Clustering of high throughput gene expression data

Pirim, Harun; Ekşioğlu, Burak; Perkins, Andy D.; Yüceer, Çetin

doi:10.1016/j.cor.2012.03.008

Cited by 108 publications

(66 citation statements)

References 132 publications

(166 reference statements)

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“…Hierarchical cluster analysis 17 revealed that gene expression profile firstly distinguished patients' BM‐MSCs from normal BM‐MSCs. It then divided again by disease category (Figure 2A).…”

Section: Resultsmentioning

confidence: 99%

Common and different alterations of bone marrow mesenchymal stromal cells in myelodysplastic syndrome and multiple myeloma

et al. 2020

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Objective:The objective of this study was to explore characteristics of bone marrow mesenchymal stromal cells (BM-MSCs) derived from patients with myelodysplastic syndrome (MDS) and multiple myeloma (MM). Methods: BM-MSCs were recovered from 17 of MDS patients, 23 of MM patients and 9 healthy donors and were passaged until proliferation stopped. General characteristics and gene expression profiles of MSCs were analysed. In vitro, ex vivo coculture, immunohistochemistry and knockdown experiments were performed to verify gene expression changes. Results: BM-MSCs failed to culture in 35.0% of patients and 50.0% of recovered BM-MSCs stopped to proliferate before passage 6. MDS-and MM-MSCs shared characteristics including decreased osteogenesis, increased angiogenesis and senescence-associated molecular pathways. In vitro and ex vivo experiments showed disease-specific changes such as neurogenic tendency in MDS-MSCs and cardiomyogenic tendency in MM-MSCs. Although the age of normal control was younger than patients and telomere length was shorter in patient's BM-MSCs, they were not different according to disease category nor degree of proliferation. Specifically, poorly proliferation BM-MSCs showed CDKN2A overexpression and CXCL12 downregulation. Immunohistochemistry of BM biopsy demonstrated that CDKN2A was intensely accumulation in perivascular BM-MSCs failed to culture. Interestingly, patient's BM-MSCs revealed improved proliferation activity after CDKN2A knockdown. Conclusion: These results collectively indicate that MDS-MSCs and MM-MSCs have common and different alterations at various degrees. Hence, it is necessary to evaluate their alteration status using representative markers such as CDKN2A expression.

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Section: Resultsmentioning

confidence: 99%

Common and different alterations of bone marrow mesenchymal stromal cells in myelodysplastic syndrome and multiple myeloma

et al. 2020

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“…The application of data clustering for the classification of time series is usually reserved for gene expression data [Pirim et al, 2012, Liao, 2005. In this paper, we showed how useful clustering analysis can be as an exploratory tool of the parameter space of dynamical systems.…”

Section: Discussionmentioning

confidence: 99%

Mean field analysis of a spatial stochastic model of a gene regulatory network

et al. 2014

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A gene regulatory network may be defined as a collection of DNA segments which interact with each other indirectly through their RNA and protein products. Such a network is said to contain a negative feedback loop if its products inhibit gene transcription, and a positive feedback loop if a gene product promotes its own production. Negative feedback loops can create oscillations in mRNA and protein levels while positive feedback loops are primarily responsible for signal amplification. It is often the case in real biological systems that both negative and positive feedback loops operate in parameter regimes that result in low copy numbers of gene products.In this paper we investigate the spatio-temporal dynamics of a single feedback loop in a eukaryotic cell. We first develop a simplified spatial stochastic model of a canonical feedback system (either positive or negative). Using a Gillespie's algorithm, we compute sample trajectories and analyse their corresponding statistics. We then derive a system of equations that describe the spatio-temporal evolution of the stochastic means. Subsequently, we examine the spatially homogeneous case and compare the results of numerical simulations with the spatially explicit case. Finally, using a combination of steady-state analysis and data clustering techniques, we explore model behaviour across a subregion of the parameter space that is difficult to access experimentally and compare the parameter landscape of our spatio-temporal and spatially-homogeneous models.

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“…Clustering has been used in many areas of biological data analysis (Pirim et al 2012), the goal being to find structures in high-dimension data. Such structures are often multifaceted owing to the nature of the problem.…”

Section: Introductionmentioning

confidence: 99%

Graph-based unsupervised feature selection and multiview clustering for microarray data

Swarnkar

Mitra

2015

J Biosci

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A challenge in bioinformatics is to analyse volumes of gene expression data generated through microarray experiments and obtain useful information. Consequently, most microarray studies demand complex data analysis to infer biologically meaningful information from such high-throughput data. Selection of informative genes is an important data analysis step to identify a set of genes which can further help in finding the biological information embedded in microarray data, and thus assists in diagnosis, prognosis and treatment of the disease. In this article we present an unsupervised feature selection technique which attempts to address the goal of explorative data analysis, unfolding the multi-faceted nature of data. It focuses on extracting multiple clustering views considering the diversity of each view from high-dimensional data. We evaluated our technique on benchmark data sets and the experimental results indicates the potential and effectiveness of the proposed model in comparison to the traditional single view clustering models, as well as other existing methods used in the literature for the studied datasets.

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Clustering of high throughput gene expression data

Cited by 108 publications

References 132 publications

Common and different alterations of bone marrow mesenchymal stromal cells in myelodysplastic syndrome and multiple myeloma

Common and different alterations of bone marrow mesenchymal stromal cells in myelodysplastic syndrome and multiple myeloma

Mean field analysis of a spatial stochastic model of a gene regulatory network

Graph-based unsupervised feature selection and multiview clustering for microarray data

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