ArticlePLoS computational biology2020
Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Analysis and visualization of expression patterns with fuzzy sets as FlowSets.NAR genomics and bioinformatics · 2026Article
- New biological insights into osteosarcoma-lessons from single cell sequencing studies.Cancer metastasis reviews · 2026Review
- TemporalVAE: atlas-assisted temporal mapping of time-series single-cell transcriptomes during embryogenesis.Nature cell biology · 2025Article
- scHSC: enhancing single-cell RNA-seq clustering via hard sample contrastive learning.Briefings in bioinformatics · 2025Article
- Sceptic: pseudotime analysis for time-series single-cell sequencing and imaging data.Genome biology · 2025Article
- Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data.Briefings in bioinformatics · 2025Article
- Single-cell map of innate-like lymphocyte response toiScience · 2025Article
- Single-cell RNA sequencing identifies CXADR as a fate determinant of the placental exchange surface.Nature communications · 2025Article
- Identification of Critical States in Complex Biological Systems Using Cell-Specific Causal Network Entropy.Research (Washington, D.C.) · 2025Article
- Optimized network inference for immune diseased single cells.Frontiers in immunology · 2025Article
- The frequency of CD38Nature communications · 2024Article
- Joint trajectory inference for single-cell genomics using deep learning with a mixture prior.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Recovering biomolecular network dynamics from single-cell omics data requires three time points.NPJ systems biology and applications · 2024Article
- Cellular proliferation biases clonal lineage tracing and trajectory inference.Bioinformatics (Oxford, England) · 2024Article
- CD38+ Alveolar macrophages mediate early control of M. tuberculosis proliferation in the lung.Research square · 2024Article
- An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.Fundamental research · 2024Article
- Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon.Nature methods · 2024Article
- Integrating patients in time series clinical transcriptomics data.Bioinformatics (Oxford, England) · 2024Article
- Modeling single cell trajectory using forward-backward stochastic differential equations.PLoS computational biology · 2024Article
- Studying temporal dynamics of single cells: expression, lineage and regulatory networks.Biophysical reviews · 2024Review
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2 authors.
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Abstract
Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, most of these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNA-seq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNA-seq data. In performance comparison tests, Tempora inferred known developmental lineages from three diverse tissue development time series data sets, beating state of the art methods in accuracy and speed. Tempora works at the level of cell clusters (types) and uses biological pathway information to help identify cell type relationships. This approach increases gene expression signal from single cells, processing speed, and interpretability of the inferred trajectory. Our results demonstrate the utility of a combination of time and pathway information to supervise trajectory inference for scRNA-seq based analysis.
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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.