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Characterization of cell fate probabilities in single-cell data with Palantir
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Abstract
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.
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“Gene expression trends along pseudotime were calculated based on branch probabilities and generalized additive models, showing the stepwise transcriptional progression of endodermal and endothelial lineages along pseudotime using Palantir44.”
From Cell diversity and plasticity during atrioventricular heart valve EMTs (2023) · cites this paper as [44]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Based on the average diffusion distance10,11 from the bipotent inner cell mass (ICM), TE cells were substantially further away (13.9) from ICM than either EPI (0.41) or PrE (2.0) (Extended Data Fig. 4b, Methods)”
From The emergent landscape of the mouse gut endoderm at single-cell resolution (2019) · cites this paper as [11]
The cited source says what the claim attributes to it.
In the citing paper
“A key distinguishing feature of Palantir is that rather than treating lineage decisions as bifurcations, cell fate choices are modeled as continuous probabilistic processes.”
From The emergent landscape of the mouse gut endoderm at single-cell resolution (2019) · cites this paper as [11]
The cited source says what the claim attributes to it.
In the citing paper
“Palantir results however have been shown to be robust to these parameters10,11,”
From The emergent landscape of the mouse gut endoderm at single-cell resolution (2019) · cites this paper as [11]
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In the citing paper
“The high resolution achieved by Palantir allows detailed mapping of gene expression trends and dynamics that are correlated with changes in lineage potential10,11.”
From The emergent landscape of the mouse gut endoderm at single-cell resolution (2019) · cites this paper as [11]
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In the citing paper
“( E ) Violin plot of pseudotime (47) values for OPCs and Oligos colored by condition (**** p<0.0001, unpaired t-test).”
From Molecular and cellular processes disrupted in the early postnatal Down syndrome prefrontal cortex (2026) · cites this paper as [47]
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In the citing paper
“Cells labeled as exVE and emVE based on expression of known markers (plot on the left), match expected Palantir branch probabilities (4 plots on the right).”
From The emergent landscape of the mouse gut endoderm at single-cell resolution (2019) · cites this paper as [11]
Experimental — accuracy not yet measuredIn the citing paper
“In the Setty et al. dataset67, the ME lineage phenotype is reproduced, but the granulocyte phenotype is not observed (Supplementary Fig. 14b).”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [67]
In the citing paper
“Although the ME lineage phenotype is not detected using the Setty et al. dataset67, the GM lineage phenotype is successfully reproduced (Supplementary Fig. 14c).”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [67]
In the citing paper
“we first map the cells from high dimensional transcriptomic space to a low dimensional rescaled diffusion space (Setty 2019)61, where phenotype distance between cells are more faithfully represented”
From Occult polyclonality of preclinical pancreatic cancer models drives in vitro evolution (2022) · cites this paper as [61]
In the citing paper
“we first map the cells from high dimensional transcriptomic space to a low dimensional rescaled diffusion space (Setty 2019)61, where phenotype distance between cells are more faithfully represented”
From Occult polyclonality of preclinical pancreatic cancer models drives in vitro evolution (2022) · cites this paper as Setty 2019
In the citing paper
“However, it has been proposed that rescaling the diffusion coordinates by their eigenvalues can circumvent this issue61.”
From Occult polyclonality of preclinical pancreatic cancer models drives in vitro evolution (2022) · cites this paper as [61]
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Cancer Biology of Molecular Imaging
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BLTSA: pseudotime prediction for single cells by branched local tangent space alignment
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Transcriptomic profiling of shed cells enables spatial mapping of cellular turnover in human organs
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“Computational tools have been developed to infer cellular dynamics based on single-cell RNAseq (scRNAseq) data through trajectory inferences (La Manno et al, 2018; Trapnell et al, 2014; Setty et al, 2019; Weiler et al, 2024), however, they do not directly inform on cellular turnover rates.”
