ArticleNature biotechnology2017
Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development.
Article in Nature biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 1 of them a synthesis that pooled it.
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Who cites it
118 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of circulating long noncoding RNAs in hepatocellular carcinoma: a systematic review and meta-analysis.BMC gastroenterology · 2026Pooled it
- TopClust: Topological-based analysis of scRNA-seq data for data-driven identification of clusters and core cluster cells.Biophysical journal · 2026Article
- The Prospective Regulatory Functions of lncRNAs and Their ceRNA Networks in the Development of Motor Neurons and Associated Diseases.Biomolecules · 2026Review
- Top-Down versus Bottom-Up Proteomics in Highly Sensitive LC-MS-Based Profiling of Limited Samples.Analytical chemistry · 2026Article
- Topological Data Analysis for Unsupervised Feature Selection in Large Scale Spatial Omics Data Sets.Bulletin of mathematical biology · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- A Faithful Discretization of Verbose Directional Transforms.Discrete & computational geometry · 2026Article
- Cell-type-specific alkaloid and terpenoid biosynthesis in glandular trichomes: single-cell and spatial transcriptomic perspectives.Frontiers in plant science · 2026Review
- A distribution-guided Mapper algorithm.BMC bioinformatics · 2025Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- Enhancer Dynamics and Spatial Organization Drive Anatomically Restricted Cellular States in the Human Spinal Cord.bioRxiv : the preprint server for biology · 2025Article
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- The topology and geometry of neural representations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Topological and geometric analysis of cell states in single-cell transcriptomic data.Briefings in bioinformatics · 2024Article
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 2024Article
- Topological data analysis reveals core heteroblastic and ontogenetic programs embedded in leaves of grapevine (Vitaceae) and maracuyá (Passifloraceae).PLoS computational biology · 2024Article
- Representing and extracting knowledge from single-cell data.Biophysical reviews · 2024Review
- Scbean: a python library for single-cell multi-omics data analysis.Bioinformatics (Oxford, England) · 2024Article
- Single-nucleus transcriptomic analysis reveals the relationship between gene expression in oligodendrocyte lineage and major depressive disorder.Journal of translational medicine · 2024Article
58 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Transcriptional programs control cellular lineage commitment and differentiation during development. Understanding of cell fate has been advanced by studying single-cell RNA-sequencing (RNA-seq) but is limited by the assumptions of current analytic methods regarding the structure of data. We present single-cell topological data analysis (scTDA), an algorithm for topology-based computational analyses to study temporal, unbiased transcriptional regulation. Unlike other methods, scTDA is a nonlinear, model-independent, unsupervised statistical framework that can characterize transient cellular states. We applied scTDA to the analysis of murine embryonic stem cell (mESC) differentiation in vitro in response to inducers of motor neuron differentiation. scTDA resolved asynchrony and continuity in cellular identity over time and identified four transient states (pluripotent, precursor, progenitor, and fully differentiated cells) based on changes in stage-dependent combinations of transcription factors, RNA-binding proteins, and long noncoding RNAs (lncRNAs). scTDA can be applied to study asynchronous cellular responses to either developmental cues or environmental perturbations.
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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.