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

Sharan, Roded; Maron‐Katz, Adi; Shamir, Ron

doi:10.1093/bioinformatics/btg232

Cited by 339 publications

(235 citation statements)

References 44 publications

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“…Raw data were normalized and quantified using dCHIP (Li and Hung Wong 2001). Clustering into self-organizing maps was performed using Expander (Sharan et al 2003a).…”

Section: Gene Expression Profilingmentioning

confidence: 99%

An initial blueprint for myogenic differentiation

Blais¹,

Tsikitis²,

et al. 2005

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We have combined genome-wide transcription factor binding and expression profiling to assemble a regulatory network controlling the myogenic differentiation program in mammalian cells. We identified a cadre of overlapping and distinct targets of the key myogenic regulatory factors (MRFs)-MyoD and myogenin-and Myocyte Enhancer Factor 2 (MEF2). We discovered that MRFs and MEF2 regulate a remarkably extensive array of transcription factor genes that propagate and amplify the signals initiated by MRFs. We found that MRFs play an unexpectedly wide-ranging role in directing the assembly and usage of the neuromuscular junction. Interestingly, these factors also prepare myoblasts to respond to diverse types of stress. Computational analyses identified novel combinations of factors that, depending on the differentiation state, might collaborate with MRFs. Our studies suggest unanticipated biological insights into muscle development and highlight new directions for further studies of genes involved in muscle repair and responses to stress and damage.[Keywords: ChIP-on-chip; myogenesis; transcriptional regulation network] Supplemental material is available at http://www.genesdev.org.

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“…Raw data were normalized and quantified using dCHIP (Li and Hung Wong 2001). Clustering into self-organizing maps was performed using Expander (Sharan et al 2003a).…”

Section: Gene Expression Profilingmentioning

confidence: 99%

An initial blueprint for myogenic differentiation

Blais¹,

Tsikitis²,

et al. 2005

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“…Clustering is an optimization problem having applications in many fields ranging from bioinformatics [16,1] to image processing [19], with various algorithmic approaches available [15]. The general idea of clustering is to partition a set of data items into subsets, called clusters, in such a way that highly similar items belong to the same cluster and items having low similarity belong to different clusters.…”

Section: Introductionmentioning

confidence: 99%

Generalized Graph Clustering: Recognizing (p,q)-Cluster Graphs

Heggernes

Lokshtanov

Nederlof

et al. 2010

Lecture Notes in Computer Science

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Cluster Editing is a classical graph theoretic approach to tackle the problem of data set clustering: it consists of modifying a similarity graph into a disjoint union of cliques, i.e, clusters. As pointed out in a number of recent papers, the cluster editing model is too rigid to capture common features of real data sets. Several generalizations have thereby been proposed. In this paper, we introduce (p, q)-cluster graphs, where each cluster misses at most p edges to be a clique, and there are at most q edges between a cluster and other clusters. Our generalization is the first one that allows a large number of false positives and negatives in total, while bounding the number of these locally for each cluster by p and q. We show that recognizing (p, q)-cluster graphs is NP-complete when p and q are input. On the positive side, we show that (0, q)-cluster, (p, 1)-cluster, (p, 2)-cluster, and (1, 3)-cluster graphs can be recognized in polynomial time.

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“…The first three algorithms have been reported to be the best among the six biclustering methods [23]. ISA and BiMax are implemented by the software BicAT developed by [23], SAMBA is implemented by EX-PANDER [25], and BND is provided by PROXIMUS [19]. Figure 2 and Figure 3 present the results on synthetic datasets.…”

Section: Results Analysismentioning

confidence: 99%