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

Xuejun Liu; Marta Milo; Neil D. Lawrence; Magnus Rattray

doi:10.1093/bioinformatics/bti583

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

Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray

Bioinformatics, 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 http://umber.sbs.man.ac.uk/resources/puma.

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BibTeX


@Article{Liu-tractable04,
  title = 	 {A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips},
  author = 	 {Liu, Xuejun and Milo, Marta and Lawrence, Neil D. and Rattray, Magnus},
  journal =      {Bioinformatics},
  pages = 	 {3637--3644},
  year = 	 {2005},
  volume = 	 {21},
  number =       {18},
  doi = 	 {10.1093/bioinformatics/bti583},
  url = 	 {/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 Journal Article
%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
%J Bioinformatics
%D 2005	
%F Liu-tractable04
%P 3637--3644
%R 10.1093/bioinformatics/bti583
%U /publications/liu-tractable04.html
%V 21
%N 18
%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  - JOUR
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
DA  - 2005/07/14	
ID  - Liu-tractable04
VL  - 21
IS  - 18
SP  - 3637
EP  - 3644
DO  - 10.1093/bioinformatics/bti583
UR  - /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.. (2005). A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips. Bioinformatics 21(18):3637-3644 doi:10.1093/bioinformatics/bti583 Available from /publications/liu-tractable04.html.