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Related Experiment Video

Updated: Jul 9, 2026

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

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

Published on: January 16, 2019

TimeClust: a clustering tool for gene expression time series.

Paolo Magni¹, Fulvia Ferrazzi, Lucia Sacchi

¹Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Via Ferrata 1, Pavia, Italy. paolo.magni@unipv.it

Bioinformatics (Oxford, England)

|December 11, 2007

Summary

TimeClust is a new software for clustering genes based on their temporal expression profiles from DNA microarray time-course experiments. It offers novel algorithms alongside established methods for analyzing gene expression patterns.

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

DNA microarrays generate time-course expression data.
Analyzing temporal gene expression is crucial for understanding biological processes.
Existing clustering methods may have limitations for short time series.

Purpose of the Study:

To introduce TimeClust, a user-friendly software package.
To enable effective clustering of genes based on temporal expression profiles.
To facilitate the analysis of DNA microarray time-course data.

Main Methods:

Implementation of two original clustering algorithms tailored for short time series.
Integration of hierarchical clustering.
Inclusion of self-organizing maps (SOMs).

Main Results:

TimeClust provides a user-friendly interface for gene expression analysis.
The software effectively clusters genes with similar temporal expression patterns.
It supports the analysis of complex time-course microarray datasets.

Conclusions:

TimeClust is a valuable tool for researchers studying gene expression dynamics.
The software enhances the analysis of time-course gene expression data.
It offers flexible and powerful clustering capabilities for biological research.