A probabilistic model for the extraction of expression levels from oligonucleotide arrays

Milo, Marta; Fazeli, Alireza; Niranjan, Mahesan; Lawrence, Neil D.

doi:10.1042/bst0311510

Cited by 30 publications

(15 citation statements)

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“…In this fashion both gMOS and mgMOS process each chip independently. The expression pattern can then be analysed and compared in any statistical downstream analysis using the variance to integrate the data with the correspondent measure of conWdence in the expression level recorded for each gene at each time point (Milo et al, 2003). The variances associated with each transcript on the AVymetrix GeneChips enables us to weight expression levels for each time point when clustering temporal proWles, thus smoothing oV noisy points.…”

Section: Gene Expression Analysismentioning

confidence: 99%

“…As an alternative approach it is possible to make use of probabilistic models to describe both the PM and MM observed signals and to provide at the same time a measure of uncertainty for each estimate. The family of models, gamma model for oligonucleotide signal (gMOS) and modiWed gMOS (mgMOS) (Milo et al, 2003) do not require additional normalisation through multiple-chips and automatically account for background correction. They estimate the GSB for each probe set on the chip by modeling the data observed with a probability density function that is unique to each probe set.…”

Section: Gene Expression Analysismentioning

confidence: 99%

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Information for gene networks in inner ear development: A study centered on the transcription factor gata2

2007

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Section: Gene Expression Analysismentioning

confidence: 99%

Section: Gene Expression Analysismentioning

confidence: 99%

Information for gene networks in inner ear development: A study centered on the transcription factor gata2

2007

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“…This error information is especially significant for weakly expressed genes since these genes are often associated with high variability. Probabilistic probe-level processing methods (Milo et al, 2003, Hein et al, 2005, Liu et al, 2005 have been developed to calculate the measurement error associated with each gene expression estimate and this measurement error has been shown to be useful in the downstream analysis (Sanguinetti et al, 2005, Liu et al, 2006a, Pearson, 2008. However, this probelevel measurement error is not considered in existing mixture models that can deal with experimental replication.…”

Section: Introductionmentioning

confidence: 98%

Including Probe-Level Measurement Error in Robust Mixture Clustering of Replicated Microarray Gene Expression

Liu¹,

Rattray²

2010

Statistical Applications in Genetics and Molecular Biology

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Probabilistic mixture models provide a popular approach to cluster noisy gene expression data for exploring gene function. Since gene expression data obtained from microarray experiments are often associated with significant sources of technical and biological noise, replicated experiments are typically used to deal with data variability, and internal replication (e.g. from multiple probes per gene in an experiment) provides valuable information about technical sources of noise. However, current implementations of mixture models either do not consider the correlation between the replicated measurements for the same experimental condition, or ignore the probe-level measurement error, and thus overlook the rich information about technical noise. Moreover, most current methods use non-robust Gaussian components to describe the data, and these methods are therefore sensitive to non-Gaussian clusters and outliers. In many cases, this will lead to over-estimation of the number of model components as multiple Gaussian components are used to fit a non-Gaussian cluster. We propose a robust Student's t-mixture model, which explicitly handles replicated gene expression data, includes the consideration of probe-level measurement error when available and automatically selects the appropriate number of model components using a minimum message length criterion. We apply the model to gene expression data using probe-level measurements from an Affymetrix probe-level model, multi-mgMOS, which provides uncertainty estimates. The proposed Student's t-mixture model shows robust performance on synthetic data sets with realistic noise characteristics in comparison to a standard Gaussian mixture model and two other previously published methods. We also compare performance with these methods on two yeast time-course data sets and show that the new method obtains more biologically meaningful clusters in terms of enrichment statistics for GO categories and interactions between transcription factors and genes. Automatically selecting the number of components is more computationally efficient than using a model selection approach and allows the methods to be applied to larger data sets.

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“…An important aspect of any inference procedure that is based on experimental data is the quantification of measurement uncertainties, and the propagation of the effect of uncertainties in downstream inference. In this context, Milo et al [6] developed a procedure to quantify probe level uncertainty in Affymetrix microarrays by fitting a Gamma density function. Uncertainties quantified in this manner can be propagated through downstream analysis [7].…”

Section: Introductionmentioning

confidence: 99%

Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle

Chang

Hung

Niranjan

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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Using high throughput DNA binding data for transcription factors and DNA microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (NCA) approach. We incorporated probe level uncertainties in gene expression measurements into the NCA analysis by the application of probabilistic principal component analysis (PPCA), and applied the method to data from yeast metabolic cycle. Analysis shows statistically significant enhancement to periodicity in a large fraction of the transcription factor activities inferred from the model. For several of these we found literature evidence of post-transcriptional regulation. Accounting for probe level uncertainty of microarray measurements leads to improved network component analysis. Transcription factor profiles showing greater periodicity at their activity levels, rather than at the corresponding mRNA levels, for over half the regulators in the networks points to extensive post-transcriptional regulations.

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A probabilistic model for the extraction of expression levels from oligonucleotide arrays

Cited by 30 publications

References 1 publication

Information for gene networks in inner ear development: A study centered on the transcription factor gata2

Information for gene networks in inner ear development: A study centered on the transcription factor gata2

Including Probe-Level Measurement Error in Robust Mixture Clustering of Replicated Microarray Gene Expression

Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle

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