Systematic mapping of cell state trajectories, cell lineage, and perturbations in the zebrafish embryo using single cell transcriptomics
High-throughput mapping of cellular differentiation hierarchies from single-cell data promises to empower systematic interrogations of vertebrate development and disease. Here, we applied single-cell RNA sequencing to >92,000 cells from zebrafish embryos during the first day of development. Using a graph-based approach, we mapped a cell state landscape that describes axis patterning, germ layer formation, and organogenesis. We tested how clonally related cells traverse this landscape by develo...
Provenance — who produced it, who reused it
Linked to 13 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- PAGA: graph abstraction reconciles clustering with trajectory in... 2019 · 1,812 cites
- Confronting false discoveries in single-cell differential expres... 2021 · 980 cites
- Inferring spatial and signaling relationships between cells from... 2020 · 417 cites
- Cell type prioritization in single-cell data 2020 · 268 cites
- Quantifying the effect of experimental perturbations at single-c... 2021 · 227 cites
7 further papers cite this accession but reuse could not be confirmed.
Deep data QC
83/100 · BStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
The B grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0