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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Published on: July 29, 2022

LCE: a link-based cluster ensemble method for improved gene expression data analysis.

Natthakan Iam-on1, Tossapon Boongoen, Simon Garrett

  • 1Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion, UK. nii07@aber.ac.uk

Bioinformatics (Oxford, England)
|May 7, 2010
PubMed
Summary
This summary is machine-generated.

The new link-based cluster ensemble (LCE) method improves gene expression data clustering by combining multiple analyses. LCE offers robust and efficient clustering, outperforming existing methods for microarray data analysis.

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Published on: September 18, 2021

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Mining

Background:

  • Selecting optimal clustering methods for gene expression data is challenging due to numerous possibilities.
  • Traditional methods like hierarchical clustering are often sub-optimal.
  • Cluster ensemble methods improve robustness and quality by combining multiple data partitions, but existing techniques neglect cluster-to-cluster relationships.

Purpose of the Study:

  • To introduce a novel cluster ensemble method that incorporates relationships among clusters.
  • To enhance the capability of ensemble methodologies for microarray data clustering.

Main Methods:

  • The study presents the link-based cluster ensemble (LCE) method.
  • LCE summarizes sample-cluster co-occurrence statistics and discovers relationships among clusters.
  • The method generates a high-level data matrix applicable to various numerical clustering techniques.

Main Results:

  • LCE demonstrates outstanding performance, outperforming existing cluster ensemble algorithms.
  • The method shows excellent and robust performance across diverse datasets, including noisy and imbalanced data.
  • LCE is computationally efficient for large datasets and gene clustering.

Conclusions:

  • The link-based cluster ensemble (LCE) method significantly advances microarray data clustering.
  • LCE provides a robust, efficient, and high-performing solution compared to existing ensemble techniques.
  • The discovered cluster-to-cluster associations enhance the ensemble methodology for gene expression analysis.