A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips

Xuejun LiuMarta MiloNeil D. LawrenceMagnus Rattray
,  21(18):3637-3644, 2005.

Abstract

**Motivation:** Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarisation methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large data sets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and that it does not account for specific binding to the mismatch (MM) probes.\ \ **Results:** We extend mgMOS to model the binding affinity of probe-pairs across multiple chips and to capture the effect of specific binding to MM probes. The new model, multi-mgMOS, provides improved accuracy, as demonstrated on some bench-mark data sets and a real time-course data set, and is much more computationally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling. We demonstrate how the probabilistic model can be used to estimate credibility intervals for expression levels and their log-ratios between conditions.\ \ **Availability:** Both mgMOS and the new model multi-mgMOS have been implemented in an R package that is currently available from .

Cite this Paper


BibTeX
@InProceedings{pmlr-v-liu-tractable04, title = {A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips}, author = {Xuejun Liu and Marta Milo and Neil D. Lawrence and Magnus Rattray}, pages = {3637--3644}, year = {}, editor = {}, volume = {21}, number = {18}, url = {http://inverseprobability.com/publications/liu-tractable04.html}, abstract = {**Motivation:** Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarisation methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large data sets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and that it does not account for specific binding to the mismatch (MM) probes.\ \ **Results:** We extend mgMOS to model the binding affinity of probe-pairs across multiple chips and to capture the effect of specific binding to MM probes. The new model, multi-mgMOS, provides improved accuracy, as demonstrated on some bench-mark data sets and a real time-course data set, and is much more computationally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling. We demonstrate how the probabilistic model can be used to estimate credibility intervals for expression levels and their log-ratios between conditions.\ \ **Availability:** Both mgMOS and the new model multi-mgMOS have been implemented in an R package that is currently available from .} }
Endnote
%0 Conference Paper %T A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips %A Xuejun Liu %A Marta Milo %A Neil D. Lawrence %A Magnus Rattray %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-liu-tractable04 %I PMLR %J Proceedings of Machine Learning Research %P 3637--3644 %R 10.1093/bioinformatics/bti583 %U http://inverseprobability.com %V %N 18 %W PMLR %X **Motivation:** Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarisation methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large data sets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and that it does not account for specific binding to the mismatch (MM) probes.\ \ **Results:** We extend mgMOS to model the binding affinity of probe-pairs across multiple chips and to capture the effect of specific binding to MM probes. The new model, multi-mgMOS, provides improved accuracy, as demonstrated on some bench-mark data sets and a real time-course data set, and is much more computationally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling. We demonstrate how the probabilistic model can be used to estimate credibility intervals for expression levels and their log-ratios between conditions.\ \ **Availability:** Both mgMOS and the new model multi-mgMOS have been implemented in an R package that is currently available from .
RIS
TY - CPAPER TI - A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips AU - Xuejun Liu AU - Marta Milo AU - Neil D. Lawrence AU - Magnus Rattray BT - PY - DA - ED - ID - pmlr-v-liu-tractable04 PB - PMLR SP - 3637 DP - PMLR EP - 3644 DO - 10.1093/bioinformatics/bti583 L1 - UR - http://inverseprobability.com/publications/liu-tractable04.html AB - **Motivation:** Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarisation methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large data sets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and that it does not account for specific binding to the mismatch (MM) probes.\ \ **Results:** We extend mgMOS to model the binding affinity of probe-pairs across multiple chips and to capture the effect of specific binding to MM probes. The new model, multi-mgMOS, provides improved accuracy, as demonstrated on some bench-mark data sets and a real time-course data set, and is much more computationally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling. We demonstrate how the probabilistic model can be used to estimate credibility intervals for expression levels and their log-ratios between conditions.\ \ **Availability:** Both mgMOS and the new model multi-mgMOS have been implemented in an R package that is currently available from . ER -
APA
Liu, X., Milo, M., Lawrence, N.D. & Rattray, M.. (). A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips. , in PMLR (18):3637-3644

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