J Biomed Inform - Average correlation clustering algorithm (ACCA) for grouping of co-regulated genes with similar pattern of variation in their expression values.

Tópicos

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Resumo

Distance based clustering algorithms can group genes that show similar expression values under multiple experimental conditions. They are unable to identify a group of genes that have similar pattern of variation in their expression values. Previously we developed an algorithm called divisive correlation clustering algorithm (DCCA) to tackle this situation, which is based on the concept of correlation clustering. But this algorithm may also fail for certain cases. In order to overcome these situations, we propose a new clustering algorithm, called average correlation clustering algorithm (ACCA), which is able to produce better clustering solution than that produced by some others. ACCA is able to find groups of genes having more common transcription factors and similar pattern of variation in their expression values. Moreover, ACCA is more efficient than DCCA with respect to the time of execution. Like DCCA, we use the concept of correlation clustering concept introduced by Bansal et al. ACCA uses the correlation matrix in such a way that all genes in a cluster have the highest average correlation values with the genes in that cluster. We have applied ACCA and some well-known conventional methods including DCCA to two artificial and nine gene expression datasets, and compared the performance of the algorithms. The clustering results of ACCA are found to be more significantly relevant to the biological annotations than those of the other methods. Analysis of the results show the superiority of ACCA over some others in determining a group of genes having more common transcription factors and with similar pattern of variation in their expression profiles. Availability of the software: The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software.

Resumo Limpo

distanc base cluster algorithm can group gene show similar express valu multipl experiment condit unabl identifi group gene similar pattern variat express valu previous develop algorithm call divis correl cluster algorithm dcca tackl situat base concept correl cluster algorithm may also fail certain case order overcom situat propos new cluster algorithm call averag correl cluster algorithm acca abl produc better cluster solut produc other acca abl find group gene common transcript factor similar pattern variat express valu moreov acca effici dcca respect time execut like dcca use concept correl cluster concept introduc bansal et al acca use correl matrix way gene cluster highest averag correl valu gene cluster appli acca wellknown convent method includ dcca two artifici nine gene express dataset compar perform algorithm cluster result acca found signific relev biolog annot method analysi result show superior acca other determin group gene common transcript factor similar pattern variat express profil avail softwar softwar develop use c visual basic languag can execut microsoft window platform softwar may download zip file httpwwwisicalacinrajat need instal two word file includ zip file need consult instal execut softwar

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