ArticleNature protocols2020
FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets.
Article in Nature protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Navigating the data processing for cytometry-based single-cell proteomics.Nature protocols · 2026Review
- Resident memory T cell development is gradual and shows AP-1 gene expression in mature cells.JCI insight · 2025Article
- Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq.GigaScience · 2025Article
- The tidyomics ecosystem: enhancing omic data analyses.Nature methods · 2024Article
- ThebioRxiv : the preprint server for biology · 2024Article
- Fetal brain response to maternal inflammation requires microglia.Development (Cambridge, England) · 2024Article
- Article
- tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.Bioinformatics advances · 2023Article
- Article
- NK cell receptor and ligand composition influences the clearance of SARS-CoV-2.The Journal of clinical investigation · 2021Article
- Generalized and scalable trajectory inference in single-cell omics data with VIA.Nature communications · 2021Article
- CD8Journal for immunotherapy of cancer · 2021Article
- Machine Learning Approaches on High Throughput NGS Data to Unveil Mechanisms of Function in Biology and Disease.Cancer genomics & proteomicsReview
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Authors and funding
8 authors.
Funding
Abstract
High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.
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