Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities

Guido SanguinettiNeil D. LawrenceMagnus Rattray
,  22(22):2275-2281, 2006.

Abstract

**Motivation**: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. Recent experimental high-throughput techniques such as Chromatine Immunoprecipitation provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data.\ \ **Results**: We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast data sets in which the network structure has previously been obtained using Chromatine Immunoprecipitation data. Predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell.\ \ **Availability**: MATLAB code is available from .

Cite this Paper


BibTeX
@InProceedings{pmlr-v-sanguinetti-chipvar06, title = {Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities}, author = {Guido Sanguinetti and Neil D. Lawrence and Magnus Rattray}, pages = {2275--2281}, year = {}, editor = {}, volume = {22}, number = {22}, url = {http://inverseprobability.com/publications/sanguinetti-chipvar06.html}, abstract = {**Motivation**: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. Recent experimental high-throughput techniques such as Chromatine Immunoprecipitation provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data.\ \ **Results**: We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast data sets in which the network structure has previously been obtained using Chromatine Immunoprecipitation data. Predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell.\ \ **Availability**: MATLAB code is available from .} }
Endnote
%0 Conference Paper %T Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities %A Guido Sanguinetti %A Neil D. Lawrence %A Magnus Rattray %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-sanguinetti-chipvar06 %I PMLR %J Proceedings of Machine Learning Research %P 2275--2281 %R 10.1093/bioinformatics/btl473 %U http://inverseprobability.com %V %N 22 %W PMLR %X **Motivation**: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. Recent experimental high-throughput techniques such as Chromatine Immunoprecipitation provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data.\ \ **Results**: We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast data sets in which the network structure has previously been obtained using Chromatine Immunoprecipitation data. Predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell.\ \ **Availability**: MATLAB code is available from .
RIS
TY - CPAPER TI - Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities AU - Guido Sanguinetti AU - Neil D. Lawrence AU - Magnus Rattray BT - PY - DA - ED - ID - pmlr-v-sanguinetti-chipvar06 PB - PMLR SP - 2275 DP - PMLR EP - 2281 DO - 10.1093/bioinformatics/btl473 L1 - UR - http://inverseprobability.com/publications/sanguinetti-chipvar06.html AB - **Motivation**: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. Recent experimental high-throughput techniques such as Chromatine Immunoprecipitation provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data.\ \ **Results**: We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast data sets in which the network structure has previously been obtained using Chromatine Immunoprecipitation data. Predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell.\ \ **Availability**: MATLAB code is available from . ER -
APA
Sanguinetti, G., Lawrence, N.D. & Rattray, M.. (). Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. , in PMLR (22):2275-2281

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