Abstract：
Single-cell transcriptomics allows unbiased characterization of cell heterogeneity in a sample by profiling gene expression at single-cell level. These profiles capture snapshots of transient or steady states in dynamic processes, such as cell cycle, activation, or differentiation, which can be computationally ordered into a \"flip-book\" of cell development using trajectory inference methods. However, prediction of more complex topology structures, such as multifurcations or trees, remains challenging. In this chapter, we present two user-friendly protocols for inferring tree-shaped single-cell trajectories and pseudotime from single-cell transcriptomics data with Totem. Totem is a trajectory inference method that offers flexibility in inferring both nonlinear and linear trajectories and usability by avoiding the cumbersome fine-tuning of parameters. The QuickStart protocol provides a simple and practical example, whereas the GuidedStart protocol details the analysis step-by-step. Both protocols are demonstrated using a case study of human bone marrow CD34+ cells, allowing the study of the branching of three lineages: erythroid, lymphoid, and myeloid. All the analyses can be fully reproduced in Linux, macOS, and Windows operating systems (amd64 architecture) with >8 Gb of RAM using the provided docker image distributed with notebooks, scripts, and data in Docker Hub (elolab/repro-totem-ti). These materials are shared online under open-source license at https://elolab.github.io/Totem-protocol .
摘要：
单细胞转录组学允许通过在单细胞水平上分析基因表达来无偏表征样品中的细胞异质性。这些配置文件捕获动态过程中的瞬态或稳态快照，如细胞周期，激活,或差异化，可以使用轨迹推理方法将其计算排序为细胞发育的“翻转书”。然而,预测更复杂的拓扑结构，如分叉或树木，仍然具有挑战性。在这一章中,我们提出了两种用户友好的协议，用于从Totem的单细胞转录组学数据中推断树形单细胞轨迹和伪时间。图腾是一种轨迹推理方法，通过避免繁琐的参数微调，提供了推断非线性和线性轨迹的灵活性和可用性。QuickStart协议提供了一个简单而实用的示例，而GuidedStart方案详细说明了一步一步的分析。两种方案都是使用人类骨髓CD34+细胞的案例研究来证明的，允许研究三个谱系的分支：红系，淋巴样,和骨髓。所有的分析都可以在Linux中完全复制，macOS,和具有>8GbRAM的Windows操作系统（amd64体系结构）使用提供的与笔记本一起分发的Docker映像，脚本,和DockerHub中的数据(elolab/repro-totem-ti)。这些材料在https://elolab的开源许可下在线共享。github.io/图腾协议。
