Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

Background  

Time-course microarray experiments produce vector gene expression profiles across a series of time points. Clustering genes based on these profiles is important in discovering functional related and co-regulated genes. Early developed clustering algorithms do not take advantage of the ordering in a time-course study, explicit use of which should allow more sensitive detection of genes that display a consistent pattern over time. Peddada et al. [1] proposed a clustering algorithm that can incorporate the temporal ordering using order-restricted statistical inference. This algorithm is, however, very time-consuming and hence inapplicable to most microarray experiments that contain a large number of genes. Its computational burden also imposes difficulty to assess the clustering reliability, which is a very important measure when clustering noisy microarray data.     

The clustering of regression models method with applications in gene expression data

Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, many "per-gene" analytic methods have been proposed. These methods can generally be characterized as using a regression function to independently model the observations for each gene; various adjustments for multiplicity are then used to interpret the statistical significance of these per-gene regression models over the collection of genes analyzed. Motivated by this common structure of per-gene models, we proposed a new model-based clustering method--the clustering of regression models method, which groups genes that share a similar relationship to the covariate(s). This method provides a unified approach for a family of clustering procedures and can be applied for data collected with various experimental designs. In addition, when combined with per-gene methods for assessing differential expression that employ the same regression modeling structure, an integrated framework for the analysis of microarray data is obtained. The proposed methodology was applied to two microarray data sets, one from a breast cancer study and the other from a yeast cell cycle study.     

EDISA: extracting biclusters from multiple time-series of gene expression profiles

Background  

Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets.     

Comparison of Gene Expression Profiles of Fenneropenaeus chinensis Challenged with WSSV and Vibrio

Microarray technique was used to analyze the gene expression profiles of shrimp when they were challenged by WSSV and heat-inactivated Vibrio anguillarum, respectively. At 6 h post challenge (HPC), a total of 806 clones showed differential expression profile in WSSV-challenged samples, but not in Vibrio-challenged samples. The genes coding energy metabolism enzyme and structure protein were the most downregulated elements in 6 h post WSSV-challenged (HPC-WSSV) tissues. However, a total of 155 clones showed differential expression in the Vibrio-challenged samples, but not in WSSV-challenged samples. Serine-type endopeptidase and lysosome-related genes were the most upregulated elements in tissues 6 h post Vibrio challenge (HPC-Vibrio). Totally, 188 clones showed differential expression in both 6 and 12 HPC-WSSV and HPC-Vibrio samples. Most of the differentially expressed genes (185/188) were downregulated in the samples of 12 HPC-WSSV, whereas upregulated in the samples at 6 and 12 HPC-Vibrio and 6 HPC-WSSV. The expression profiles of three differentially expressed genes identified in microarray hybridization were analyzed in hemocytes, lymphoid organ, and hepatopancreas of shrimp challenged by WSSV or Vibrio through real-time PCR. The results further confirmed the microarray hybridization results. The data will provide great help for us in understanding the immune mechanism of shrimp responding to WSSV or Vibrio. Wang and Li contributed equally to this work.     

Microarray data integration for genome-wide analysis of human tissue-selective gene expression

Background

Microarray gene expression data are accumulating in public databases. The expression profiles contain valuable information for understanding human gene expression patterns. However, the effective use of public microarray data requires integrating the expression profiles from heterogeneous sources.

Results

In this study, we have compiled a compendium of microarray expression profiles of various human tissue samples. The microarray raw data generated in different research laboratories have been obtained and combined into a single dataset after data normalization and transformation. To demonstrate the usefulness of the integrated microarray data for studying human gene expression patterns, we have analyzed the dataset to identify potential tissue-selective genes. A new method has been proposed for genome-wide identification of tissue-selective gene targets using both microarray intensity values and detection calls. The candidate genes for brain, liver and testis-selective expression have been examined, and the results suggest that our approach can select some interesting gene targets for further experimental studies.

Conclusion

A computational approach has been developed in this study for combining microarray expression profiles from heterogeneous sources. The integrated microarray data can be used to investigate tissue-selective expression patterns of human genes.

     

Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments

Background  

The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating variability of gene expression measurements in microarray experiments is essential for correctly identifying differentially expressed genes. Several recently developed methods for testing differential expression of genes utilize hierarchical Bayesian models to "pool" information from multiple genes. We have developed a statistical testing procedure that further improves upon current methods by incorporating the well-documented relationship between the absolute gene expression level and the variance of gene expression measurements into the general empirical Bayes framework.     

Clustering gene expression data with a penalized graph-based metric

Background  

The search for cluster structure in microarray datasets is a base problem for the so-called "-omic sciences". A difficult problem in clustering is how to handle data with a manifold structure, i.e. data that is not shaped in the form of compact clouds of points, forming arbitrary shapes or paths embedded in a high-dimensional space, as could be the case of some gene expression datasets.     

