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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:为了更好地理解细胞内潜在过程的动力学,有必要在一段时间内进行测量。现代高通量技术通常用于测量细胞产物(如代谢物、肽、蛋白质、μRNA或mRNA)在不同时间点的行为。与经典时间序列相比,时间点的数量通常非常有限,测量是在不规则的时间间隔进行的。其主要原因是实验的成本,以及动态行为通常表现出强烈的反应,在刺激后不久迅速变化,然后缓慢收敛到某个稳定状态。另一个原因可能只是缺少值。为了进行更可靠的分析,重复实验和复制是很常见的。对于所有的复制,初始刺激确实在同一时间开始,并且复制完全同步的理想假设很少得到满足。因此,在进行进一步分析之前,需要首先调整或校准时间分辨数据。动态时间扭曲(DTW)被认为是等距时间点时间序列数据的常用对齐技术之一。在本文中,我们对DTW算法进行了修改,使其能够在不同的、非等距的时间点(时间间隔较大)将序列与测量值对齐。这类数据通常被称为时间分辨数据,其特征是测量之间的时间间隔不规则,以及不同重复的时间点不相同。这种新算法可以很容易地用于对齐高通量实验中的时间分辨数据,并遇到测量中存在的时间稀缺和噪声等问题。我们提出了一种改进的DTW方法,通过使用单调三次插值样条来适应时间分辨数据的要求。我们提出的方法提供了一种非线性

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