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Updated: May 28, 2026

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Unsupervised fuzzy pattern discovery in gene expression data.

Gene P K Wu¹, Keith C C Chan, Andrew K C Wong

¹Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong. cspkwu@comp.polyu.edu.hk

BMC Bioinformatics

|October 13, 2011

Summary

This summary is machine-generated.

This study introduces a novel fuzzy gene clustering method for analyzing gene expression data without class labels. The approach effectively uncovers hidden gene interaction patterns, particularly in cancerous tissues, improving pattern discovery from microarrays.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene expression pattern discovery is crucial but challenging without sample class information.
Traditional methods discretize gene expression, treating it as a discrete-data problem.
Discovering patterns becomes difficult when tissue class information is unavailable.

Purpose of the Study:

To develop a method for discovering gene expression patterns without relying on sample class labels.
To address limitations in finding overlapping patterns across gene clusters.
To enhance pattern discovery in large gene expression datasets.

Main Methods:

Clustering genes into smaller groups and using a representative gene for discretization.
Introducing a 'fuzzifying' method to overcome limitations of crisp gene clusters.
Applying the method to synthetic and real gene expression data.

Main Results:

The proposed fuzzy gene clustering and discretization method is effective, even without class labels.
Fuzzification reveals overlapping relationships among gene clusters, uncovering hidden patterns.
High-order patterns discovered highlight multiple gene interactions in cancerous tissues.

Conclusions:

The method provides a unified framework for fast and accurate gene expression pattern discovery.
Fuzzy gene clustering, discretization, and fuzzification are essential for comprehensive pattern discovery in large gene sets.
The approach is effective for analyzing error-prone microarray data, even without tissue class information.