ArticleNAR genomics and bioinformatics2020
Untangling biological factors influencing trajectory inference from single cell data.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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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
12 citing papers in PubMed.
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- A novel paradigm for single-cell annotation in stem cell research.Stem cell reports · 2025Review
- Dynamic modelling of cell cycle arrest through integrated single-cell and mathematical modelling approaches.PLoS computational biology · 2025Article
- Population-Level Cell Trajectory Inference Based on Gaussian Distributions.Biomolecules · 2024Article
- Eomes expression identifies the early bone marrow precursor to classical NK cells.Nature immunology · 2024Article
- DELVE: feature selection for preserving biological trajectories in single-cell data.Nature communications · 2024Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.Briefings in bioinformatics · 2024Article
- Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference.bioRxiv : the preprint server for biology · 2023Article
- Feature selection for preserving biological trajectories in single-cell data.bioRxiv : the preprint server for biology · 2023Article
- Density-based detection of cell transition states to construct disparate and bifurcating trajectories.Nucleic acids research · 2022Article
- Severe testing with high-dimensional omics data for enhancing biomedical scientific discovery.NPJ systems biology and applications · 2022Article
- RNA velocity unraveled.PLoS computational biology · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in single-cell RNA sequencing over the past decade has shifted the discussion of cell identity toward the transcriptional state of the cell. While the incredible resolution provided by single-cell RNA sequencing has led to great advances in unraveling tissue heterogeneity and inferring cell differentiation dynamics, it raises the question of which sources of variation are important for determining cellular identity. Here we show that confounding biological sources of variation, most notably the cell cycle, can distort the inference of differentiation trajectories. We show that by factorizing single cell data into distinct sources of variation, we can select a relevant set of factors that constitute the core regulators for trajectory inference, while filtering out confounding sources of variation (e.g. cell cycle) which can perturb the inferred trajectory. Script are available publicly on https://github.com/mochar/cell_variation.
Identifiers
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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.