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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]
The cited source says what the claim attributes to it.
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]
The cited source says what the claim attributes to it.
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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“To validate this hypothesis, we performed pseudo-time trajectory analysis within monocyte clusters using Palantir and scVelo/Velocyto (Bergen et al., 2020; La Manno et al., 2018; Setty et al., 2019) which could predict the direction of differentiation and “RNA velocity” — the time derivative of the gene expression sta…”
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Single-Cell RNA Sequencing Data Analysis
Kazi, J. U. (2025) · Springer eBooks · doi:10.1007/978-3-031-85600-6_13
Bischoff, S., Poličar, P. G., Mukherjee, S., Macke, J. H., Claassen, M., & Schröder, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.26.683064
CycleVI: isolating cell cycle variation with an interpretable deep generative model
Mozdzanowski, P., Tarbier, M., & Jeuken, G. S. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag372
“To evaluate CycleVI’s performance in this challenging context, we applied it to a public single-cell RNA-seq dataset of 31,634 human bone marrow cells enriched for the CD34+ hematopoietic stem and progenitor cell (HSPC) population (Setty et al. 2019).”
KSRV: a Kernel PCA-Based framework for inferring spatial RNA velocity at single-cell resolution
Yan, H., Jiang, J. W., Qiu, H., Shi, Y.-Z., & Zhang, B. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1695803
Single-cell transcriptomic analysis reveals age-related remodeling of brain endothelial cells
Nguyen, H. D., Siddiqui, S., Bohannon, D. G., Blair, R. V., Deng, H.-W., Prat, A., & Kim, W.-K. (2026) · Aging Brain · doi:10.1016/j.nbas.2026.100164
“0.0) on our normalized dataset to compute multiscale diffusion maps (n_eigs = 8) [42].”
InfluentialUsing Diffusion Models as Cell Fate Reprogramming Planners
Shankar, S. S. (2025) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3768322.3769018
“For pseudotime reconstruction we benchmark against the methods of Setty et al. [44] (Wishbone) and, Setty et al. [45] ( Palantir ), as well as the Monocle family of models [4, 39] and the recent scRL method of Fu et al. [14].”
SEEK-VEC: Augmenting topic modeling with spectral ensemble learningPreprint
Danning, R., Ke, Z. T., Ma, R., & Lin, X. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.12.693799
“We compare SEEK-VEC to four popular methods for single-cell analysis: Destiny [36], Palantir [37], PHATE [38], and Slingshot [39].”
Haddad, Y. H., Rom, Y. A., Green, G. S., Cain, A., Raveh, B., & Habib, N. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.15.694345
Additional file 4 of Leveraging gene correlations in single cell transcriptomic data
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S. X., Nie, Q., & Lander, A. D. (2024) · Figshare · doi:10.6084/m9.figshare.27059071
Mei, Y., Liu, Y., Liu, W., Chen, M., Liu, X., Wang, S., Mou, J., Xing, H., Tang, K., Tian, Z., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26962412
Zheng, S. C., Stein-O’Brien, G., Boukas, L., Goff, L. A., & Hansen, K. D. (2024) · Figshare · doi:10.6084/m9.figshare.26632561
Additional file 5 of Leveraging gene correlations in single cell transcriptomic data
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S. X., Nie, Q., & Lander, A. D. (2024) · Figshare · doi:10.6084/m9.figshare.27059074
Additional file 8 of Leveraging gene correlations in single cell transcriptomic data
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S. X., Nie, Q., & Lander, A. D. (2024) · Figshare · doi:10.6084/m9.figshare.27059083
Mei, Y., Liu, Y., Liu, W., Chen, M., Liu, X., Wang, S., Mou, J., Xing, H., Tang, K., Tian, Z., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26962454
Additional file 10 of Leveraging gene correlations in single cell transcriptomic data
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S. X., Nie, Q., & Lander, A. D. (2024) · Figshare · doi:10.6084/m9.figshare.27059092
Mei, Y., Liu, Y., Liu, W., Chen, M., Liu, X., Wang, S., Mou, J., Xing, H., Tang, K., Tian, Z., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26962430
Mei, Y., Liu, Y., Liu, W., Chen, M., Liu, X., Wang, S., Mou, J., Xing, H., Tang, K., Tian, Z., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26962421
Additional file 1 of Leveraging gene correlations in single cell transcriptomic data
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S. X., Nie, Q., & Lander, A. D. (2024) · Figshare · doi:10.6084/m9.figshare.27059062
Mei, Y., Liu, Y., Liu, W., Chen, M., Liu, X., Wang, S., Mou, J., Xing, H., Tang, K., Tian, Z., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26962442
Mei, Y., Liu, Y., Liu, W., Chen, M., Liu, X., Wang, S., Mou, J., Xing, H., Tang, K., Tian, Z., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26962451
Luo, Y., Wang, J., Li, K., Li, M., Xu, S., Liu, X., Zhang, Z., Xu, X., Zhang, Y., Pan, J., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19728499
Mei, Y., Liu, Y., Liu, W., Chen, M., Liu, X., Wang, S., Mou, J., Xing, H., Tang, K., Tian, Z., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26962433
Mei, Y., Liu, Y., Liu, W., Chen, M., Liu, X., Wang, S., Mou, J., Xing, H., Tang, K., Tian, Z., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26962418
Wang, L., Nie, R., Zhang, J., & Cai, J. (2024) · Figshare · doi:10.6084/m9.figshare.26553392
Additional file 9 of Leveraging gene correlations in single cell transcriptomic data
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S. X., Nie, Q., & Lander, A. D. (2024) · Figshare · doi:10.6084/m9.figshare.27059086
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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