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Banerjee I. Analysis of regulatory network involved in mechanical induction of embryonic stem cell differentiation. PLoS One 2012; 7:e35700; PMID:22558203; http://dx.doi
"... Embryonic stem cells are conventionally differentiated by modulating specific growth factors in the cell culture media. Recently the effect of cellular mechanical microenvironment in inducing phenotype specific differentiation has attracted considerable attention. We have shown the possibility of in ..."
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Embryonic stem cells are conventionally differentiated by modulating specific growth factors in the cell culture media. Recently the effect of cellular mechanical microenvironment in inducing phenotype specific differentiation has attracted considerable attention. We have shown the possibility of inducing endoderm differentiation by culturing the stem cells on fibrin substrates of specific stiffness [1]. Here, we analyze the regulatory network involved in such mechanically induced endoderm differentiation under two different experimental configurations of 2-dimensional and 3-dimensional culture, respectively. Mouse embryonic stem cells are differentiated on an array of substrates of varying mechanical properties and analyzed for relevant endoderm markers. The experimental data set is further analyzed for identification of co-regulated transcription factors across different substrate conditions using the technique of bi-clustering. Overlapped bi-clusters are identified following an optimization formulation, which is solved using an evolutionary algorithm. While typically such analysis is performed at the mean value of expression data across experimental repeats, the variability of stem cell systems reduces the confidence on such analysis of mean data. Bootstrapping technique is thus integrated with the bi-clustering algorithm to determine sets of robust bi-clusters, which is found to differ significantly from corresponding bi-clusters at the mean data value. Analysis of robust bi-clusters reveals an overall similar network interaction as has been reported for chemically induced endoderm or endodermal organs but with differences in patterning between 2-dimensional and 3-
Sparse Biclustering of Transposable Data
, 2013
"... We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. ..."
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We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for biclustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression data set. This article has supplementary material online.
Simultaneous clustering: A survey
- 4th International Conference on Pattern Recognition and Machine Intelligence
, 2011
"... Abstract. Although most of the clustering literature focuses on onesided clustering algorithms, simultaneous clustering has recently gained attention as a powerful tool that allows to circumvent some limitations of classical clustering approach. Simultaneous clustering methods perform clustering in ..."
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Abstract. Although most of the clustering literature focuses on onesided clustering algorithms, simultaneous clustering has recently gained attention as a powerful tool that allows to circumvent some limitations of classical clustering approach. Simultaneous clustering methods perform clustering in the two dimensions simultaneously. In this paper, we introduce a large number of existing simultaneous clustering approaches applied in bioinformatics as well as in text mining, web mining and information retrieval and classify them in accordance with the methods used to perform the clustering and the target applications.
C.: F2g: Efficient discovery of full-patterns
- In: ECML/PKDD IW on New Frontiers to Mine Complex Patterns. Springer-Verlag
, 2013
"... Abstract. An increasing number of biomedical tasks, such as pattern-based biclustering, require the disclosure of the transactions (e.g. genes) that support each pattern (e.g. expression profiles). The discovery of pat-terns with their supporting transactions, referred as full-pattern mining, has be ..."
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Abstract. An increasing number of biomedical tasks, such as pattern-based biclustering, require the disclosure of the transactions (e.g. genes) that support each pattern (e.g. expression profiles). The discovery of pat-terns with their supporting transactions, referred as full-pattern mining, has been solved recurring to extensions over Apriori and vertical-based algorithms for frequent itemset mining. Although pattern-growth alter-natives are known to be more efficient across multiple biological datasets, there are not yet adaptations for the efficient delivery of full-patterns. In this paper, we propose a pattern-growth algorithm able to discover full-patterns with heightened efficiency and minimum memory overhead. Results confirm that for dense datasets or low support thresholds, a common requirement in biomedical settings, this method can achieve significant performance improvements against its peers. 1
Biclustering Methods: Biological Relevance and Application in Gene Expression Analysis
"... DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical appr ..."
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DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering) methods where genes (or respectively samples) are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes). An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or medical implications. In this study we evaluate 13 biclustering and 2 clustering (k-means and hierarchical) methods. We use several approaches to compare their performance on two real gene expression data sets. For this purpose we apply four evaluation measures in our analysis: (1) we examine how well the considered (bi)clustering methods differentiate various sample types; (2) we evaluate how well the groups of genes discovered by the (bi)clustering methods are annotated with similar Gene Ontology categories; (3) we evaluate the capability of the methods to differentiate genes that are known to be specific to the particular sample types we study and (4) we compare the running time of the algorithms. In the end, we conclude that as long as the samples are well defined and annotated, the contamination of the samples is limited, and the samples are well replicated, biclustering methods such
Suppressed Expression of T-Box Transcription Factors Is Involved in Senescence in Chronic Obstructive Pulmonary Disease
"... Chronic obstructive pulmonary disease (COPD) is a major global health problem. The etiology of COPD has been associated with apoptosis, oxidative stress, and inflammation. However, understanding of the molecular interactions that modulate COPD pathogenesis remains only partly resolved. We conducted ..."