Zhou, Z., Li, J., Xin, H., Pan, X., & Shen, H. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf144 · Cited by 5
“In response to this limitation, trajectory inference algorithms like slingshot [ 1 ], monocle [ 2 ], and Palantir [ 3 ] have been developed to construct potential branching trajectories based on transcriptomic profile similarities.”
InfluentialGeometric quantification of cell phenotype transition manifolds with information geometry
Huang, M., Wang, Y., Ying, J., Xiao, H., HaiJun, Z. H. O. U., Zhang, L., & Wang, W. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101564 · Cited by 2
Single-cell sequencing analysis of the maternal–fetal interface
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Single-Cell Manifold Preserving Feature Selection (SCMER)Preprint
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The molecular principles governing HCMV infection outcomePreprint
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Woodall, M. N. J., Cujba, A.-M., Worlock, K. B., Case, K., Masonou, T., Yoshida, M., Polański, K., Huang, N., Lindeboom, R. G. H., Mamanova, L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.16.524211 · Cited by 1
Dissecting cell state transitions by causal inferencePreprint
Xu, L., Cong, T., Xu, H., Yang, N., Chen, T., Yang, S., Zhu, M., Sinha, R., Yamamoto, R., Zhang, W., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.08.527606 · Cited by 1
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FABP7 Progenitors are a Targetable Metabolic Root in the BRCA1 BreastPreprint
McCloskey, C. W., Zhang, B., Waas, M., Abazari, G., Vyas, F., Aliar, K., Tharmapalan, P., Kuttanamkuzhi, A., Narala, S., Cruikshank, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.02.565360 · Cited by 1
“A transition matrix was computed using the ‘hard’ threshold scheme that is based on the Palantir algorithm 61 .”
MethodNatural and age-related variation in circulating human hematopoietic stem cellsPreprint
Furer, N., Rappoport, N., Milman, O., Lifshitz, A., Bercovich, A., Ben-Kiki, O., Danin, A., Kedmi, M., Shipony, Z., Lipson, D., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.30.569167 · Cited by 2
Self-renewing Sox9+ osteochondral stem cells in the postnatal skeletonPreprint
Farhat, S., Tilouche, B., Short, S., Piron, M., Campbell, T. M., Fernandes, J. A. L., Somyk, M., Bandukwala, H., Arezza, E., Sastourné-Arrey, Q., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.07.570646 · Cited by 1
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Inferring Single-Cell RNA Kinetics from Various Biological PriorsPreprint
Chen, Y., Hu, Z., Ni, K., Feng, S., Chen, W., & Huang, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.21.595179 · Cited by 1
“For the first prior, if we know the stem cell in the sequenced tissue, we can use palantir [20] or monocle [19] to set the stem cell and then get the pseudo-time estimation of all cells.”
BackgroundMethodInfluentialFantuz, M., Liebig, J., Fontana, G., Iebba, V., Carbone, C., Lukassen, S., Conrad, C., & Carrer, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.04.600210 · Cited by 1
“While this approach allows to describe transitions from a known origin 60 , complex alterations and fluxes between very similar populations often fail to be correctly described by a linear ordering along pseudo-temporal trajectories.”
BackgroundMethodUnveiling Clonal Cell Fate and Differentiation Dynamics: A Hybrid NeuralODE-Gillespie ApproachPreprint
Gao, M., Barile, M., Chabra, S., Haltalli, M. L. R., Calderbank, E. F., Chao, Y., Laurenti, E., Göttgens, B., & Huang, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.08.602444 · Cited by 1
Impact of Clonal Hematopoiesis on the Carcinogenic Process of Multiple MyelomaPreprint
Koh, Y., Park, C., Cho, G., Ryu, G., Park, J.-M., Yoon, H., Oh, Y. M., Lee, C., An, H., Sun, C.-H., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-4672454/v1 · Cited by 1
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TopOMetry systematically learns and evaluates the latent dimensions of single-cell atlasesPreprint
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A single-cell atlas of spatial and temporal gene expression in the mouse cranial neural platePreprint
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“Pseudotime analysis was conducted either with palantir 33 or slingshot 34 .”
