Related Experiment Video
Updated: Jun 30, 2026
09:45
Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response
Published on: August 10, 2017
Noise-robust soft clustering of gene expression time-course data
Matthias E Futschik1, Bronwyn Carlisle
1Institute of Theoretical Biology, Humboldt-University, Invalidenstr. 43, 10115 Berlin, Germany. m.futschik@biologie.hu-berlin.de
Journal of Bioinformatics and Computational Biology
|August 4, 2005
Summary
Soft clustering, using fuzzy c-means, offers a robust approach for analyzing microarray time-course data. This method overcomes limitations of hard clustering, improving noise resistance and gene relevance identification.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Clustering is vital for unsupervised learning in gene expression data analysis.
- Traditional hard clustering assigns genes to single clusters, which is problematic for overlapping microarray time-course data.
- Hard clustering is sensitive to noise and may require pre-filtering, potentially excluding relevant genes.
Purpose of the Study:
- To address the limitations of hard clustering in microarray data analysis.
- To introduce and implement a soft clustering approach for improved gene expression data interpretation.
- To develop a user-friendly software package for soft clustering analysis.
Main Methods:
- Application of soft clustering, specifically the fuzzy c-means algorithm.
- Development of procedures for optimizing clustering parameters.
- Implementation of a software package named Mfuzz using the R statistical language.
Main Results:
- Soft clustering provides accessible internal cluster structures, indicating cluster-gene representation.
- It defines relationships between clusters, revealing global clustering structures.
- The fuzzy c-means algorithm demonstrates increased robustness to noise and avoids a priori gene exclusion.
Conclusions:
- Soft clustering, implemented via fuzzy c-means, is advantageous for analyzing complex microarray time-course data.
- The Mfuzz R package provides a freely available tool for researchers to perform soft clustering.
- This approach enhances the identification of regulatory elements and preserves biologically relevant genes in analysis.
Related Concept Videos
01:58Cell Specific Gene Expression
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
02:34DNA Microarrays
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
03:21RNA-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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
01:20Cluster Sampling Method
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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
01:58Cell Specific Gene Expression
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
01:23Regulation of Expression at Multiple Steps
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...