Continuous representations of time-series gene expression data

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Continuous representations of time-series gene expression data (2003)

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by Ziv Bar-joseph , Georg K. Gerber , David K. Gifford , Tommi S. Jaakkola , Itamar Simon

Venue:	J COMPUT BIOL
Citations:	95 - 11 self

BibTeX

@ARTICLE{Bar-joseph03continuousrepresentations,
    author = {Ziv Bar-joseph and Georg K. Gerber and David K. Gifford and Tommi S. Jaakkola and Itamar Simon},
    title = { Continuous representations of time-series gene expression data},
    journal = {J COMPUT BIOL},
    year = {2003},
    pages = {341}
}

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Abstract

We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve. We constrain the spline coefficients of genes in the same class to have similar expression patterns, while also allowing for gene specific parameters. We show that unobserved time points can be reconstructed using our method with 10–15 % less error when compared to previous best methods. Our clustering algorithm operates directly on the continuous representations of gene expression profiles, and we demonstrate that this is particularly effective when applied to nonuniformly sampled data. Our continuous alignment algorithm also avoids difficulties encountered by discrete approaches. In particular, our method allows for control of the number of degrees of freedom of the warp through the specification of parameterized functions, which helps to avoid overfitting. We demonstrate that our algorithm produces stable low-error alignments on real expression data and further show a specific application to yeast knock-out data that produces biologically meaningful results.

Keyphrases

continuous representation time-series gene expression data unobserved time point time-series gene expression analysis speci cation value estimation speci application meaningful result stable low-error alignment spline coef cients continuous alignment algorithm real expression data overall smooth expression curve gene speci parameter dif culties knock-out data cubic spline parameterized function time series expression data gene expression pro le similar expression pattern discrete approach key word principled estimation time point

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