Learning dynamic image representations for self-supervised cell cycle annotation
Learning dynamic image representations for self-supervised cell cycle annotation
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DOI：
10.1101/2023.05.30.542796
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Kristina Ulicna;M. Kelkar;Christopher J. Soelistyo;G. Charras;Alan R. Lowe
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文献类型：
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作者：
Kristina Ulicna;M. Kelkar;Christopher J. Soelistyo;G. Charras;Alan R. Lowe
基于时间的单细胞轨迹的比较是具有挑战性的，由于其内在的异质性，自主决策，动态过渡和不等长度。在本文中，我们提出了一个自我监督的框架相结合的图像自动编码器与动态时间序列分析的潜在特征空间来表示，比较和注释细胞周期阶段的整个细胞轨迹。在我们完全数据驱动的方法中，我们映射异质细胞轨迹之间的相似性，并生成单细胞轨迹相持续时间，起始和过渡的统计表示。这项工作是第一次尝试将细胞周期特异性报告者的学习图像表示序列转换为无监督序列注释。
Time-based comparisons of single-cell trajectories are challenging due to their intrinsic heterogeneity, autonomous decisions, dynamic transitions and unequal lengths. In this paper, we present a self-supervised framework combining an image autoencoder with dynamic time series analysis of latent feature space to represent, compare and annotate cell cycle phases across singlecell trajectories. In our fully data-driven approach, we map similarities between heterogeneous cell tracks and generate statistical representations of single-cell trajectory phase durations, onset and transitions. This work is a first effort to transform a sequence of learned image representations from cell cycle-specific reporters into an unsupervised sequence annotation.