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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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Published on: January 16, 2019

Fuzzy-adaptive-subspace-iteration-based two-way clustering of microarray data.

Jahangheer Shaik¹, Mohammed Yeasin

¹Department of Pathology and Immunology, Washington University, Cortex Building, St Louis, MO 63130, USA. jshaik@path.wustl.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|May 2, 2009

Summary

This summary is machine-generated.

This study introduces Fuzzy-Adaptive-Subspace-Iteration-based Two-way Clustering (FASIC) for identifying differentially expressed genes (DEGs) in microarray data. FASIC enhances gene clustering and classification accuracy in two-sample experiments.

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray data analysis is crucial for identifying differentially expressed genes (DEGs).
Existing clustering methods may not fully capture complex gene-sample relationships in two-sample experiments.

Purpose of the Study:

To present a novel Fuzzy-Adaptive-Subspace-Iteration-based Two-way Clustering (FASIC) algorithm.
To improve the identification and significance determination of DEGs from microarray data.
To enhance the classification accuracy of sample classes based on gene expression patterns.

Main Methods:

Fuzzy membership is integrated into the Adaptive Subspace Iteration (ASI) algorithm, creating a fuzzy-ASI approach for two-way clustering.
A progressive framework assigns relevance values to genes within clusters, followed by scoring and ranking based on classification potential.
The R-test is employed to convert ranks into P values for determining gene significance.

Main Results:

The FASIC approach demonstrates effective two-way clustering of microarray data.
Validated DEGs show potential for accurate sample classification.
Empirical analyses on simulated and real datasets confirm the efficacy of the proposed method.

Conclusions:

FASIC provides a robust framework for identifying DEGs in two-sample microarray studies.
The fuzzy-ASI algorithm enhances the analysis of gene expression data.
The method offers improved accuracy in gene significance determination and sample classification.