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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]
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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]
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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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“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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“Recently, Pearl’s structural causal model [ 23 ] has effectively utilized the Markovian property [ 20 ] to uncover causal relationships and has been widely applied in the analysis of complex gene regulatory networks [ 24 , 25 ].”
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“More recently, single-cell RNA sequencing (scRNA-seq) studies have questioned this binary decision-making model, and have instead suggested that cell-fate determination is a continuous and plastic process [20,21], with cells gradually committing to their final fates rather than making an instantaneous and irreversible…”
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“Subsequently, a multiscale-data matrix was generated using defined number of eigen vectors from the diffusion map using 'determine_multiscale_function' from Palantir.”
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“Pseudotime inference typically ranks cells based on cell similarity (Qiu et al. 2017, Street et al. 2018, Setty et al. 2019, Qu et al. 2025).”
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“After transferring Seurat object to the proper Anndata format, we then run the python package Palantir with default parameters [34].”
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“Pseudotime analysis, like using Monocle (58, 59), Slingshot (80), and Palantir (60), is important for scRNA-seq analysis.”
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“To ascertain the differential trajectory of monocytes, macrophages, and dendritic-like cells in control and macHif1aKO hearts we used Palantir trajectory analysis 32 .”
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“1.0) R package 46 with the Palantir pseudotime values of cells and 300 interpolated points for each trajectory.”
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“Second-generation algorithms incorporated advanced graph theory: Monocle 3 [14] implemented uniform manifold approximation and projection (UMAP) and principal graph analysis, Palantir [15] applied Markov processes for fate decisions, and PAGA [16] developed partition-based graph abstraction.”
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“This approach not only identifies cell types and tracks changes in cellular states but also reveals cellular heterogeneity and uncovers the molecular and cellular mechanisms underlying complex biological phenomena at the single-cell level (Setty et al. 2019; Trapnell et al. 2014).”
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Lung NR3C1+ and CXCR6high T cells distinguish immunopathogenesis of human emphysema
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“To con fi rm the CD4 T cell subsets, we performed pseudotemporal analysis of the different populations using the Palantir modeling method 50 .”
Chiello, J. L., Shaikh, N., Jacobi, J. J., Gaulin, N., Santos, G. S., Keck, C., Hess, S. M., Lichty, B. D., Singh, P. K., Rosario, S. R., et al. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.09.047 · Cited by 4
“The cell-cell transition matrix was computed on imputed expression within the CellRank.PseudotimeKernel using pseudotime calculated by Palantir 91 as the time key, with the root being set within the unpolarized “M0” cell population.”
He, Y., Yu, J., Xie, Y., Wu, Y., Wang, L., Ren, J., Zhang, Z., Yu, T., Jiang, S., Shan, H., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07297-2 · Cited by 3
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Sequential RNA polymerase II activation drives human hematopoiesis
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“Pseudotime analysis was performed to examine the pause-and-release, versus initiate-and-release genes along the MEP, GMP and CLP lineage trajectories of the RNA-seq UMAP as previously described 36,52 .”
DLX2 acts as a pioneer factor and drives Msx1 + ectomesenchyme formation from embryonic stem cells
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Cross-species plant single-cell analysis: community challenges and shared solutions
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“S6C); we were then able to apply pseudotime trajectory analysis to the data using the Palantir algorithm (26), with PanINs as the starting root point.”
Han, L., Ji, Y., Yu, Y., Ni, Y., Zeng, H., Zhang, X., Liu, H., & Zhang, Y. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011319 · Cited by 3
“The pseudotime for each OPCST was individually inferred using Palantir [81].”
Deciphering gene regulatory programs in mouse embryonic skin through single-cell multiomics analysis
Deng, Q., Cai, P., Luo, Y., Zhang, Z., Ma, W., Huang, Z., Chen, X., Hao, S., Ma, W., Xu, J., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03652-0 · Cited by 4
“To further refine our velocity predictions, we applied CellRank, which detects the probabilities of initial and terminal states for each cell based on RNA velocity [60, 61].”
LISA2: Learning Complex Single-Cell Trajectory and Expression Trends
Chen, Y., Zhang, Y., Li, J. Y. H., & Ouyang, Z. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.681206 · Cited by 3
“…tree (DDRTree) is built directly on the principal component analysis (PCA)/independent component analysis (ICA) space (Qiu et al., 2017; Cao et al., 2019), but Monocle 2 may be affected by noise and thus fail to distinguish correct terminal states for multiple branches (Setty et al., 2019).”
BackgroundMethodProfiling intermediate cell states in high resolution
MacLean, A. L. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100204 · Cited by 3
“It would also be of interest to compare the transitionscoresasdefinedherewith those based upon entropy and concepts from statistical physics (Teschendorff and Feinberg, 2021).”
