References

References#

[Soldatov19]

Soldatov et al. (2019), Spatiotemporal structure of cell fate decisions in murine neural crest, Science.

[Wolf18]

Wolf et al. (2018), Scanpy: large-scale single-cell gene expression data analysis, Genome Biology.

[Setty19]

Setty et al. (2019), Characterization of cell fate probabilities in single-cell data with Palantir, Nature Biotechnology.

[Albergante20]

Albergante et al. (2019), Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph, Entropy.

[Faure20]

Faure et al. (2020), Single cell RNA sequencing identifies early diversity of sensory neurons forming via bi-potential intermediates, Nature Communications.

[Mao15]

Mao et al. (2015), SimplePPT: A simple principal tree algorithm, SIAM International Conference on Data Mining.

[Bargaje17]

Bargaje et al. (2017), Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells, PNAS.

[Lange22]

Lange et al. (2022), CellRank for directed single-cell fate mapping, Nature Methods.

[Kameneva21]

Kameneva et al. (2021) Single-cell transcriptomics of human embryos identifies multiple sympathoblast lineages with potential implications for neuroblastoma origin, Nature Genetics.

[Ji22]

Ji et al. (2022) A statistical framework for differential pseudotime analysis with multiple single-cell RNA-seq samples, biorxiv.

_images/scFates_logo_dark.png