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• W1538077892 abstract "An important research problem in computational biology is the identification of expression programs, sets of co-activated genes orchestrating physiological processes, and the characterization of the functional breadth of these programs. The use of mammalian expression data compendia for discovery of such programs presents several challenges, including: 1) cellular inhomogeneity within samples, 2) genetic and environmental variation across samples, and 3) uncertainty in the numbers of programs and sample populations. We developed GeneProgram, a new unsupervised computational framework that uses expression data to simultaneously organize genes into overlapping programs and tissues into groups to produce maps of inter-species expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Our method addresses each of the above challenges by using a probabilistic model that: 1) allocates mRNA to different expression programs that may be shared across tissues, 2) is hierarchical, treating each tissue as a sample from a population of related tissues, and 3) uses Dirichlet Processes, a non-parametric Bayesian method that provides prior distributions over numbers of sets while penalizing model complexity. Using real gene expression data, we show that GeneProgram outperforms several popular expression analysis methods in recovering biologically interpretable gene sets. From a large compendium of mouse and human expression data, GeneProgram discovers 19 tissue groups and 100 expression programs active in mammalian tissues. Our method automatically constructs a comprehensive, body-wide map of expression programs and characterizes their functional generality. This map can be used for guiding future biological experiments, such as discovery of genes for new drug targets that exhibit minimal “cross-talk” with unintended organs, or genes that maintain general physiological responses that go awry in disease states. Further, our method is general, and can be applied readily to novel compendia of biological data." @default.
• W1538077892 created "2016-06-24" @default.
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• W1538077892 date "2007-07-06" @default.
• W1538077892 modified "2023-09-27" @default.
• W1538077892 title "Hierarchical Dirichlet Process-Based Models For Discovery of Cross-species Mammalian Gene Expression" @default.
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