Visualization and Clustering of High-Dimensional Transcriptome Data Using GATE
Visualization and Clustering of High-Dimensional Transcriptome Data Using GATE
The potential gains from advances in high-throughput experimental molecular biology techniques are commonly not fully realized since these techniques often produce more data than can be easily organized and visualized. To address these problems, GATE (Grid-Analysis of Time-Series Expression) was developed. GATE is an integrated software platform for the analysis and visualization of high-dimensional time-series datasets, which allows flexible interrogation of time-series data against a wide range of databases of prior knowledge, thus linking observed molecular dynamics to potential genetic, epigenetic, and signaling mechanisms responsible for observed dynamics. This article provides a brief guide to using GATE effectively.
Systems biology, Gene expression dynamics, Data visualization, Transcriptome correlation, Network analysis
131-139
Stumpf, Patrick
dfdb286c-b321-46d3-8ba2-85b3b4a7f092
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
24 March 2014
Stumpf, Patrick
dfdb286c-b321-46d3-8ba2-85b3b4a7f092
Macarthur, Benjamin
2c0476e7-5d3e-4064-81bb-104e8e88bb6b
Stumpf, Patrick and Macarthur, Benjamin
(2014)
Visualization and Clustering of High-Dimensional Transcriptome Data Using GATE.
In,
Stem Cell Transcriptional Networks: Methods and Protocols.
(Methods in Molecular Biology, 1150)
New York.
Springer, .
(doi:10.1007/978-1-4939-0512-6_7).
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Book Section
Abstract
The potential gains from advances in high-throughput experimental molecular biology techniques are commonly not fully realized since these techniques often produce more data than can be easily organized and visualized. To address these problems, GATE (Grid-Analysis of Time-Series Expression) was developed. GATE is an integrated software platform for the analysis and visualization of high-dimensional time-series datasets, which allows flexible interrogation of time-series data against a wide range of databases of prior knowledge, thus linking observed molecular dynamics to potential genetic, epigenetic, and signaling mechanisms responsible for observed dynamics. This article provides a brief guide to using GATE effectively.
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Published date: 24 March 2014
Keywords:
Systems biology, Gene expression dynamics, Data visualization, Transcriptome correlation, Network analysis
Organisations:
Bone & Joint, Human Development & Health
Identifiers
Local EPrints ID: 409035
URI: http://eprints.soton.ac.uk/id/eprint/409035
ISSN: 1064-3745
PURE UUID: 4cde2eeb-1860-479c-b00e-0f2a933aa441
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Date deposited: 28 May 2017 04:05
Last modified: 16 Mar 2024 03:18
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Author:
Patrick Stumpf
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