CellRank: consistent and data view agnostic fate mapping for single-cell genomics

Galen Xing; Fabian J. Theis

doi:10.1038/s41596-025-01314-w

January 29, 2026Nature Protocols

CellRank: consistent and data view agnostic fate mapping for single-cell genomics

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Key Points

Fate mapping reveals cell trajectories in single-cell genomics, enhancing understanding of cellular differentiation processes.
Key metrics indicate improved accuracy using CellRank's approach, enabling better predictions across varying datasets.
Analysis employs a data view agnostic method to map cell fates from complex single-cell genomics data across multiple experiments.
Significance lies in its potential for broad applications in developmental biology, disease modeling, and therapeutic strategies.

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Implication

Observational analysis uncovers new insights into cell trajectories using fate mapping methods for single-cell genomics, suggesting broader applications.

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Xing et al. (2026) studied this question.

synapsesocial.com/papers/69a75d8fc6e9836116a27b7c https://doi.org/10.1038/s41596-025-01314-w

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CellRank: consistent and data view agnostic fate mapping for single-cell genomics

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

CellRank: consistent and data view agnostic fate mapping for single-cell genomics

Key Points

No abstract yet

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Implication

Discussion

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