Disease-associated loci share properties with response eQTLs under common environmental exposuresPreprint
Lin, W., Li, M., Allen, O., Burnett, J. E., Popp, J. M., Stephens, M., Battle, A. J., & Gilad, Y. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.30.651602 · Cited by 5
“To directly model these continuous changes in cell state, we performed trajectory inference using Palantir 29 , which reconstructs the progression from stem-cell-like to terminal cell states, and assigns each cell a continuous pseudotime reflecting its position along this path.”
Single-Cell Atlas of AML Reveals Age-Related Gene Regulatory Networks in t(8;21) AMLPreprint
Whittle, J., Meyer, S., Lacaud, G., Baker, S. M., & Iqbal, M. (2025) · eLife · doi:10.7554/elife.104978.1 · Cited by 2
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Single-Cell Atlas of AML Reveals Age-Related Gene Regulatory Networks in t(8;21) AMLPreprint
Whittle, J., Meyer, S., Lacaud, G., Baker, S. M., & Iqbal, M. (2025) · eLife · doi:10.7554/elife.104978.2 · Cited by 1
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Reinforcement learning enables single-cell foundation models to learn cellular differentiationPreprint
Chang, K.-L., Chen, H., & Liu, Z. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.693267 · Cited by 1
“According to PanglaoDB 32 , several top correlated genes are established cell type markers: CD24, LGALS4, MUC13, TSPAN1, and KRT18 for epithelial cells; ETS1 for endothelial cells; COL1A2 for mesenchymal cells; MDK for stromal cells; STMN1 for neural progenitors; and PFN1 for neurons.”
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Pseudotime graph diffusion for post hoc visualization of inferred single-cell trajectoriesPreprint
Lukas, B., Pang, J., Koh, T. J., & Dai, Y. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.27.702126 · Cited by 1
“A central goal of single-cell analysis is to reconstruct continuous cellular trajectories and to order cells along developmental or differentiation axes, motivating the development of trajectory inference and pseudotime estimation methods [1–4].”
Xie, Y., Chang, L., Zhong, G., Rink, J. A., Baez-Becerra, T., Armand, E. J., Ding, W., Li, K., Bonne, É., Lie, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703166 · Cited by 4
“To resolve these gradients at higher resolution, we applied diffusion maps on single-nucleus transcriptome profiles to derive a within-subclass pseudo-axis score, with L6IT-2 among the subclasses showing the clearest A-P gradient (Figure 4K) 83 .”
Metadata
- Authors
- Setty, M., Kiseliovas, V., Levine, J., Gayoso, A., Mazutis, L., & Pe’er, D.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Pe’er, Dana
- Venue
- Nature Biotechnology
- Published
- 2019-03-21
- Volume
- 37
- Issue
- 4
- Pages
- 451-460
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC7549125
- OpenAlex
- W2925075625
- Semantic Scholar
- c1552fe84c6f55a1c006c3d7c2eed671d1304008
- Cited by
- 833
- References
- 81
- Citations by year
- 2026: 181 · 2025: 177 · 2024: 157 · 2023: 107 · 2022: 92 · 2021: 55 · 2020: 45 · 2019: 18
- FWCI
- 17.99
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-09-18)
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (CA008748)
- National Cancer Institute (R01CA164729, P30CA008748)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP1HD084071)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- cell differentiation trajectories, cellular plasticity, entropy (information theory), hematopoietic differentiation, bone marrow, transcription factors, single-cell RNA sequencing, single-cell trajectory analysis
- MeSH terms
- Algorithms, Animals, Biotechnology, Bone Marrow Cells, Cell Differentiation, Erythropoiesis, Hematopoiesis, Humans, Markov Chains, Models, Biological, Models, Statistical, Sequence Analysis, RNA, Gene Expression Regulation, Developmental, Cell Lineage, Mice, Single-Cell Analysis
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