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Chronic obstructive pulmonary disease (COPD) is a major global health problem. The etiology of COPD has been associated with apoptosis, oxidative stress, and inflammation. However, understanding of the molecular interactions that modulate COPD pathogenesis remains only partly resolved. We conducted an exploratory study on COPD etiology to identify the key molecular participants. We used information-theoretic algorithms including Context Likelihood of Relatedness (CLR), Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Inferelator. We captured direct functional associations among genes, given a compendium of gene expression profiles of human lung epithelial cells. A set of genes differentially expressed in COPD, as reported in a previous study were superposed with the resulting transcriptional regulatory networks. After factoring in the properties of the networks, an established COPD susceptibility locus and domain-domain interactions involving protein products of genes in the generated networks, several molecular candidates were predicted to be involved in the etiology of COPD. These include COL4A3, CFLAR, GULP1, PDCD1, CASP10, PAX3, BOK, HSPD1, PITX2, and PML. Furthermore, T-box (TBX) genes and cyclin-dependent kinase inhibitor 2A (CDKN2A), which are in a direct transcriptional regulatory relationship, emerged as preeminent participants in the etiology of COPD by means of senescence. Contrary to observations in neoplasms, our study reveals that the expression of genes and proteins in the lung
Article Elucidating Polypharmacological Mechanisms of Polyphenols by Gene Module Profile Analysis
, 2014
"... Abstract: Due to the diverse medicinal effects, polyphenols are among the most intensively studied natural products. However, it is a great challenge to elucidate the polypharmacological mechanisms of polyphenols. To address this challenge, we establish a method for identifying multiple targets of c ..."
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Abstract: Due to the diverse medicinal effects, polyphenols are among the most intensively studied natural products. However, it is a great challenge to elucidate the polypharmacological mechanisms of polyphenols. To address this challenge, we establish a method for identifying multiple targets of chemical agents through analyzing the module profiles of gene expression upon chemical treatments. By using FABIA algorithm, we have performed a biclustering analysis of gene expression profiles derived from Connectivity Map (cMap), and clustered the profiles into 49 gene modules. This allowed us to define a 49 dimensional binary vector to characterize the gene module profiles, by which we can compare the expression profiles for each pair of chemical agents with Tanimoto coefficient. For the agent pairs with similar gene expression profiles, we can predict the target of one agent from the other. Drug target enrichment analysis indicated that this method is efficient to predict the multiple targets of chemical agents. By using this method, we identify 148 targets for 20 polyphenols derived from cMap. A large part of the targets are validated by experimental observations. The results show that the medicinal effects of polyphenols are far beyond their well-known antioxidant activities. This method is also applicable to dissect the polypharmacology of other natural products.
1 Systems biology Identification of Transcription Factors for Drug-Associated Gene Modules and Biomedical Implications
"... Motivation: One of the major findings in systems biomedicine is that both pathogenesis of diseases and drug mode of action (MoA) have a module basis. However, the transcription factors (TFs) regulating the modules remain largely unknown. Results: In this study, by using biclustering approach FABIA, ..."
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Motivation: One of the major findings in systems biomedicine is that both pathogenesis of diseases and drug mode of action (MoA) have a module basis. However, the transcription factors (TFs) regulating the modules remain largely unknown. Results: In this study, by using biclustering approach FABIA, we generate 49 modules for gene expression profiles upon 1309 agent treatments. These modules are of biological relevance in terms of functional enrichment, drug-drug interactions and 3D proximity in chromatins. By using the information of drug targets (some of which are TFs) and biological regulation, the links between 28 modules and 12 specific TFs, such as ERs, Nrf2 and PPARγ, can be estab-lished. Some of the links are supported by 3D transcriptional regula-tion data (derived from ChIA-PET experiments) and drug MoA as well. The relationships between modules and TFs provide new clues to interpreting biological regulation mechanisms, in particular the lipid metabolism regulation by ERα. In addition, the links between natural products (e.g., polyphenols), and their associated modules and TFs are helpful to elucidate their polypharmacological effects in terms of activating specific TFs, such as ERs, Nrf2 and PPARγ. Contact:
modules and biomedical implications
, 2013
"... Identification of transcription factors for drug-associated gene ..."
