Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Oligonucleotide Array Sequence Analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,369 records · Page 76Linked to original sources
PubMed2004

Identifying time-lagged gene clusters using gene expression data.

MOTIVATION: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters. RESULTS: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters. AVAILABILITY: The software is available upon request. CONTACT: jiliping@comp.nus.edu.sg SUPPLEMENTARY INFORMATION: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.

Cluster Analysis↗
PubMed2004

Classification using partial least squares with penalized logistic regression.

MOTIVATION: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one. RESULTS: In this paper, the question of classification in such a high-dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining partial least squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate.

Algorithms↗
PubMed2005

ALOHOMORA: a tool for linkage analysis using 10K SNP array data.

SUMMARY: ALOHOMORA is a software tool designed to facilitate genome-wide linkage studies performed with high-density single nucleotide polymorphism (SNP) marker panels such as the Affymetrix GeneChip(R) Human Mapping 10K Array. Genotype data are converted into appropriate formats for a number of common linkage programs and subjected to standard quality control routines before linkage runs are started. ALOHOMORA is written in Perl and may be used to perform state-of-the-art linkage scans in small and large families with any genetic model. Options for using different genetic maps or ethnicity-specific allele frequencies are implemented. Graphic outputs of whole-genome multipoint LOD score values are provided for the entire dataset as well as for individual families. AVAILABILITY: ALOHOMORA is available free of charge for non-commercial research institutions. For more details, see http://gmc.mdc-berlin.de/alohomora/

Algorithms↗
PubMed2005

Differential gene expression detection and sample classification using penalized linear regression models.

Differential gene expression detection and sample classification using microarray data have received much research interest recently. Owing to the large number of genes p and small number of samples n (p >> n), microarray data analysis poses big challenges for statistical analysis. An obvious problem owing to the 'large p small n' is over-fitting. Just by chance, we are likely to find some non-differentially expressed genes that can classify the samples very well. The idea of shrinkage is to regularize the model parameters to reduce the effects of noise and produce reliable inferences. Shrinkage has been successfully applied in the microarray data analysis. The SAM statistics proposed by Tusher et al. and the 'nearest shrunken centroid' proposed by Tibshirani et al. are ad hoc shrinkage methods. Both methods are simple, intuitive and prove to be useful in empirical studies. Recently Wu proposed the penalized t/F-statistics with shrinkage by formally using the (1) penalized linear regression models for two-class microarray data, showing good performance. In this paper we systematically discussed the use of penalized regression models for analyzing microarray data. We generalize the two-class penalized t/F-statistics proposed by Wu to multi-class microarray data. We formally derive the ad hoc shrunken centroid used by Tibshirani et al. using the (1) penalized regression models. And we show that the penalized linear regression models provide a rigorous and unified statistical framework for sample classification and differential gene expression detection.

Algorithms↗
PubMed2006

Incorporating gene functions as priors in model-based clustering of microarray gene expression data.

MOTIVATION: Cluster analysis of gene expression profiles has been widely applied to clustering genes for gene function discovery. Many approaches have been proposed. The rationale is that the genes with the same biological function or involved in the same biological process are more likely to co-express, hence they are more likely to form a cluster with similar gene expression patterns. However, most existing methods, including model-based clustering, ignore known gene functions in clustering. RESULTS: To take advantage of accumulating gene functional annotations, we propose incorporating known gene functions as prior probabilities in model-based clustering. In contrast to a global mixture model applicable to all the genes in the standard model-based clustering, we use a stratified mixture model: one stratum corresponds to the genes of unknown function while each of the other ones corresponding to the genes sharing the same biological function or pathway; the genes from the same stratum are assumed to have the same prior probability of coming from a cluster while those from different strata are allowed to have different prior probabilities of coming from the same cluster. We derive a simple EM algorithm that can be used to fit the stratified model. A simulation study and an application to gene function prediction demonstrate the advantage of our proposal over the standard method. CONTACT: weip@biostat.umn.edu

Algorithms↗
PubMed2006

A systematic comparison and evaluation of biclustering methods for gene expression data.

MOTIVATION: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available. RESULTS: First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings.

Algorithms↗
PubMed2006

Towards clustering of incomplete microarray data without the use of imputation.

MOTIVATION: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process. RESULTS: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data. AVAILABILITY: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.

Algorithms↗
PubMed2006

Regularized linear discriminant analysis and its application in microarrays.

In this paper, we introduce a modified version of linear discriminant analysis, called the "shrunken centroids regularized discriminant analysis" (SCRDA). This method generalizes the idea of the "nearest shrunken centroids" (NSC) (Tibshirani and others, 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named "rda") is available on CRAN (http://www.r-project.org) for download and testing.

Computer Simulation↗
PubMed2005

Identification of gene expression signatures in autoimmune disease without the influence of familial resemblance.

