文献翻译：Inferring gene expression dynamics via functional regression analysis
Inferring gene expression dynamics via functional regression analysis
通过函数型回归分析推断基因表达动态
Hans-Georg Müller1, Jeng-Min Chiou2 and Xiaoyan Leng*
Background: 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. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other.
翻译：时间基因表达谱刻画了特定基因的时间动态表达，在目前的基因试验中经常收集到。在实验分析中，在整个生命周期中，得到基因表达。对不同发展阶段的基因表达模式的关系很感兴趣，以研究长期基因调控发展中的模式。我们使用函数型数据分析来研究不同阶段时间基因表达谱互相之间的关系。
相关表达：Temporal gene expression profiles
characterize， increasingly collected in current gene expression experiments，relating A to B
Results: We 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.
证明了函数型回归可以表明存在于时间基因表达谱之间的关系，并且包含金降维。使用这些工具，父代与子代的基因表达在胚胎期相同的基因上具有强相关。进一步的，可以区分基因组，展示胚胎期与后来生命期的正或负的相关关系。特别的，找到了与肌肉发展相关的基因表达的正的关系。以及，果蝇的严格母系基因一个负的相关的
规范表达积累：pinpoint ,incorporat,moreover,
Conclusion: Our 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.
Background
Biological motivation and overview生物学的动机和综述
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. In higher organisms, with very few exceptions, all of the different types of cell possess the same genes; however each type of cell only expresses a unique set of "signature" genes at a certain time, depending on current develop-mental tasks [1]. Different life stages of an organism are thought to share the same or similar set of "signature"
genes, which thus play a role throughout ontogenesis. For example, there are two phases of somatic muscle formation in the development of Drosophila melanogaster. The first phase of myogenesis occurs during embryonic development and generates larval muscle elements that medi-ate the relatively simple behaviors of the larva. During pupal metamorphosis, a second phase of myogenesis gen-erates a diverse pattern of muscle fibers, facilitating the more complex behaviors of the adult fly [2].
----------------------------------------------------------------------------------------------------------------------
Key features and relevance of functional regression
The developmental gene-specific expression time courses are viewed as being generated by an underlying smooth random trajectory which is specific to each gene. These trajectories are sampled at a grid of measurement points during each life cycle phase, e.g., s1,...,sp, where p = 31 for the measurements of gene expression during the embryonal period. If we denote the embryonal phase predictor trajectories by Xi(s), where i is a gene index, then the observed data for the embryonal gene expression are Xij =Xi(sij) + eij, where the eij are measurement errors which are assumed to be independent, with zero mean and finite variance.
基因表达实践过程的发展通常认为是由潜在的光滑轨迹产生的，对每个基因都有一条光滑随机的轨迹。在每个生命阶段，这些轨迹在测量的格子点上被抽样出来，
通过函数型回归分析推断基因表达动态
Hans-Georg Müller1, Jeng-Min Chiou2 and Xiaoyan Leng*
Background: 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. We use tools from functional data analysis to study dynamic changes by relating temporal gene expression profiles of different developmental stages to each other.
翻译：时间基因表达谱刻画了特定基因的时间动态表达，在目前的基因试验中经常收集到。在实验分析中，在整个生命周期中，得到基因表达。对不同发展阶段的基因表达模式的关系很感兴趣，以研究长期基因调控发展中的模式。我们使用函数型数据分析来研究不同阶段时间基因表达谱互相之间的关系。
相关表达：Temporal gene expression profiles
characterize， increasingly collected in current gene expression experiments，relating A to B
Results: We 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.
证明了函数型回归可以表明存在于时间基因表达谱之间的关系，并且包含金降维。使用这些工具，父代与子代的基因表达在胚胎期相同的基因上具有强相关。进一步的，可以区分基因组，展示胚胎期与后来生命期的正或负的相关关系。特别的，找到了与肌肉发展相关的基因表达的正的关系。以及，果蝇的严格母系基因一个负的相关的
规范表达积累：pinpoint ,incorporat,moreover,
Conclusion: Our 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.
Background
Biological motivation and overview生物学的动机和综述
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. In higher organisms, with very few exceptions, all of the different types of cell possess the same genes; however each type of cell only expresses a unique set of "signature" genes at a certain time, depending on current develop-mental tasks [1]. Different life stages of an organism are thought to share the same or similar set of "signature"
genes, which thus play a role throughout ontogenesis. For example, there are two phases of somatic muscle formation in the development of Drosophila melanogaster. The first phase of myogenesis occurs during embryonic development and generates larval muscle elements that medi-ate the relatively simple behaviors of the larva. During pupal metamorphosis, a second phase of myogenesis gen-erates a diverse pattern of muscle fibers, facilitating the more complex behaviors of the adult fly [2].
----------------------------------------------------------------------------------------------------------------------
Key features and relevance of functional regression
The developmental gene-specific expression time courses are viewed as being generated by an underlying smooth random trajectory which is specific to each gene. These trajectories are sampled at a grid of measurement points during each life cycle phase, e.g., s1,...,sp, where p = 31 for the measurements of gene expression during the embryonal period. If we denote the embryonal phase predictor trajectories by Xi(s), where i is a gene index, then the observed data for the embryonal gene expression are Xij =Xi(sij) + eij, where the eij are measurement errors which are assumed to be independent, with zero mean and finite variance.
基因表达实践过程的发展通常认为是由潜在的光滑轨迹产生的，对每个基因都有一条光滑随机的轨迹。在每个生命阶段，这些轨迹在测量的格子点上被抽样出来，
