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Alignment of time-resolved data from high throughput experiments

机译:从高吞吐量实验中解析数据的对齐

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To better understand the dynamics of the underlying processes in cells, it is necessary to take measurements over a time course. Modern high-throughput technologies are often used for this purpose to measure the behavior of cell products like metabolites, peptides, proteins, μRNA or mRNA at different points in time. Compared to classical time series, the number of time points is usually very limited and the measurements are taken at irregular time intervals. The main reasons for this are the costs of the experiments and the fact that the dynamic behavior usually shows a strong reaction and fast changes shortly after a stimulus and then slowly converges to a certain stable state. Another reason might simply be missing values. It is common to repeat the experiments and to have replicates in order to carry out a more reliable analysis. The ideal assumptions that the initial stimulus really started exactly at the same time for all replicates and that the replicates are perfectly synchronized are seldom satisfied. Therefore, there is a need to first adjust or align the time-resolved data before further analysis is carried out. Dynamic time warping (DTW) is considered as one of the common alignment techniques for time series data with equidistant time points. In this paper, we modified the DTW algorithm so that it can align sequences with measurements at different, non-equidistant time points with large gaps in between. This type of data is usually known as time-resolved data characterized by irregular time intervals between measurements as well as non-identical time points for different replicates. This new algorithm can be easily used to align time-resolved data from high-throughput experiments and to come across existing problems such as time scarcity and existing noise in the measurements. We propose a modified method of DTW to adapt requirements imposed by time-resolved data by use of monotone cubic interpolation splines. Our presented approach provides a nonlinear
机译:为了更好地了解细胞中底层过程的动态,有必要在时间过程中进行测量。现代化的高通量技术通常用于该目的,以测量细胞产物的行为,如代谢物,肽,蛋白质,μRNA或mRNA在不同的时间点。与古典时间序列相比,时间点的数量通常非常有限,并且测量以不规则的时间间隔进行。这是实验的主要原因以及动态行为通常在刺激后不久显示出强烈反应和快速变化的事实,然后缓慢地收敛到某个稳定状态。另一个原因可能只是缺少值。重复实验是常见的,并且具有复制以进行更可靠的分析。初始刺激真正开始的理想假设与所有复制相同的时间,并且复制完全同步是很少的满意。因此,在进行进一步分析之前,需要首先调整或对准时间分辨的数据。动态时间翘曲(DTW)被认为是具有等距时间点的时间序列数据的公共对准技术之一。在本文中,我们修改了DTW算法,使得它可以在不同的非等距时间点与介于之间的不同,非等距时间点进行对准序列。这种类型的数据通常称为时间分辨的数据,其特征在于测量之间的不规则时间间隔以及不同复制的非相同时间点。这种新算法可以很容易地用于从高吞吐量实验对准时间解析的数据,并遇到现有问题,例如测量中的时间稀缺和现有的噪声。我们提出了一种修改的DTW方法,通过使用单调立方插值样条曲目来调整时间解析数据所施加的要求。我们所提出的方法提供了非线性

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