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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

Clustering.

G J McLachlan¹, R W Bean², S K Ng³

¹Department of Mathematics, The University of Queensland, Brisbane, QLD, Australia. g.mclachlan@uq.edu.au.

Methods in Molecular Biology (Clifton, N.J.)

|November 30, 2016

Summary

This summary is machine-generated.

This study introduces model-based clustering using normal mixtures for gene expression analysis. It groups genes and tissues with similar expression patterns, enhancing biological insights from complex datasets.

Keywords:

Autoregressive random effects Clustering of gene profiles Clustering of tissue samples Hierarchical agglomerative methods Mixtures of factor analyzers Mixtures of linear mixed-effects models Model-based methods Normal mixture models Partitional methods Time-course data k-means

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene expression data analysis often requires grouping genes or samples with similar patterns.
Various clustering methods exist, including hierarchical, k-means, and self-organizing maps.
Model-based approaches offer a probabilistic framework for clustering.

Purpose of the Study:

To apply model-based clustering using mixtures of normals to gene expression data.
To cluster both tissue samples (gene signatures) and gene profiles.
To analyze time-course gene expression data for dynamic pattern identification.

Main Methods:

Utilizing mixtures of normals for probabilistic clustering.
Applying clustering to identify groups of genes with similar expression profiles.
Clustering tissue samples based on their gene expression signatures.
Incorporating time-course data for dynamic gene expression analysis.

Main Results:

Successful model-based clustering of gene expression data was achieved.
Identified distinct clusters of genes and tissue samples with similar behaviors.
Demonstrated the utility of normal mixture models for gene expression data.

Conclusions:

Model-based clustering with normal mixtures provides a robust method for analyzing gene expression data.
This approach facilitates the organization of genes and tissues into biologically meaningful groups.
Effective for analyzing complex datasets, including time-course gene expression profiles.