Microarray analysis of relative gene expression stability for selection of internal reference genes in the rhesus macaque brain

Background  

Normalization of gene expression data refers to the comparison of expression values using reference standards that are consistent across all conditions of an experiment. In PCR studies, genes designated as "housekeeping genes" have been used as internal reference genes under the assumption that their expression is stable and independent of experimental conditions. However, verification of this assumption is rarely performed. Here we assess the use of gene microarray analysis to facilitate selection of internal reference sequences with higher expression stability across experimental conditions than can be expected using traditional selection methods.     

Hepatic Gene Expression in Morbidly Obese Women: Implications for Disease Susceptibility

The objective of this study was to determine the molecular bases of disordered hepatic function and disease susceptibility in obesity. We compared global gene expression in liver biopsies from morbidly obese (MO) women undergoing gastric bypass (GBP) surgery with that of women undergoing ventral hernia repair who had experienced massive weight loss (MWL) following prior GBP. Metabolic and hormonal profiles were examined in MO vs. MWL groups. Additionally, we analyzed individual profiles of hepatic gene expression in liver biopsy specimens obtained from MO and MWL subjects. All patients underwent preoperative metabolic profiling. RNAs were extracted from wedge biopsies of livers from MO and MWL subjects, and analysis of mRNA expression was carried out using Affymetrix HG‐U133A microarray gene chips. Genes exhibiting greater than twofold differential expression between MO and MWL subjects were organized according to gene ontology and hierarchical clustering, and expression of key genes exhibiting differential regulation was quantified by real‐time–polymerase chain reaction (RT‐PCR). We discovered 154 genes to be differentially expressed in livers of MWL and MO subjects. A total of 28 candidate disease susceptibility genes were identified that encoded proteins regulating lipid and energy homeostasis (PLIN, ENO3, ELOVL2, APOF, LEPR, IGFBP1, DDIT4), signal transduction (MAP2K6, SOCS‐2), postinflammatory tissue repair (HLA‐DQB1, SPP1, P4HA1, LUM), bile acid transport (SULT2A, ABCB11), and metabolism of xenobiotics (GSTT2, CYP1A1). Using gene expression profiling, we have identified novel candidate disease susceptibility genes whose expression is altered in livers of MO subjects. The significance of altered expression of these genes to obesity‐related disease is discussed.     

h-Profile plots for the discovery and exploration of patterns in gene expression data with an application to time course data

Background  

An ever increasing number of techniques are being used to find genes with similar profiles from microarray studies. Visualization of gene expression profiles can aid this process, potentially contributing to the identification of co-regulated genes and gene function as well as network development.     

Identification of nutrient partitioning genes participating in rice grain filling by singular value decomposition (SVD) of genome expression data

Background

In order to identify rice genes involved in nutrient partitioning, microarray experiments have been done to quantify genomic scale gene expression. Genes involved in nutrient partitioning, specifically grain filling, will be used to identify other co-regulated genes, and DNA binding proteins. Proper identification of the initial set of bait genes used for further investigation is critical. Hierarchical clustering is useful for grouping genes with similar expression profiles, but decreases in utility as data complexity and systematic noise increases. Also, its rigid classification of genes is not consistent with our belief that some genes exhibit multifaceted, context dependent regulation.

Results

Singular value decomposition (SVD) of microarray data was investigated as a method to complement current techniques for gene expression pattern recognition. SVD's usefulness, in finding likely participants in grain filling, was measured by comparison with results obtained previously via clustering. 84 percent of these known grain-filling genes were re-identified after detailed SVD analysis. An additional set of 28 genes exhibited a stronger grain-filling pattern than those grain-filling genes that were unselected. They also had upstream sequence containing motifs over-represented among grain filling genes.

Conclusions

The pattern-based perspective that SVD provides complements to widely used clustering methods. The singular vectors provide information about patterns that exist in the data. Other aspects of the decomposition indicate the extent to which a gene exhibits a pattern similar to those provided by the singular vectors. Thus, once a set of interesting patterns has been identified, genes can be ranked by their relationship with said patterns.

     

FLAME,a novel fuzzy clustering method for the analysis of DNA microarray data

Background  

Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing clustering approaches, mainly developed in computer science, have been adapted to microarray data analysis. However, previous studies revealed that microarray datasets have very diverse structures, some of which may not be correctly captured by current clustering methods. We therefore approached the problem from a new starting point, and developed a clustering algorithm designed to capture dataset-specific structures at the beginning of the process.     

Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes

Background  

A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species.     

Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study

Background  

A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reduction of raw expression data and the feature selections necessary for, for example, classification of disease samples remains a challenge. To solve the problem we propose a two-level analysis. First self-organizing map (SOM) is used. SOM is a vector quantization method that simplifies and reduces the dimensionality of original measurements and visualizes individual tumor sample in a SOM component plane. Next, hierarchical clustering and K-means clustering is used to identify patterns of gene expression useful for classification of samples.