A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression

Alfredo A. Kalaitzis; Neil D. Lawrence

doi:10.1186/1471-2105-12-180

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A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression

Alfredo A. Kalaitzis, Neil D. Lawrence

BMC Bioinformatics, 12(180), 2011.

Abstract

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 significantly outperforms the current state of the art.

Conclusions

Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of micro-array 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.

Links

Cite this Paper

BibTeX


@Article{Kalaitzis-simple11,
  title = 	 {A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through {G}aussian Process Regression},
  author = 	 {Kalaitzis, Alfredo A. and Lawrence, Neil D.},
  journal =      {BMC Bioinformatics},
  year = 	 {2011},
  volume = 	 {12},
  number =       {180},
  doi = 	 {10.1186/1471-2105-12-180},
  pdf = 	 {https://link.springer.com/content/pdf/10.1186/1471-2105-12-180.pdf},
  url = 	 {/publications/kalaitzis-simple11.html},
  abstract = 	 {**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 significantly outperforms the current state of the art.\
\
**Conclusions**\
\
Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of micro-array 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.}
}

Endnote

%0 Journal Article
%T A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
%A Alfredo A. Kalaitzis
%A Neil D. Lawrence
%J BMC Bioinformatics
%D 2011	
%F Kalaitzis-simple11
%R 10.1186/1471-2105-12-180
%U /publications/kalaitzis-simple11.html
%V 12
%N 180
%X **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 significantly outperforms the current state of the art.\
\
**Conclusions**\
\
Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of micro-array 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.

RIS


TY  - JOUR
TI  - A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
AU  - Alfredo A. Kalaitzis
AU  - Neil D. Lawrence
DA  - 2011/05/20	
ID  - Kalaitzis-simple11
VL  - 12
IS  - 180
DO  - 10.1186/1471-2105-12-180
L1  - https://link.springer.com/content/pdf/10.1186/1471-2105-12-180.pdf
UR  - /publications/kalaitzis-simple11.html
AB  - **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 significantly outperforms the current state of the art.\
\
**Conclusions**\
\
Gaussian processes offer an attractive trade-off between efficiency and usability for the analysis of micro-array 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.
ER  -

APA


Kalaitzis, A.A. & Lawrence, N.D.. (2011). A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression. BMC Bioinformatics 12(180) doi:10.1186/1471-2105-12-180 Available from /publications/kalaitzis-simple11.html.