Abstract
BackgroundTemporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other.ResultsWe demonstrate that functional regression methodology can pinpoint relationships that exist between temporary gene expression profiles for different life cycle phases and incorporates dimension reduction as needed for these high-dimensional data. By applying these tools, gene expression profiles for pupa and adult phases are found to be strongly related to the profiles of the same genes obtained during the embryo phase. Moreover, one can distinguish between gene groups that exhibit relationships with positive and others with negative associations between later life and embryonal expression profiles. Specifically, we find a positive relationship in expression for muscle development related genes, and a negative relationship for strictly maternal genes for Drosophila, using temporal gene expression profiles.ConclusionOur findings point to specific reactivation patterns of gene expression during the Drosophila life cycle which differ in characteristic ways between various gene groups. Functional regression emerges as a useful tool for relating gene expression patterns from different developmental stages, and avoids the problems with large numbers of parameters and multiple testing that affect alternative approaches.
Highlights
Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments
In this paper we study the nature of dynamic relationships of expression during different life cycle phases for two groups of such genes
We extend previous methodological work in various directions useful for the analysis of gene expression trajectories, demonstrating the following beneficial features: (1) functional linear regression can be broken down into a series of linear regressions of functional principal component scores of the response trajectories on those of the predictor trajectories; (2) this decomposition leads to a straightforward implementation of functional regression via a series of simple linear regressions; (3) the decomposition opens up alternative ways to interpret a functional regression relation; (4) outliers and influential trajectories corresponding to individual genes can be identified with this methodology; and (5) inference for functional regression can be obtained via bootstrapping
Summary
Temporal gene expression profiles characterize the time-dynamics of expression of specific genes and are increasingly collected in current gene expression experiments. In the analysis of experiments where gene expression is obtained over the life cycle, it is of interest to relate temporal patterns of gene expression associated with different developmental stages to each other to study patterns of long-term developmental gene regulation. Biological motivation and overview Normal development of an organism depends on precisely regulated temporal and spatial expression of its genes. In unicellular organisms, such as yeast, different sets of genes are expressed at different stages of the cell cycle. With very few exceptions, all of the different types of cell possess the same genes; each type of cell only expresses a unique set of "signature" genes at a certain time, depending on current develop-. A second phase of myogenesis generates a diverse pattern of muscle fibers, facilitating the more complex behaviors of the adult fly [2]
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