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通过高斯过程模型从破坏性时间序列实验中解码潜在行为Decoding underlying behaviour from destructive time series experiments through Gaussian process models |
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| 课程网址: | http://videolectures.net/licsb2010_honkela_dub/ |
| 主讲教师: | Antti Honkela |
| 开课单位: | 阿尔托大学 |
| 开课时间: | 2010-05-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A major problem for biological time series is that often experiments (such as gene expression measurements using microarrays or RNA-seq) require the organism or cells to be destroyed. This means that a particular time series is often a series of measurements of different organisms (or batches of cells) at different times. Biological replicates normally consist of a separate biological sample measured at the same time. With the advent of single cell expression experiments, where it is not currently conceivable to make genome-wide gene expression measurements without destroying the cell, we expect such set ups to be sustained. Many existing approaches to modelling transcriptional data postulate a differential equation model for continuous-time expression profiles from which the repeated observations arise. Two ways of modelling repeat experiments would be either to handle repeated observations as being from a shared profile, or from completely independent profiles. The former approach assumes that gene expression profile for each experiment does not vary, whilst the latter approach assumes no relationship between the gene expression profiles. For many experimental set ups we might expect something in between these two extremes where, whilst each individual measurement comes from a different collection of cells or a different organism, the experimental set up is broadly the same. We therefore expect some shared affects and some independent affects for the experiments. In this work we propose an integrated Gaussian process framework for analysis of such experiments. In our approach, independent aspects of the experiments are modelled as independent Gaussian process draws, while the common profile across the experiments is modelled by a separate Gaussian process. The method adds power through sharing of replicates for the common profile while being robust to outliers from individual rogue experiments. |
| 关 键 词: | 时间序列; 单细胞; 转录数据建模 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-14：lxf |
| 阅读次数: | 54 |