Model-based Method for Transcription Factor Target Identification with Limited Data

Antti Honkela, Charles Girardot, E. Hilary Gustafson, Ya-Hsin Liu, Eileen E. M. Furlong, Neil D. LawrenceMagnus Rattray
,  107(17):7793-7798, 2010.

Abstract

We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-honkela-modelbased10, title = {Model-based Method for Transcription Factor Target Identification with Limited Data}, author = {Antti Honkela and Charles Girardot and E. Hilary Gustafson and Ya-Hsin Liu and Eileen E. M. Furlong and Neil D. Lawrence and Magnus Rattray}, pages = {7793--7798}, year = {}, editor = {}, volume = {107}, number = {17}, url = {http://inverseprobability.com/publications/honkela-modelbased10.html}, abstract = {We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.} }
Endnote
%0 Conference Paper %T Model-based Method for Transcription Factor Target Identification with Limited Data %A Antti Honkela %A Charles Girardot %A E. Hilary Gustafson %A Ya-Hsin Liu %A Eileen E. M. Furlong %A Neil D. Lawrence %A Magnus Rattray %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-honkela-modelbased10 %I PMLR %J Proceedings of Machine Learning Research %P 7793--7798 %R 10.1073/pnas.0914285107 %U http://inverseprobability.com %V %N 17 %W PMLR %X We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.
RIS
TY - CPAPER TI - Model-based Method for Transcription Factor Target Identification with Limited Data AU - Antti Honkela AU - Charles Girardot AU - E. Hilary Gustafson AU - Ya-Hsin Liu AU - Eileen E. M. Furlong AU - Neil D. Lawrence AU - Magnus Rattray BT - PY - DA - ED - ID - pmlr-v-honkela-modelbased10 PB - PMLR SP - 7793 DP - PMLR EP - 7798 DO - 10.1073/pnas.0914285107 L1 - UR - http://inverseprobability.com/publications/honkela-modelbased10.html AB - We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance. ER -
APA
Honkela, A., Girardot, C., Gustafson, E.H., Liu, Y., Furlong, E.E.M., Lawrence, N.D. & Rattray, M.. (). Model-based Method for Transcription Factor Target Identification with Limited Data. , in PMLR (17):7793-7798

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