@inbook{576432708ddd42879d494e3611f0622c,
title = "Inferring Tree-Shaped Single-Cell Trajectories with Totem",
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 .",
keywords = "Single-Cell Analysis/methods, Humans, Software, Gene Expression Profiling/methods, Computational Biology/methods, Transcriptome, Cell Lineage/genetics, Algorithms, Cell Differentiation, Bioinformatics, Tree-shaped topology, Trajectory inference, Totem, Single-cell RNA-seq, Pseudotime, Data analysis, Cell connectivity",
author = "Sousa, {Ant{\'o}nio G G} and Johannes Smolander and Sini Junttila and Elo, {Laura L}",
note = "{\textcopyright} 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2024",
month = jul,
day = "28",
doi = "10.1007/978-1-0716-3886-6_9",
language = "English",
isbn = "978-1-0716-3885-9",
volume = "2812",
series = "Methods in molecular biology (Clifton, N.J.)",
publisher = "Humana press",
pages = "169--191",
booktitle = "Transcriptome Data Analysis",
address = "United States",
}