Inferring Tree-Shaped Single-Cell Trajectories with Totem

António G G Sousa, Johannes Smolander, Sini Junttila, Laura L Elo

Research output: Chapter in Book/Conference proceedingChapterScientificpeer-review

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 .

Original languageEnglish
Title of host publication Transcriptome Data Analysis
PublisherHumana press
Pages169-191
Number of pages23
Volume2812
ISBN (Electronic)978-1-0716-3886-6
ISBN (Print)978-1-0716-3885-9
DOIs
Publication statusPublished - 28 Jul 2024
MoE publication typeA3 Part of a book or another research book

Publication series

NameMethods in molecular biology (Clifton, N.J.)
ISSN (Print)1064-3745

Keywords

  • Single-Cell Analysis/methods
  • Humans
  • Software
  • Gene Expression Profiling/methods
  • Computational Biology/methods
  • Transcriptome
  • Cell Lineage/genetics
  • Algorithms
  • Cell Differentiation

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