Probe-level Measurement Error Improves Accuracy in Detecting Differential Gene Expression

Xuejun Liu, Marta Milo, Neil D. Lawrence, Magnus Rattray
Bioinformatics, 22(17):2107-2113, 2006.

Abstract

**Motivation:** Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level variance provides useful information which can help in the identification of differentially expressed genes.\ \ **Results:** We propose a Bayesian method to include probe-level variances into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for effcient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational effciency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in data set and a mouse time-course data set. Results show that the inclusion of probelevel measurement error improves accuracy in detecting differential gene expression.\ \ **Availability:** The methods described in this paper have been implemented in an R package *pplr* that is currently available from .\ \ **Contact:** Magnus Rattray

Cite this Paper

BibTeX
@Article{Liu-variances06, title = {Probe-level Measurement Error Improves Accuracy in Detecting Differential Gene Expression}, author = {Liu, Xuejun and Milo, Marta and Lawrence, Neil D. and Rattray, Magnus}, journal = {Bioinformatics}, pages = {2107--2113}, year = {2006}, volume = {22}, number = {17}, doi = {10.1093/bioinformatics/btl361}, url = {http://inverseprobability.com/publications/liu-variances06.html}, abstract = {**Motivation:** Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level variance provides useful information which can help in the identification of differentially expressed genes.\ \ **Results:** We propose a Bayesian method to include probe-level variances into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for effcient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational effciency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in data set and a mouse time-course data set. Results show that the inclusion of probelevel measurement error improves accuracy in detecting differential gene expression.\ \ **Availability:** The methods described in this paper have been implemented in an R package *pplr* that is currently available from .\ \ **Contact:** Magnus Rattray} }
Endnote
%0 Journal Article %T Probe-level Measurement Error Improves Accuracy in Detecting Differential Gene Expression %A Xuejun Liu %A Marta Milo %A Neil D. Lawrence %A Magnus Rattray %J Bioinformatics %D 2006 %F Liu-variances06 %P 2107--2113 %R 10.1093/bioinformatics/btl361 %U http://inverseprobability.com/publications/liu-variances06.html %V 22 %N 17 %X **Motivation:** Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level variance provides useful information which can help in the identification of differentially expressed genes.\ \ **Results:** We propose a Bayesian method to include probe-level variances into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for effcient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational effciency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in data set and a mouse time-course data set. Results show that the inclusion of probelevel measurement error improves accuracy in detecting differential gene expression.\ \ **Availability:** The methods described in this paper have been implemented in an R package *pplr* that is currently available from .\ \ **Contact:** Magnus Rattray
RIS
TY - JOUR TI - Probe-level Measurement Error Improves Accuracy in Detecting Differential Gene Expression AU - Xuejun Liu AU - Marta Milo AU - Neil D. Lawrence AU - Magnus Rattray DA - 2006/01/01 ID - Liu-variances06 VL - 22 IS - 17 SP - 2107 EP - 2113 DO - 10.1093/bioinformatics/btl361 UR - http://inverseprobability.com/publications/liu-variances06.html AB - **Motivation:** Finding differentially expressed genes is a fundamental objective of a microarray experiment. Numerous methods have been proposed to perform this task. Existing methods are based on point estimates of gene expression level obtained from each microarray experiment. This approach discards potentially useful information about measurement error that can be obtained from an appropriate probe-level analysis. Probabilistic probe-level models can be used to measure gene expression and also provide a level of uncertainty in this measurement. This probe-level variance provides useful information which can help in the identification of differentially expressed genes.\ \ **Results:** We propose a Bayesian method to include probe-level variances into the detection of differentially expressed genes from replicated experiments. A variational approximation is used for effcient parameter estimation. We compare this approximation with MAP and MCMC parameter estimation in terms of computational effciency and accuracy. The method is used to calculate the probability of positive log-ratio (PPLR) of expression levels between conditions. Using the measurements from a recently developed Affymetrix probe-level model, multi-mgMOS, we test PPLR on a spike-in data set and a mouse time-course data set. Results show that the inclusion of probelevel measurement error improves accuracy in detecting differential gene expression.\ \ **Availability:** The methods described in this paper have been implemented in an R package *pplr* that is currently available from .\ \ **Contact:** Magnus Rattray ER -
APA
Liu, X., Milo, M., Lawrence, N.D. & Rattray, M.. (2006). Probe-level Measurement Error Improves Accuracy in Detecting Differential Gene Expression. Bioinformatics 22(17):2107-2113 doi:10.1093/bioinformatics/btl361 Available from http://inverseprobability.com/publications/liu-variances06.html.

Related Material