ArticleFundamental research2024
An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.
Article in Fundamental research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Simultaneous modeling of chromatin conformation changes from multiple single-cell interaction maps with ChromMovie.Genome research · 2026Article
- Multi-omics integrative analysis reveals novel genetic loci and candidate genes for ischemic stroke.Molecular therapy. Nucleic acids · 2025Article
- Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.Briefings in bioinformatics · 2025Review
- DualNetM: an adaptive dual network framework for inferring functional-oriented markers.BMC biology · 2025Article
- ChromMovie: A Molecular Dynamics Approach for Simultaneous Modeling of Chromatin Conformation Changes from Multiple Single-Cell Hi-C Maps.bioRxiv : the preprint server for biology · 2025Article
- Alternative splicing dynamics during gastrulation in mouse embryo.Scientific reports · 2025Article
- Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis.IET systems biology · 2024Article
- Inference and analysis of cell-cell communication of non-myeloid circulating cells in late sepsis based on single-cell RNA-seq.IET systems biology · 2024Article
- A composite scaling network of EfficientNet for improving spatial domain identification performance.Communications biology · 2024Article
- Genetic inference and single cell expression analysis of potential targets in heart failure and breast cancer.Journal of cancer research and clinical oncology · 2024Article
- Bioinformatics and Biomedical Computing.Fundamental research · 2024Article
- Transcriptome Analyses Reveal the Important miRNAs Involved in Immune Response of Gastric Cancer.IET systems biologyArticle
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6 authors.
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Abstract
The increasing emergence of the time-series single-cell RNA sequencing (scRNA-seq) data, inferring developmental trajectory by connecting transcriptome similar cell states (i.e., cell types or clusters) has become a major challenge. Most existing computational methods are designed for individual cells and do not take into account the available time series information. We present IDTI based on the Increment of Diversity for Trajectory Inference, which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq data. We apply IDTI to simulated and three real diverse tissue development datasets, and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching accuracy. The results have shown that the IDTI method accurately constructs the cell state trajectory without the requirement of starting cells. In the performance test, we further demonstrate that IDTI has the advantages of high accuracy and strong robustness.
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