Characterization of cell fate probabilities in single-cell data with Palantir

By Manu N. Setty, V. Kiseliovas, Jacob H. Levine, Adam Gayoso, L. Mazutis, Dana Pe'er. Nature Biotechnology, 2019

675 citations recorded by the source corpus.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir’s resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems. Palantir uses single-cell RNA-seq data to generate continuous models of differentiation, infer developmental trajectories, and calculate the probabilities of cell fate choices.

Subfields: Biophysics, Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c1552fe84c6f55a1c006c3d7c2eed671d1304008

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Fabian J Theis
2. Jiarui Ding
3. H. Schiller
4. Dana Pe'er
5. Aviv Regev
6. Manu N. Setty
7. Nir Yosef
8. Romain Lopez

In Cancer Research

1. Aviv Regev
2. Fabian J Theis
3. Jason D. Buenrostro
4. Manu N. Setty

In Molecular Biology

1. Fabian J Theis
2. Aviv Regev
3. Manu N. Setty
4. H. Schiller
5. Marius Lange
6. Caleb A. Lareau
7. Volker Bergen
8. Jiarui Ding

Citations in the corpus

• CellRank for directed single-cell fate mapping (2020, positive): “Palantir 21 identified terminal beta and delta states.”
• Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin (2020, methodology): “We provided 10 normalized topics from cisTopic (Bravo González-Blas et al., 2019) and scATAC UMAP coordinates as inputs to Palantir (Setty et al., 2019) to construct a diffusion map (palantir.utils.run_diffusion_maps(pca_projections, n_components = 10)).”