Clustering of unevenly sampled gene expression time-series data

Möller-Levet, Carla S.; Klawonn, Frank; Cho, K. H.; Yin, Hujun

doi:10.1016/j.fss.2004.10.014

Cited by 57 publications

(47 citation statements)

References 22 publications

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“…In other words, DE serves as an optimization strategy that avoids FSTS being trapped in local optima by guiding it in a more promising search space, in which FSTS will approach fast the optimal solution. The FSTS method is a variant of the classic Fuzzy C-Means algorithm in the sense that it incorporates a similarity metric, Short Time Series (STS), which in turn manages to find the differences in the shapes, as defined by the relative change of expression and the corresponding temporal information, regardless of the difference in absolute values [25].…”

Section: Evolutionary Fuzzy Temporal Clustering Algorithm (Olympus)mentioning

confidence: 99%

“…Therefore, it is necessary to obtain prototypes that take into account the similar slopes according to temporal information. The prototypes are estimated after calculating the partial derivative of the fitness function and solve for k v after setting it equal to zero, as described in [25]:…”

Section: Algorithmic Stagesmentioning

confidence: 99%

“…Additionally, the Bayesian Information Criterion (BIC) was integrated into OLYMPUS in an attempt to determine the best number of clusters for a given data set [24]. The FSTS method revised the standard fuzzy c-means method by incorporating the Short Time Series (STS) distance into the equations for computing the membership matrix and the prototypes (or cluster centers), thus developed a fuzzy time series clustering algorithm [25]. Despite the fact that this method converges fast, it is easily trapped in local optima, since it is sensitive to initialization parameters.…”

Section: Introductionmentioning

confidence: 99%

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OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

Dimitrakopoulou

Vrahatis

Wilk

et al. 2013

Computer Methods and Programs in Biomedicine

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The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes. We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection. The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web

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Section: Evolutionary Fuzzy Temporal Clustering Algorithm (Olympus)mentioning

confidence: 99%

Section: Algorithmic Stagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

Dimitrakopoulou

Vrahatis

Wilk

et al. 2013

Computer Methods and Programs in Biomedicine

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“…The second type of problem under studying is to cluster N sequences into C groups [18] [5] [13]. This problem exists primarily in the area of bioinformatics, for example, clustering gene or protein sequences.…”

Section: Related Workmentioning

confidence: 99%

Mining Naturally Smooth Evolution of Clusters from Dynamic Data

Wang¹,

Liu²,

Feng³

et al. 2007

Proceedings of the 2007 SIAM International Conference on Data Mining

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Many clustering algorithms have been proposed to partition a set of static data points into groups. In this paper, we consider an evolutionary clustering problem where the input data points may move, disappeare, and emerge. Generally, these changes should result in a smooth evolution of the clusters. Mining this naturally smooth evolution is valuable for providing an aggregated view of the numerous individual behaviors.We solve this novel and generalized form of clustering problem by converting it into a Bayesian learning problem. Analogous to that the EM clustering algorithm clusters static data points by learning a Gaussian mixture model, our method mines the evolution of clusters from dynamic data points by learning a hidden semi-Markov model (HSMM). By utilizing characteristics of the evolutionary clustering problem, we derive a new unsupervised learning algorithm which is much more efficient than the algorithms used to learn traditional variable-duration HSMMs. Because the HSMM models the probabilistic relationship between the dynamic data set and corresponding evolving clusters, we can interpret the learned parameters as the evolving clusters intuitively using the Viterbi filtering technique. Because learning an HSMM is in fact learning an optimal Viterbi filter, the learned cluster evolutions are smooth and fit well with the data. We demonstrate the effectiveness of this method by experiments on both synthetic data and real data.

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“…While clustering temporal gene expression profiles was studied by identifying homogeneous clusters of genes in [10], the shapes of the curves were considered instead of the absolute expression ratios. Fuzzy clustering of gene temporal profiles, where the similarities between coexpressed genes are computed based on the rate of change of the expression ratios across time, has been studied in [11]. In [12], the idea of order-restricted inference levels across time was applied to select and cluster genes, where the estimation makes use of known inequalities among parameters.…”

Section: Introductionmentioning

confidence: 99%

New approaches to clustering microarray time-series data using multiple expression profile alignment

Subhani

Rueda

Ngom

et al. 2010

2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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Abstract-An important process in functional genomic studies is clustering microarray time-series data, where genes with similar expression profiles are expected to be functionally related. Clustering microarray time-series data via pairwise alignment of piecewise linear profiles has been recently introduced. In this paper, we propose a clustering approach based on a multiple profile alignment of natural cubic spline and piecewise linear representations of gene expression profiles. We combine these multiple alignment approaches with k-means. We ran our methods on a well-known data set of pre-clustered Saccharomyces cerevisiae gene expression profiles and a data set of 3315 Pseudomonas aeruginosa expression profiles. We assessed the validity of the resulting clusters and applied a c-nearest neighbor classifier for evaluating the performance of our approaches, obtaining accuracies of 89.51% and 86.12% respectively, on Saccharomyces cerevisiae data, and 90.90% and 93.71% accuracies for cubic spline and piecewise linear respectively on Pseudomonas aeruginosa data.

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Clustering of unevenly sampled gene expression time-series data

Cited by 57 publications

References 22 publications

OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

Mining Naturally Smooth Evolution of Clusters from Dynamic Data

New approaches to clustering microarray time-series data using multiple expression profile alignment

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