Dataset Information

ARBic: an all-round biclustering algorithm for analyzing gene expression data.

ABSTRACT: Identifying significant biclusters of genes with specific expression patterns is an effective approach to reveal functionally correlated genes in gene expression data. However, none of existing algorithms can simultaneously identify both broader and narrower biclusters due to their failure of balancing between effectiveness and efficiency. We introduced ARBic, an algorithm which is capable of accurately identifying any significant biclusters of any shape, including broader, narrower and square, in any large scale gene expression dataset. ARBic was designed by integrating column-based and row-based strategies into a single biclustering procedure. The column-based strategy borrowed from RecBic, a recently published biclustering tool, extracts narrower biclusters, while the row-based strategy that iteratively finds the longest path in a specific directed graph, extracts broader ones. Being tested and compared to other seven salient biclustering algorithms on simulated datasets, ARBic achieves at least an average of 29% higher recovery, relevance and[Formula: see text] scores than the best existing tool. In addition, ARBic substantially outperforms all tools on real datasets and is more robust to noises, bicluster shapes and dataset types.

SUBMITTER: Liu X

PROVIDER: S-EPMC9887595 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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ARBic: an all-round biclustering algorithm for analyzing gene expression data.

Liu Xiangyu X Yu Ting T Zhao Xiaoyu X Long Chaoyi C Han Renmin R Su Zhengchang Z Li Guojun G

NAR genomics and bioinformatics 20230131 1

Identifying significant biclusters of genes with specific expression patterns is an effective approach to reveal functionally correlated genes in gene expression data. However, none of existing algorithms can simultaneously identify both broader and narrower biclusters due to their failure of balancing between effectiveness and efficiency. We introduced ARBic, an algorithm which is capable of accurately identifying any significant biclusters of any shape, including broader, narrower and square, ...[more]

PMID: 36733402

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Project description:Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

Project description:BackgroundThe DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only 1. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis.ResultsWe develop a novel and efficient biclustering algorithm which can be regarded as a greedy version of an existing algorithm known as pCluster algorithm. By relaxing the constraint in homogeneity, the proposed algorithm has polynomial-time complexity in the worst case instead of exponential-time complexity as in the pCluster algorithm. Experiments on artificial datasets verify that our algorithm can identify both additive-related and multiplicative-related biclusters in the presence of overlap and noise. Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations. Comparative study shows that the proposed approach outperforms several existing biclustering algorithms. We also provide an interactive exploratory tool based on PC plot visualization for determining the parameters of our biclustering algorithm.ConclusionWe have proposed a novel biclustering algorithm which works with PC plots for an interactive exploratory analysis of gene expression data. Experiments show that the biclustering algorithm is efficient and is capable of detecting co-regulated genes. The interactive analysis enables an optimum parameter determination in the biclustering algorithm so as to achieve the best result. In future, we will modify the proposed algorithm for other bicluster models such as the coherent evolution model.

Dataset Information

ARBic: an all-round biclustering algorithm for analyzing gene expression data.

Publications

ARBic: an all-round biclustering algorithm for analyzing gene expression data.

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