Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model.
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Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance.The goodness of fit of the Poisson mixture model on 15 sets of biological SAGE replicates is compared to the previously proposed hierarchical gamma-Poisson (negative binomial) model, and a substantial improvement is seen.Code for the R statistical software package is included to assist investigators in applying this model to their own data.
Affiliation: Victor Ling Laboratory, Department of Cancer Genetics and Developmental Biology, BC Cancer Research Centre, 675 West 10th Ave,, Vancouver, Canada. scottz@bccrc.ca
ABSTRACT
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Background: Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Binomial or Poisson distribution. However, tag counts observed across multiple libraries (for example, one or more groups of biological replicates) have additional variance that cannot be accommodated by this assumption alone. Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance. Here, a Poisson mixture model, which assumes excess variability arises from sampling a mixture of distinct components, is proposed and the merits of this model are discussed and evaluated. Results: The goodness of fit of the Poisson mixture model on 15 sets of biological SAGE replicates is compared to the previously proposed hierarchical gamma-Poisson (negative binomial) model, and a substantial improvement is seen. In further support of the mixture model, there is observed: 1) an increase in the number of mixture components needed to fit the expression of tags representing more than one transcript; and 2) a tendency for components to cluster libraries into the same groups. A confidence score is presented that can identify tags that are differentially expressed between groups of SAGE libraries. Several examples where this test outperforms those previously proposed are highlighted. Conclusion: The Poisson mixture model performs well as a) a method to represent SAGE data from biological replicates, and b) a basis to assign significance when testing for differential expression between multiple groups of replicates. Code for the R statistical software package is included to assist investigators in applying this model to their own data. Related in: MedlinePlus |
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Mentions: However, a number of observations are found significant using the overdispersed log-linear model and not the Poisson mixture model, and vice versa. A closer look at the most extreme examples illustrates the superior performance of the mixture approach (Figure 3). In the first example, tag ACAACAAAGA seems clearly expressed in normal libraries, but is completely abolished in the ependymoma libraries. However, according to the overdispersed model, the observation is not at all significant (p = 0.9998). The mixture model, however, produces a confidence score of 99.42%, which suggests this tag is highly informative with respect to sample type. This example demonstrates the difficulty that the log-linear model has with fitting groups where tag counts are zero, a problem that is even more pronounced when using a logistic regression model (for a more thorough discussion of this problem see [6]). |
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Affiliation: Victor Ling Laboratory, Department of Cancer Genetics and Developmental Biology, BC Cancer Research Centre, 675 West 10th Ave,, Vancouver, Canada. scottz@bccrc.ca
Background: Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Binomial or Poisson distribution. However, tag counts observed across multiple libraries (for example, one or more groups of biological replicates) have additional variance that cannot be accommodated by this assumption alone. Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance. Here, a Poisson mixture model, which assumes excess variability arises from sampling a mixture of distinct components, is proposed and the merits of this model are discussed and evaluated.
Results: The goodness of fit of the Poisson mixture model on 15 sets of biological SAGE replicates is compared to the previously proposed hierarchical gamma-Poisson (negative binomial) model, and a substantial improvement is seen. In further support of the mixture model, there is observed: 1) an increase in the number of mixture components needed to fit the expression of tags representing more than one transcript; and 2) a tendency for components to cluster libraries into the same groups. A confidence score is presented that can identify tags that are differentially expressed between groups of SAGE libraries. Several examples where this test outperforms those previously proposed are highlighted.
Conclusion: The Poisson mixture model performs well as a) a method to represent SAGE data from biological replicates, and b) a basis to assign significance when testing for differential expression between multiple groups of replicates. Code for the R statistical software package is included to assist investigators in applying this model to their own data.