BackgroundPOT1a deficiency in mesenchymal niches perturbs B-lymphopoiesis
Nakashima, K., Kunisaki, Y., Hosokawa, K., Gotoh, K., Yao, H., Yuta, R., Semba, Y., Nogami, J., Kikushige, Y., Stumpf, P. S., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05374-0 · Cited by 4
MLBKFD: Probabilistic Model Methods to Infer Pseudo Trajectories from Single-cell Data
Han, C., Cao, W., Li, C., Guo, Y., Wang, Y., Shi, Y.-Z., & Zhang, B. (2023) · Journal of Computational Biophysics and Chemistry · doi:10.1142/s2737416524400015 · Cited by 3
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scDown: A Pipeline for Single-Cell RNA-Seq Downstream Analysis
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Cellular proliferation biases clonal lineage tracing and trajectory inference
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“…Street et al. 2018, Weinreb et al. 2018, Cao et al. 2019, Chen et al. 2019, Lin and Bar-Joseph 2019, Saelens et al. 2019, Schiebinger et al. 2019, Setty et al. 2019, Tritschler et al. 2019, Wolf et al. 2019, Tong et al. 2020, Tran and Bader 2020, Yeo et al. 2021, Zhang et al. 2021) and,…”
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Laruy, B. L., García-Gónzalez, I., Casquero-García, V., & Benedito, R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.13.295238 · Cited by 3
Cytopath: Simulation-based inference of differentiation trajectories from RNA velocity fieldsPreprint
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Transiently increased intercommunity regulation characterizes concerted cell phenotypic transitionPreprint
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“3a) [46].”
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Feature selection for preserving biological trajectories in single-cell dataPreprint
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“By assuming cells are asynchronously changing over time such that a profiled biological sample 9 from a single experimental time point describes a range of the underlying dynamic process, computational trajectory 10 inference approaches have leveraged minimum spanning tree approaches [20, 21, 22], curve-fitting [23, 2…”
BackgroundJackson, C. A., Beheler-Amass, M., Tjärnberg, A., Suresh, I., Hickey, A. S.-M., Bonneau, R., & Gresham, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.21.558277 · Cited by 4
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Spatiotemporal dynamics of tumor microenvironment remodelingPreprint
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“To investigate the molecular underpinnings of this transition, we applied Palantir to infer pseudotime trajectories from the Harmony-integrated manifold and performed trajectory differential gene expression analysis 35 .”
TopoMetry systematically learns and evaluates the latent geometry of single-cell dataPreprint
Sidarta-Oliveira, D., Domingos, A. I., & Velloso, L. A. (2022) · eLife · doi:10.1101/2022.03.14.484134 · Cited by 3
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Early neuronal reprogramming and cell cycle reentry shape Alzheimer’s disease progression
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CytoSimplex: visualizing single-cell fates and transitions on a simplex
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“Pseudotime inference and RNA velocity methods have both been used extensively for this task (Ji and Ji 2016, Welch et al. 2016, La Manno et al. 2018, Street et al. 2018, Cao et al. 2019, Saelens et al. 2019, Setty et al. 2019, Bergen et al. 2020, Gu et al. 2022, Li et al. 2023).”
Fu, Z., Chen, C., Wang, S., Wang, J., & Chen, S. (2025) · BMC Biology · doi:10.1186/s12915-025-02315-7 · Cited by 4
“We validated these findings in hematopoiesis [36] and endocrinogenesis [37] datasets with ten technical replicates (Additional file 1: Fig.”
OmnibusX: A unified platform for accessible multi-omics analysis
Truong, L., Truong, T., & Nguyen, H. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013480 · Cited by 2
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“For high-resolution trajectory sampling, we integrated the Palantir algorithm (42), setting the number of waypoints to 500.”
Human bronchial basal cells are a community of variants
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“26 Focusing first on the population sample, a nearest neighbor graph identified two possible terminal states (Figures 3A1 and 3A2).”
A novel paradigm for single-cell annotation in stem cell research
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Çeti̇n, R., Picco, G., van Staalduinen, J., Bindels, E. M., Hoogenboezem, R. M., van Beek, G., Sanders, M. A., Fidan, Y., Korkmaz, A., Gribnau, J. H., et al. (2025) · Development · doi:10.1242/dev.204792 · Cited by 2
“Pseudotime construction was performed with Palantir (Setty et al., 2019) and Slingshot (Street et al., 2018).”
CellRank: consistent and data view agnostic fate mapping for single-cell genomics
Weiler, P., & Theis, F. J. (2026) · Nature Protocols · doi:10.1038/s41596-025-01314-w · Cited by 3
Single-cell atlas of AML reveals age-related gene regulatory networks in t(8;21) AML
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“To assess the robustness of the developmental pathways identified by Monocle2 [64], we performed an independent trajectory analysis using Palantir [65].”
The Elusive Origin of Glioblastoma: Where Do We Stand?
Marin, M. P., Almabrok, H., Miller, M. L., & Haggiagi, A. M. (2026) · Cells · doi:10.3390/cells15070590 · Cited by 2
Perturb-seq uncovers pathological obstacles to direct cardiac reprogramming in vivo
Cai, Y., Yang, Y., Yang, J., Ding, R., Zhang, Q., Dang, X., Li, C., & Zhao, Y. (2026) · Cell stem cell · doi:10.1016/j.stem.2026.03.006 · Cited by 3
Wang, H., Semba, T., Yonemura, A., Naito, H., Fu, L., Tajiri, T., Iwatsuki, M., & Ishimoto, T. (2026) · Cancer Science · doi:10.1111/cas.70435 · Cited by 4
“Furthermore, to investigate the differentiation trajectories of CAF subtypes, we applied multiple pseudotime inference methods, including scFates [46], Palantir [47], and CellRank [48].”
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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