ArticleNucleic acids research2022
Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 32 citations in OpenAlex.
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- From static definitions to dynamic landscapes: physiological states in yeast.Frontiers in microbiology · 2026Article
- Cell lineage tracing: Methods, applications, and challenges.Quantitative biology (Beijing, China) · 2025Review
- Decrypting cancer's spatial code: from single cells to tissue niches.Molecular oncology · 2025Review
- Inferring causal trajectories from spatial transcriptomics using CASCAT.Nucleic acids research · 2025Article
- CeiTEA: Adaptive Hierarchy of Single Cells with Topological Entropy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data.Biology · 2025Article
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.Nature reviews. Molecular cell biology · 2025Review
- scGRN-Entropy: Inferring cell differentiation trajectories using single-cell data and gene regulation network-based transfer entropy.PLoS computational biology · 2024Article
- StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases.Genome biology · 2024Article
- Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation.Cell reports methods · 2024Article
- BEST4American journal of physiology. Cell physiology · 2024Review
- scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier.Nature communications · 2023Article
- Integrative Analysis of Machine Learning and Molecule Docking Simulations for Ischemic Stroke Diagnosis and Therapy.Molecules (Basel, Switzerland) · 2023Article
- Reversing pathological cell states: the road less travelled can extend the therapeutic horizon.Trends in cell biology · 2023Review
- Cell-connectivity-guided trajectory inference from single-cell data.Bioinformatics (Oxford, England) · 2023Article
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2 authors at 1 institution in 1 country.
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Abstract
Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing the cell-state manifold and cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-Lab/Margaret) for inferring single-cell trajectory and fate mapping for diverse dynamic cellular processes. MARGARET reconstructs complex trajectory topologies using a deep unsupervised metric learning and a graph-partitioning approach based on a novel connectivity measure, automatically detects terminal cell states, and generalizes the quantification of fate plasticity for complex topologies. On a diverse benchmark consisting of synthetic and real datasets, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. For human hematopoiesis, MARGARET accurately identified all major lineages and associated gene expression trends and helped identify transitional progenitors associated with key branching events. For embryoid body differentiation, MARGARET identified novel transitional populations that were validated by bulk sequencing and functionally characterized different precursor populations in the mesoderm lineage. For colon differentiation, MARGARET characterized the lineage for BEST4/OTOP2 cells and the heterogeneity in goblet cell lineage in the colon under normal and inflamed ulcerative colitis conditions. Finally, we demonstrated that MARGARET can scale to large scRNA-seq datasets consisting of ∼ millions of cells.
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