorithms↗
PubMed2006

Gene expression patterns in gastric cancer.

OBJECTIVE: To identify gene expression patterns in distinct stages of intestinal-type gastric cancer(GC). METHODS: Gene expression patterns of distinct stages of intestinal-type GC samples from 3 patients were compared with cDNA microarray, which contained 576 genes. There were 506 target genes, which included 51 genes identified from our previous experiment with suppression subtractive hybridization(SSH) and other 455 genes chosen for their important roles in cancers. Hierarchical clustering was performed to clarify genes in association with distinct stages of GC. RESULTS: One hundred and eighty-one differentially expressed genes with average Cy5:Cy3 ratios higher than 2.0 or lower than 0.5 in at least one stage of GC were identified by cDNA microarray. Among them, 48 genes were up-regulated and 133 down-regulated. Hierarchical clustering analysis separated the differentially expressed genes in different stages of GC into 5 main characteristic groups. Some important differentially expressed genes in different stages of GC were identified, such as SEC23IP, LIPF, ES(BQ291520), SLC5A1, PG(encoding similar to pepsin A precursor), CXCR4, DICER1, SH3GL2, and IGF2R. CONCLUSION: The differentially expressed gene patterns and some important genes were identified, which might be useful in further study on carcinogenesis, progression and metastasis of intestinal-type GC.

DNA, Neoplasm↗