ArticleCurrent opinion in systems biology2021
Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate.
Article in Current opinion in systems biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data.Nature biotechnology · 2026Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Heterokairic Genes and the Eco-Evo-Devo of Timing.Evolution & development · 2026Review
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- Inferring cell differentiation maps from lineage tracing data.Nature methods · 2026Article
- Transcriptome-wide association studies at cell-state level using single-cell eQTL data.Cell genomics · 2026Article
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- stTrace: detecting spatial-temporal domains from spatial transcriptome to trace developmental path.Briefings in bioinformatics · 2025Article
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks.Briefings in bioinformatics · 2024Article
- A review of single-cell transcriptomics and epigenomics studies in maternal and child health.Epigenomics · 2024Review
- DNA Barcoding Technology for Lineage Recording and Tracing to Resolve Cell Fate Determination.Cells · 2023Review
- Review
- Studying stochastic systems biology of the cell with single-cell genomics data.bioRxiv : the preprint server for biology · 2023Article
- Computational Approaches and Challenges in Spatial Transcriptomics.Genomics, proteomics & bioinformatics · 2023Review
- Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics.Frontiers in network physiology · 2023Review
- Advancing root developmental research through single-cell technologies.Current opinion in plant biology · 2022Review
- TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data.Interdisciplinary sciences, computational life sciences · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Rapid technological advances in transcriptomics and lineage tracing technologies provide new opportunities to understand organismal development at the single-cell level. Building on these advances, various computational methods have been proposed to infer developmental trajectories and to predict cell fate. These methods have unveiled previously uncharacterized transitional cell types and differentiation processes. Importantly, the ability to recover cell states and trajectories has been evolving hand-in-hand with new technologies and diverse experimental designs; more recent methods can capture complex trajectory topologies and infer short- and long-term cell fate dynamics. Here, we summarize and categorize the most recent and popular computational approaches for trajectory inference based on the information they leverage and describe future challenges and opportunities for the development of new methods for reconstructing differentiation trajectories and inferring cell fates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.