Mining co-regulated gene profiles for the detection of functional associations in gene expression data

MOTIVATION: Association pattern discovery (APD) methods have been successfully applied to gene expression data. They find groups of co-regulated genes in which the genes are either up- or down-regulated throughout the identified conditions. These methods, however, fail to identify similarly expressed genes whose expressions change between up- and down-regulation from one condition to another. In order to discover these hidden patterns, we propose the concept of mining co-regulated gene profiles. Co-regulated gene profiles contain two gene sets such that genes within the same set behave identically (up or down) while genes from different sets display contrary behavior. To reduce and group the large number of similar resulting patterns, we propose a new similarity measure that can be applied together with hierarchical clustering methods. RESULTS: We tested our proposed method on two well-known yeast microarray data sets. Our implementation mined the data effectively and discovered patterns of co-regulated genes that are hidden to traditional APD methods. The high content of biologically relevant information in these patterns is demonstrated by the significant enrichment of co-regulated genes with similar functions. Our experimental results show that the Mining Attribute Profile (MAP) method is an efficient tool for the analysis of gene expression data and competitive with bi-clustering techniques.     

The correlation of gene expression and co-regulated gene patterns in characteristic KEGG pathways

There is great interest in chromosome- and pathway-based techniques for genomics data analysis in the current work in order to understand the mechanism of disease. However, there are few studies addressing the abilities of machine learning methods in incorporating pathway information for analyzing microarray data. In this paper, we identified the characteristic pathways by combining the classification error rates of out-of-bag (OOB) in random forests with pathways information. At each characteristic pathway, the correlation of gene expression was studied and the co-regulated gene patterns in different biological conditions were mined by Mining Attribute Profile (MAP) algorithm. The discovered co-regulated gene patterns were clustered by the average-linkage hierarchical clustering technique. The results showed that the expression of genes at the same characteristic pathway were approximate. Furthermore, two characteristic pathways were discovered to present co-regulated gene patterns in which one contained 108 patterns and the other contained one pattern. The results of cluster analysis showed that the smallest similarity coefficient of clusters was more than 0.623, which indicated that the co-regulated patterns in different biological conditions were more approximate at the same characteristic pathway. The methods discussed in this paper can provide additional insight into the study of microarray data.     

Identification and molecular characterization of novel anther-specific genes in Oryza sativa L. by using cDNA microarray

The complicated genetic pathway regulates the developmental programs of male reproductive organ, anther tissues. To understand these molecular mechanisms, we performed cDNA microarray analyses and in situ hybridization to monitor gene expression patterns during anther development in rice. Microarray analysis of 4,304 cDNA clones revealed that the hybridization signal of 396 cDNA clones (271 non-redundant groups) increased more than six-fold in every stage of the anthers compared with that of leaves. Cluster analysis with the expression data showed that 259 cDNA clones (156 non redundant groups) were specifically or predominantly expressed in anther tissues and were regulated by developmental stage-specific manners in the anther tissues. These co-regulated genes would be important for development of functional anther tissues. Furthermore, we selected several clones for RNA in situ hybridization analysis. From these analyses, we found several novel genes that show temporal and spatial expression patterns during anther development in addition to anther-specific genes reported so far. These results indicate that the genes identified in this experiment are controlled by different programs and are specialized in their developmental and cell types.     

Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes

Background  

A common observation in the analysis of gene expression data is that many genes display similarity in their expression patterns and therefore appear to be co-regulated. However, the variation associated with microarray data and the complexity of the experimental designs make the acquisition of co-expressed genes a challenge. We developed a novel method for Extracting microarray gene expression Patterns and Identifying co-expressed Genes, designated as EPIG. The approach utilizes the underlying structure of gene expression data to extract patterns and identify co-expressed genes that are responsive to experimental conditions.     

Spearman Correlation Identifies Statistically Significant Gene Expression Clusters in Spinal Cord Development and Injury

An important problem in the analysis of large-scale gene expression data is the validation of gene expression clusters. By examining the temporal expression patterns of 74 genes expressed in rat spinal cord under three different experimental conditions, we have found evidence that some genes cluster together under multiple conditions. Using RT-PCR data from spinal cord development and two sets of microarray data from spinal injury, we applied Spearman correlation to identify clusters and to assign P values to pairs of genes with highly similar temporal expression patterns. We found that 15% of genes occurred in statistically significant pairs in all three experimental conditions, providing both statistical and experimental support for the idea that genes that cluster together are co-regulated. In addition, we demonstrated that DNA microarray and RT-PCR data are comparable, and can be combined to confirm gene expression relationships.     

Motif-guided sparse decomposition of gene expression data for regulatory module identification

Background  

Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.     

Enhanced accumulation of Cd2+ by a Mesorhizobium sp. transformed with a gene from Arabidopsis thaliana coding for phytochelatin synthase

We expressed the Arabidopsis thaliana gene for phytochelatin synthase (PCS(At)) in Mesorhizobium huakuii subsp. rengei B3, a microsymbiont of Astragalus sinicus, a legume used as manure. The PCS(At) gene was expressed under the control of the nifH promoter, which regulates the nodule-specific expression of the nifH gene. The expression of the PCS(At) gene was demonstrated in free-living cells under low-oxygen conditions. Phytochelatin synthase (PCS) was expressed and catalyzed the synthesis of phytochelatins [(gamma-Glu-Cys)(n)-Gly; PCs] in strain B3. A range of PCs, with values of n from 2 to 7, was synthesized by cells that expressed the PCS(At) gene, whereas no PCs were found in control cells that harbored the empty plasmid. The presence of CdCl(2) activated PCS and induced the synthesis of substantial amounts of PCs. Cells that contained PCs accumulated 36 nmol of Cd(2+)/mg (dry weight) of cells. The expression of the PCS(At) gene in M. huakuii subsp. rengei B3 increased the ability of cells to bind Cd(2+) approximately 9- to 19-fold. The PCS protein was detected by immunostaining bacteroids of mature nodules of A. sinicus containing the PCS(At) gene. When recombinant M. huakuii subsp. rengei B3 established the symbiotic relationship with A. sinicus, the symbionts increased Cd(2+) accumulation in nodules 1.5-fold.     

CLICK and EXPANDER: a system for clustering and visualizing gene expression data

MOTIVATION: Microarrays have become a central tool in biological research. Their applications range from functional annotation to tissue classification and genetic network inference. A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering genes based on their expression patterns. RESULTS: We present a novel clustering algorithm, called CLICK, and its applications to gene expression analysis. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups (kernels) of highly similar elements, which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clusters. We report on the application of CLICK to a variety of gene expression data sets. In all those applications it outperformed extant algorithms according to several common figures of merit. We also point out that CLICK can be successfully used for the identification of common regulatory motifs in the upstream regions of co-regulated genes. Furthermore, we demonstrate how CLICK can be used to accurately classify tissue samples into disease types, based on their expression profiles. Finally, we present a new java-based graphical tool, called EXPANDER, for gene expression analysis and visualization, which incorporates CLICK and several other popular clustering algorithms. AVAILABILITY: http://www.cs.tau.ac.il/~rshamir/expander/expander.html