ArticleeLife2017
Discovering sparse transcription factor codes for cell states and state transitions during development.
Article in eLife, 2017. 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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Who cites it
18 citing papers in PubMed.
- Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid Leukemia.bioRxiv : the preprint server for biology · 2026Article
- Unveiling gene regulatory networks during cellular state transitions without linkage across time points.Scientific reports · 2024Article
- Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs).Briefings in bioinformatics · 2024Article
- Time-integrated BMP signaling determines fate in a stem cell model for early human development.Nature communications · 2024Article
- An urgent need for longitudinal microbiome profiling coupled with machine learning interventions.Frontiers in microbiology · 2024Article
- A dynamical systems treatment of transcriptomic trajectories in hematopoiesis.Development (Cambridge, England) · 2023Article
- Modulating mesendoderm competence during human germ layer differentiation.Cell reports · 2021Article
- Discovering a sparse set of pairwise discriminating features in high-dimensional data.Bioinformatics (Oxford, England) · 2021Article
- Lineage tracing meets single-cell omics: opportunities and challenges.Nature reviews. Genetics · 2020Review
- Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data.Nature communications · 2020Article
- Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?Trends in genetics : TIG · 2018Review
- Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics.Biophysical journal · 2018Review
- Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data.Briefings in functional genomics · 2018Review
- Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain.Nature biotechnology · 2018Article
- Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut.Cell systems · 2018Article
- Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems.Cellular and molecular gastroenterology and hepatology · 2018Review
- A new way to build cell lineages.eLife · 2017Article
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4 authors.
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Abstract
Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships.
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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.