Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

Istepanian, R.S.H.; Sungoor, Ala; Nebel, Jean‐Christophe

doi:10.1109/tnb.2011.2178262

Cited by 12 publications

(4 citation statements)

References 35 publications

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“…The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods (Istepanian RS 2011 ).…”

Section: Genome Sequence Comparison With Practical Genomicmentioning

confidence: 73%

What can we learn with Signal Analysis about Genomic information?

Chaturvedi¹,

Tiwari²

2014

IOSRJPBS

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Section: Genome Sequence Comparison With Practical Genomicmentioning

confidence: 73%

What can we learn with Signal Analysis about Genomic information?

Chaturvedi¹,

Tiwari²

2014

IOSRJPBS

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“…Specifically, increasing gene number allows our algorithm to more accurately identify the noise cluster, thus separating the gene cluster(s) of interest more precisely. While our simulation studies demonstrate that our approach is able to both capture meaningful signals from very noisy data and group them very well, we cannot compare our method with existing methods [ 36 ] simply because no information about time is contained in the clusters obtained by other methods.…”

Section: Simulation Studiesmentioning

confidence: 99%

Dynamic Clustering of Gene Expression

Doerge

2012

ISRN Bioinformatics

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It is well accepted that genes are simultaneously involved in multiple biological processes and that genes are coordinated over the duration of such events. Unfortunately, clustering methodologies that group genes for the purpose of novel gene discovery fail to acknowledge the dynamic nature of biological processes and provide static clusters, even when the expression of genes is assessed across time or developmental stages. By taking advantage of techniques and theories from time frequency analysis, periodic gene expression profiles are dynamically clustered based on the assumption that different spectral frequencies characterize different biological processes. A two-step cluster validation approach is proposed to statistically estimate both the optimal number of clusters and to distinguish significant clusters from noise. The resulting clusters reveal coordinated coexpressed genes. This novel dynamic clustering approach has broad applicability to a vast range of sequential data scenarios where the order of the series is of interest.

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“…Among them hierarchical clustering analysis using FCOM, a 'combination function' data reduction and cluster analysis software from Winlist (Verity House Software) is the most common method as it allows for an easy visual representation of the data 20 . This hierarchical clustering analysis algorithms are similar to the software tools used in a wide diversity of gene expression studies [21][22][23] . Cells analysed by polychromatic flow cytometry are divided into multiple subpopulations, the number of cells within each subphenotype is provided as a relative frequency of the total cell population of interest (Fig.…”

Section: Data Interpretationmentioning

confidence: 99%

Data analysis and presentation in flow cytometry

Khowawisetsut¹,

Sukapirom²,

Pattanapanyasat³

2018

ScienceAsia

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The advancement of flow cytometry technology together with a series of novel developments in hardware and software have facilitated both phenotypic and functional characterizations of different cell types. In addition, the availability of many monoclonal antibodies and an expanding range of dye-chemistry have made multi-parameter flow cytometry possible for simultaneous measurements of large numbers of cells with better information of complex cellular networks such as the immune system. Although it has the advantage of being a fast, objective and quantitative, but running polychromatic flow cytometry is a complex process with many challenges particularly in the data analysis. The purpose of this communication is to describe several types of presentation and analysis of both univariate and multivariate datasets.

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Comparative Analysis of Genomic Signal Processing for Microarray Data Clustering

Cited by 12 publications

References 35 publications

What can we learn with Signal Analysis about Genomic information?

What can we learn with Signal Analysis about Genomic information?

Dynamic Clustering of Gene Expression

Data analysis and presentation in flow cytometry

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