基于高斯分布的种群级细胞轨迹推断。

IF 4.8 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Biomolecules Pub Date : 2024-11-01 DOI:10.3390/biom14111396
Xiang Chen, Yibing Ma, Yongle Shi, Yuhan Fu, Mengdi Nan, Qing Ren, Jie Gao
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引用次数: 0

摘要

在过去十年中,从单细胞数据中推断发育轨迹已成为生物信息学领域的一项重大挑战。结合了方向动力学的 RNA 速度极大地推动了单细胞轨迹的研究。然而,随着单细胞 RNA 测序技术的发展,它产生了复杂的高维数据和高噪声水平。现有的轨迹推断方法忽略了细胞分布特征,在这种情况下可能表现不佳。为了解决这个问题,我们推出了基于高斯分布的轨迹推断方法 CPvGTI。CPvGTI 利用期望最大化算法优化的高斯混合模型,在原始数据空间中构建新的细胞群。通过整合 RNA 速度,CPvGTI 利用高斯过程回归分析这些细胞群的分化轨迹。为了评估 CPvGTI 的性能,我们使用四个结构不同的模拟数据集和四个真实数据集评估了 CPvGTI 的性能,并与几种最先进的方法进行了比较。模拟研究表明,与现有方法相比,CPvGTI 在伪时间预测和结构重建方面表现出色。此外,在人类前脑和小鼠造血数据集中发现的新分支轨迹也证实了 CPvGTI 的卓越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Population-Level Cell Trajectory Inference Based on Gaussian Distributions.

In the past decade, inferring developmental trajectories from single-cell data has become a significant challenge in bioinformatics. RNA velocity, with its incorporation of directional dynamics, has significantly advanced the study of single-cell trajectories. However, as single-cell RNA sequencing technology evolves, it generates complex, high-dimensional data with high noise levels. Existing trajectory inference methods, which overlook cell distribution characteristics, may perform inadequately under such conditions. To address this, we introduce CPvGTI, a Gaussian distribution-based trajectory inference method. CPvGTI utilizes a Gaussian mixture model, optimized by the Expectation-Maximization algorithm, to construct new cell populations in the original data space. By integrating RNA velocity, CPvGTI employs Gaussian Process Regression to analyze the differentiation trajectories of these cell populations. To evaluate the performance of CPvGTI, we assess CPvGTI's performance against several state-of-the-art methods using four structurally diverse simulated datasets and four real datasets. The simulation studies indicate that CPvGTI excels in pseudo-time prediction and structural reconstruction compared to existing methods. Furthermore, the discovery of new branch trajectories in human forebrain and mouse hematopoiesis datasets confirms CPvGTI's superior performance.

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来源期刊
Biomolecules
Biomolecules Biochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
9.40
自引率
3.60%
发文量
1640
审稿时长
18.28 days
期刊介绍: Biomolecules (ISSN 2218-273X) is an international, peer-reviewed open access journal focusing on biogenic substances and their biological functions, structures, interactions with other molecules, and their microenvironment as well as biological systems. Biomolecules publishes reviews, regular research papers and short communications.  Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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