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DNA Microarrays

DNA 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...

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Cluster 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...

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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

A cluster merging method for time series microarray with production values.

Camelia Chira¹, Javier Sedano, Monica Camara

¹Instituto Tecnológico de Castilla y León, c/. López Bravo 70, Pol. Ind. Villalonquéjar, 09001 Burgos, Spain.

International Journal of Neural Systems

|August 2, 2014

Summary

This summary is machine-generated.

This study introduces a novel cluster merging method for time-course microarray data analysis. It effectively groups co-expressed genes by combining replicate data, improving biological insights.

Keywords:

Microarray analysis clustering time series merging methods

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Analyzing time-course microarray data with multiple replicates presents challenges in meaningful gene clustering.
Identifying co-expressed genes is crucial for understanding biological processes like bacterial production and growth.

Purpose of the Study:

To propose a novel cluster merging method for time-course microarray data analysis.
To group genes with high correlation from multiple biological replicates.
To identify co-expressed genes related to bacterial production and growth.

Main Methods:

A novel cluster merging method is proposed, starting with clusters from individual temporal series (biological replicates).
Clusters are merged based on the frequency of gene co-occurrence across individual clusterings.
Shape-based clustering methods are used to generate initial gene groups from individual time series.

Main Results:

The method produces meaningful gene groups ranked by clustering agreement across replicates.
Gene lists are further sorted using information correlation and problem-specific measures.
The approach effectively identifies co-expressed genes relevant to bacterial growth.

Conclusions:

The proposed cluster merging method offers a robust approach for analyzing time-course microarray data.
It successfully integrates information from multiple replicates to yield biologically relevant gene clusters.
The method outperforms clustering based on mean values and standard shape-based algorithms.