Interdisciplinary Sciences: Computational Life Sciences ( IF 3.9 ) Pub Date : 2021-06-09 , DOI: 10.1007/s12539-021-00445-4 Jiang Xie 1 , Yiting Yin 1 , Jiao Wang 2
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Single-cell RNA-seq technology provides an unprecedented opportunity to allow researchers to study the biological heterogeneity during cell differentiation and development with higher resolution. Although many computational methods have been proposed to infer cell lineages from single-cell RNA-seq data, constructing accurate cell trajectories remains a challenge. We develop a novel trajectory inference method-based probability distribution (TIPD) to describe the heterogeneity of cell population. TIPD combines signalling entropy and clustering results of the gene expression profile to describe the probability distributions of heterogeneous states in a cell population. It does not require external knowledge to determine the direction of the differentiation trajectories, so its application is not limited by the annotations of the data set. We also propose a new distance metric to measure the distance of the probability distributions of the identified heterogeneous states. On this distance matrix, a minimum spanning tree (MST) is built to reorganize the order of cell clusters. The constructed MST is calculated based on systems-level information, so it is consistent with the real biological process. We validated our method on four previously published single-cell RNA-seq data sets including the linear structure and branch structure. The results showed that TIPD successfully reconstructed the differentiation trajectories that are highly consistent with the known differentiation trajectories and outperformed the other four state-of-the-art methods under different assessment criteria.
Graphic Abstract
中文翻译：
TIPD：基于概率分布的单细胞 RNA-Seq 数据轨迹推断方法
单细胞 RNA-seq 技术提供了前所未有的机会，使研究人员能够以更高的分辨率研究细胞分化和发育过程中的生物异质性。尽管已经提出了许多计算方法来从单细胞 RNA-seq 数据推断细胞谱系，但构建准确的细胞轨迹仍然是一个挑战。我们开发了一种新的基于轨迹推断方法的概率分布 (TIPD) 来描述细胞群的异质性。TIPD 结合基因表达谱的信号熵和聚类结果来描述细胞群中异质状态的概率分布。它不需要外部知识来确定微分轨迹的方向，因此其应用不受数据集注释的限制。我们还提出了一种新的距离度量来测量已识别异构状态的概率分布的距离。在这个距离矩阵上，构建了一个最小生成树（MST）来重新组织细胞簇的顺序。构建的 MST 是基于系统级信息计算的，因此与真实的生物过程一致。我们在四个先前发布的单细胞 RNA-seq 数据集上验证了我们的方法，包括线性结构和分支结构。结果表明，TIPD成功重建了与已知分化轨迹高度一致的分化轨迹，并在不同的评估标准下优于其他四种最先进的方法。
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