Even though autoimmune diseases are heterogeneous, believed to result from the interaction between genetic and environmental components, patients with these disorders exhibit reproducible patterns of gene expression in their peripheral blood mononuclear cells. A portion of this gene expression profile is a property of familial resemblance rather than autoimmune disease. Here, we wanted to identify the portion of this gene expression profile that is independent of familial resemblance and determine whether it is a product of disease duration, disease onset or other factors. By employing supervised clustering algorithms, we identified 100 genes whose expression profiles are shared in individuals with various autoimmune diseases but are not shared by unaffected family members of individuals with autoimmune disease or by controls. Individuals with early disease (1 year after onset) and established disease (10 years after onset) exhibit a near-identical expression pattern, suggesting that this unique profile is a product of disease onset rather than disease duration.

Algorithms↗
PubMed2002

SNP typing by apyrase-mediated allele-specific primer extension on DNA microarrays.

This study reports the development of a microarray-based allele-specific extension method for typing of single nucleotide polymorphisms (SNPs). The use of allele-specific primers has been employed previously to identify single base variations but it is acknowledged that certain mismatches are not refractory to extension. Here we have overcome this limitation by introducing apyrase, a nucleotide-degrading enzyme, to the extension reaction. We have shown previously that DNA polymerases exhibit slower reaction kinetics when extending a mismatched primer compared with a matched primer. This kinetic difference is exploited in the apyrase-mediated allele-specific extension (AMASE) assay, allowing incorporation of nucleotides when the reaction kinetics are fast but degrading the nucleotides before extension when the reaction kinetics are slow. Here we show that five homozygous variants (14% of the total number of variants) that were incorrectly scored in the absence of apyrase were correctly typed when apyrase was included in the extension reaction. AMASE was performed in situ on the oligonucleotide microarrays using fluorescent nucleotides to type 10 SNPs and two indels in 17 individuals generating approximately 200 genotypes. Cluster analysis of these data shows three distinct clusters with clear-cut boundaries. We conclude that SNP typing on oligonucleotide microarrays by AMASE is an efficient, rapid and accurate technique for large-scale genotyping.

Alleles↗
PubMed2006

Background gene expression in rat kidney: influence of strain, gender, and diet.

In order to gain better insight into factors (strain, gender, and diet) influencing background variability in kidney gene expression, we examined the transcriptomes of male and female Crl:CD(SD)IGSBR (Sprague-Dawley [SD]) and CDF(Fischer 344)/CrlBR rats maintained for 19 days on three different diets (ad libitum [AL], diet restriction-75% of AL, and casein-based phytoestrogen-free diet). Kidney RNA was analyzed using Agilent Rat oligo microarrays (approximately 20,000 genes). Principal component analysis demonstrated that strain and gender have the most impact on the variability in gene expression, while diet had a lesser effect. The majority of the affected genes differed by a magnitude of four-fold or less between strains/gender, with some previously known to be sex-hormone regulated (SLC22A7 and SLC21A1). One gene of particular interest was ornithine decarboxylase, a significant marker of cell proliferation and tumor promotion, which was expressed at an 18-fold greater level in SD rats. Further analysis revealed that the difference in expression was due to the use of an alternate polyadenylation signal resulting in the production of two different sizes of transcripts. These results demonstrate that gender and strain have significant influence on gene expression which could be a confounder when comparing results, especially when it involves predictive fingerprint/patterns.

Animals↗
PubMed2003

Mapping genetic alterations in tumors with single nucleotide polymorphisms.

Single nucleotide polymorphisms are the most abundant genetic markers in the human genome whose analyses can be easily conducted on a large scale. Most established methods for high-throughput single nucleotide polymorphism analyses are qualitative and are not suitable for genetic analysis of archived tumor specimens, which have compromised tissue integrity and normal tissue contaminations. Recent studies have focused on the development of quantitative methods for single nucleotide polymorphism analyses that can tolerate such imperfections in archived tissues. These methods have been used to measure the rate of allelic imbalance in small adenocarcinomas and to reveal novel correlations between allelic imbalance and disease progression in colorectal cancer. Therefore, quantitative single nucleotide polymorphism analysis provides a powerful tool for the identification of novel tumor markers and for the characterization of genetic alterations in human tumors.

Allelic Imbalance↗
PubMed2004

Current applications of microarrays in head and neck cancer research.

OBJECTIVES/HYPOTHESIS: The objective was to introduce microarray technology and its applications in cancer research to the head and neck clinician. STUDY DESIGN: Literature review combined with methodology and examples from the authors' experiences with microarray analysis of tumors of the head and neck. METHODS: Search of literature and the authors' experience was made for technical details, alternative methods of data analysis, available bioinformatics tools, and applications of microarrays in cancer research. RESULTS: Microarrays allow the simultaneous analysis of the expression of thousands of genes. The use of a well-developed microarray study design leads to informative results. There are various bioinformatics resources widely available to aid in the analysis of microarray data. However, there is not yet a gold standard for analysis because this methodology is still evolving. CONCLUSION: Microarray studies may allow researchers to identify genetic changes relevant to diagnosis and prognosis in patients with head and neck cancer. Although still relatively new, this powerful methodology has immense potential to aid in understanding of the genetic changes that are important in head and neck cancer.

DNA Mutational Analysis↗
PubMed2005

Database of mRNA gene expression profiles of multiple human organs.

