Reversed graph embedding resolves complex single-cell trajectories
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Abstract
Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.
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Cicero performs cell clustering on scATAC-seq trajectories constructed with an adaptation of Monocle 2, determines co-accessible regions within each group, and infers chromatin hubs by taking into account correlation between nearby regions.
“Similarly, Cicero [64] performs cell clustering on scATAC-seq trajectories (constructed with an adaptation of Monocle 2 [65]), determines co-accessible regions within each of these groups and finally infers chromatin hubs by taking into account the correlation between nearby regions.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [65]
Monocle2 pseudotime was strongly correlated with the maturation trajectory, with Pearson and Spearman R=0.94.
“Monocle2 pseudotime was strongly correlated with our maturation trajectory (both pearson and spearman R=0.94).”
From Developmental diversification of cortical inhibitory interneurons (2018) · cites this paper as [13]
Very similar maturation trajectories were obtained using PCA or reverse graph embedding.
“We obtained very similar trajectories using approaches based on PCA or reverse graph embedding (Extended Data Fig. 1)13”
From Developmental diversification of cortical inhibitory interneurons (2018) · cites this paper as [13]
Existing computational methods for predicting lineage trajectories generally rely on a priori knowledge of the starting point (and thus direction) of the inferred biological process.
“Although a large number of computational methods for predicting lineage trajectories have been described, they generally rely upon (i) a priori knowledge of the starting point (and thus, direction) of the inferred biological process (6, 7)”
From Single-cell transcriptional diversity is a hallmark of developmental potential (2020) · cites this paper as [7]
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- Authors
- Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C.
- Affiliations
- University of Washington (US)
- Shanghai Jiao Tong University (CN)
- University of Illinois Chicago (US)
- Corresponding author
- Trapnell, Cole
- Venue
- Nature Methods
- Published
- 2017-08-21
- Volume
- 14
- Issue
- 10
- Pages
- 979-982
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC5764547
- OpenAlex
- W2747877289
- Cited by
- 5,070
- References
- 43
- Citations by year
- 2026: 763 · 2025: 898 · 2024: 889 · 2023: 727 · 2022: 598 · 2021: 464 · 2020: 312 · 2019: 255 · 2018: 131 · 2017: 24 · 2014: 2 · 2012: 1
- FWCI
- 81.28
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Alfred P. Sloan Foundation
- National Institutes of Health
- National Institute on Aging (P50AG005136)
- National Institute of Diabetes and Digestive and Kidney Diseases (U54DK107979)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP2HD088158)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell trajectory analysis, cell fate determination, blood development, gene expression regulation
- MeSH terms
- Algorithms, Animals, Cell Differentiation, Computer Simulation, Models, Biological, Mutation, Transcription Factors, Gene Expression Regulation, Developmental, Transcriptome
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