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A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
BACKGROUND: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied di...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116489/ https://www.ncbi.nlm.nih.gov/pubmed/21599902 http://dx.doi.org/10.1186/1471-2105-12-180 |
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author | Kalaitzis, Alfredo A Lawrence, Neil D |
author_facet | Kalaitzis, Alfredo A Lawrence, Neil D |
author_sort | Kalaitzis, Alfredo A |
collection | PubMed |
description | BACKGROUND: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series. In this paper we propose a simple model for accounting for the underlying temporal nature of the data based on a Gaussian process. RESULTS: We review Gaussian process (GP) regression for estimating the continuous trajectories underlying in gene expression time-series. We present a simple approach which can be used to filter quiet genes, or for the case of time series in the form of expression ratios, quantify differential expression. We assess via ROC curves the rankings produced by our regression framework and compare them to a recently proposed hierarchical Bayesian model for the analysis of gene expression time-series (BATS). We compare on both simulated and experimental data showing that the proposed approach considerably outperforms the current state of the art. CONCLUSIONS: Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of microarray time series. The Gaussian process framework offers a natural way of handling biological replicates and missing values and provides confidence intervals along the estimated curves of gene expression. Therefore, we believe Gaussian processes should be a standard tool in the analysis of gene expression time series. |
format | Online Article Text |
id | pubmed-3116489 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31164892011-06-17 A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression Kalaitzis, Alfredo A Lawrence, Neil D BMC Bioinformatics Research Article BACKGROUND: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which genes are differentially expressed. Often these analysis stages are applied disregarding the fact that the data is drawn from a time series. In this paper we propose a simple model for accounting for the underlying temporal nature of the data based on a Gaussian process. RESULTS: We review Gaussian process (GP) regression for estimating the continuous trajectories underlying in gene expression time-series. We present a simple approach which can be used to filter quiet genes, or for the case of time series in the form of expression ratios, quantify differential expression. We assess via ROC curves the rankings produced by our regression framework and compare them to a recently proposed hierarchical Bayesian model for the analysis of gene expression time-series (BATS). We compare on both simulated and experimental data showing that the proposed approach considerably outperforms the current state of the art. CONCLUSIONS: Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of microarray time series. The Gaussian process framework offers a natural way of handling biological replicates and missing values and provides confidence intervals along the estimated curves of gene expression. Therefore, we believe Gaussian processes should be a standard tool in the analysis of gene expression time series. BioMed Central 2011-05-20 /pmc/articles/PMC3116489/ /pubmed/21599902 http://dx.doi.org/10.1186/1471-2105-12-180 Text en Copyright ©2011 Kalaitzis and Lawrence; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Kalaitzis, Alfredo A Lawrence, Neil D A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression |
title | A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression |
title_full | A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression |
title_fullStr | A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression |
title_full_unstemmed | A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression |
title_short | A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression |
title_sort | simple approach to ranking differentially expressed gene expression time courses through gaussian process regression |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116489/ https://www.ncbi.nlm.nih.gov/pubmed/21599902 http://dx.doi.org/10.1186/1471-2105-12-180 |
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