Genome-wide expression profiling of normal tissue may facilitate our understanding of the etiology of diseased organs and augment the development of new targeted therapeutics. Here, we have developed a high-density gene expression database of 18,927 unique genes for 158 normal human samples from 19 different organs of 30 different individuals using DNA microarrays. We report four main findings. First, despite very diverse sample parameters (e.g., age, ethnicity, sex, and postmortem interval), the expression profiles belonging to the same organs cluster together, demonstrating internal stability of the database. Second, the gene expression profiles reflect major organ-specific functions on the molecular level, indicating consistency of our database with known biology. Third, we demonstrate that any small (i.e., n approximately 100), randomly selected subset of genes can approximately reproduce the hierarchical clustering of the full data set, suggesting that the observed differential expression of >90% of the probed genes is of biological origin. Fourth, we demonstrate a potential application of this database to cancer research by identifying 19 tumor-specific genes in neuroblastoma. The selected genes are relatively underexpressed in all of the organs examined and belong to therapeutically relevant pathways, making them potential novel diagnostic markers and targets for therapy. We expect this database will be of utility for developing rationally designed molecularly targeted therapeutics in diseases such as cancer, as well as for exploring the functions of genes.

Cluster Analysis↗
PubMed2005

Robust and accurate cancer classification with gene expression profiling.

Robust and accurate cancer classification is critical in cancer treatment. Gene expression profiling is expected to enable us to diagnose tumors precisely and systematically. However, the classification task in this context is very challenging because of the curse of dimensionality and the small sample size problem. In this paper, we propose a novel method to solve these two problems. Our method is able to map gene expression data into a very low dimensional space and thus meets the recommended samples to features per class ratio. As a result, it can be used to classify new samples robustly with low and trustable (estimated) error rates. The method is based on linear discriminant analysis (LDA). However, the conventional LDA requires that the within-class scatter matrix S(w) be nonsingular. Unfortunately, Sw is always singular in the case of cancer classification due to the small sample size problem. To overcome this problem, we develop a generalized linear discriminant analysis (GLDA) that is a general, direct, and complete solution to optimize Fisher's criterion. GLDA is mathematically well-founded and coincides with the conventional LDA when S(w) is nonsingular. Different from the conventional LDA, GLDA does not assume the nonsingularity of S(w), and thus naturally solves the small sample size problem. To accommodate the high dimensionality of scatter matrices, a fast algorithm of GLDA is also developed. Our extensive experiments on seven public cancer datasets show that the method performs well. Especially on some difficult instances that have very small samples to genes per class ratios, our method achieves much higher accuracies than widely used classification methods such as support vector machines, random forests, etc.

Algorithms↗
PubMed2003

Intensity-based segmentation of microarray images.

The underlying principle in microarray image analysis is that the spot intensity is a measure of the gene expression. This implicitly assumes the gene expression of a spot to be governed entirely by the distribution of the pixel intensities. Thus, a segmentation technique based on the distribution of the pixel intensities is appropriate for the current problem. In this paper, clustering-based segmentation is described to extract the target intensity of the spots. The approximate boundaries of the spots in the microarray are determined by manual adjustment of rectilinear grids. The distribution of the pixel intensity in a grid containing a spot is assumed to be the superposition of the foreground and the local background. The k-means clustering technique and the partitioning around medoids (PAM) were used to generate a binary partition of the pixel intensity distribution. The median (k-means) and the medoid (PAM) of the cluster members are chosen as the cluster representatives. The effectiveness of the clustering-based segmentation techniques was tested on publicly available arrays generated in a lipid metabolism experiment (Callow et al., 2000). The results are compared against those obtained using the region-growing approach (SPOT) (Yang et al., 2001). The effect of additive white Gaussian noise is also investigated.

Algorithms↗
PubMed2002

DNASER I: layout and data analysis.

We present the DNA analyzer (DNASER), a novel bioinstrumentation for real-time acquisition and elaboration of images from fluorescent DNA microarrays. A white light beam illuminates the target sample allowing the images grabbing on a high sensibility and wide-band charge-coupled device camera (ORCA II-Hamamatsu). This high-performance device permits to acquire images faster and of higher quality than the traditional systems. The DNA microarrays images are processed to recognize the DNA chip spots, to analyze their superficial distribution on the glass slide and to evaluate their geometric and intensity properties. Differently form conventional techniques, the spots analysis is fully automated and the DNASER does not require any additional information about the DNA microarray geometry. The DNASER hardware and software architecture is illustrated. Preliminary results are shown from experiments performed on real DNA samples.

Algorithms↗
PubMed2006

From 'omes to biology.

Technologies that have emerged from the genome project have dramatically increased our ability to generate data on the way in which organisms respond to their environments, how they execute their programmes of development and growth, and how these are altered in the development of disease states. However, our ability to analyse these large datasets has not kept pace with our ability to generate them and consequently new strategies must be developed to address the issues associated with their analysis. One approach that we have employed quite successfully is to look at data from microarrays (or proteomics or metabolomics experiments) not as independent datasets, but rather as elements of a much larger body of biological information across various scales that must be integrated with, and interpreted within, the context of such ancillary data. Here we outline the general approach and provide three examples from published studies of the way in which we have applied this strategy.

Animals↗