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

Alfredo A. KalaitzisNeil D. Lawrence
,  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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-kalaitzis-simple11, title = {A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression}, author = {Alfredo A. Kalaitzis and Neil D. Lawrence}, year = {}, editor = {}, volume = {12}, number = {180}, url = {http://inverseprobability.com/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 Conference Paper %T A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression %A Alfredo A. Kalaitzis %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-kalaitzis-simple11 %I PMLR %J Proceedings of Machine Learning Research %P -- %R 10.1186/1471-2105-12-180 %U http://inverseprobability.com %V %N 180 %W PMLR %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 - CPAPER TI - A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression AU - Alfredo A. Kalaitzis AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-kalaitzis-simple11 PB - PMLR SP - DP - PMLR EP - DO - 10.1186/1471-2105-12-180 L1 - UR - http://inverseprobability.com/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.. (). A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression. , in PMLR (180):-

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