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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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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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“S20, C and D, the Palantir method as implemented in CellRank was used (94, 95).”
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“A final point came after a survey of our data with two additional algorithms: ‘RNA velocity’ (La Manno et al., 2018) and Palantir (Setty et al., 2019).”
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“26 We specified the starting point as monocytes.”
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“Palantir prediction was generated using run_pal-antir function with num_waypoints = 500 and other parameters set to defaults.”
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“Furthermore, new transcriptomic and bioinformatic techniques have revealed the lineage trajectories of single cells and their gene expression changes during development (Nowotschin et al., 2019b; Peng et al., 2019; Setty et al., 2019).”
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“Palantir models cell fate choices as a continuous probabilistic process over pseudotime, estimating the probability of a cell in an intermediate state to reach a terminal state (here: G, OP, and GOP).”
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MethodSelf-citationAstrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease
Serrano-Pozo, A., Li, H., Li, Z., Muñoz-Castro, C., Jaisa-aad, M., Healey, M. A., Welikovitch, L. A., Jayakumar, R., Bryant, A. G., Noori, A., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01791-4 · Cited by 140
Long, H., Jia, Q., Wang, L., Fang, W., Wang, Z., Jiang, T., Zhou, F., Zheng, J., Huang, J., Zhou, L., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.04.018 · Cited by 138
Nam, A. S., Dusaj, N., Izzo, F., Murali, R., Myers, R. M., Mouhieddine, T. H., Sotelo, J., Benbarche, S., Waarts, M., Gaiti, F., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01179-9 · Cited by 140
Distinct properties of adipose stem cell subpopulations determine fat depot-specific characteristics
Nahmgoong, H., Jeon, Y. G., Park, E. S., Choi, Y. H., Han, S. M., Park, J., Ji, Y., Sohn, J. H., Han, J. S., Kim, Y. Y., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2021.11.014 · Cited by 135
Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis
Adil, A., Kumar, V., Jan, A. T., & Asger, M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.591122 · Cited by 138
“The datasets of 4.5 million cells are already published in Data 2 , the largest of which contains more than 1.5 million CD34 hematopoietic cells of human bone marrow (Setty et al., 2019) and 1.3 million transcriptomes of mouse brain cells ( Figures 2B,C ).”
BackgroundInfluentialLi, C., Virgilio, M. C., Collins, K. L., & Welch, J. D. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01476-y · Cited by 130
Poch, T., Krause, J., Casar, C., Liwinski, T., Glau, L., Kaufmann, M., Ahrenstorf, A. E., Hess, L. U., Ziegler, A. E., Martrus, G., et al. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.03.016 · Cited by 124
Genetic and epigenetic coordination of cortical interneuron development
Allaway, K. C., Gabitto, M. I., Wapinski, O. L., Saldi, G.-A., Wang, C., Bandler, R. C., Wu, S. J., Bonneau, R., & Fishell, G. (2021) · Nature · doi:10.1038/s41586-021-03933-1 · Cited by 117
Statistical mechanics meets single-cell biology
Teschendorff, A. E., & Feinberg, A. P. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00341-z · Cited by 127
UniTVelo: temporally unified RNA velocity reinforces single-cell trajectory inference
Gao, M., Qiao, C., & Huang, Y. (2022) · Nature Communications · doi:10.1038/s41467-022-34188-7 · Cited by 125
“3a), which is in good concordance with the pseudotime inferred by a diffusion map method with starting node specified manually [22].”
BackgroundMethodTedesco, M., Giannese, F., Lazarević, D., Giansanti, V., Rosano, D., Monzani, S., Catalano, I., Grassi, E., Zanella, E. R., Botrugno, O. A., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01031-1 · Cited by 114
Schwann cell precursors represent a neural crest‐like state with biased multipotency
Kastriti, M. E., Faure, L., Ahsen, D. V., Bouderlique, T., Boström, J., Solovieva, T., Jackson, C. R., Bronner-Fraser, M. E., Meijer, D., Hadjab, S., et al. (2022) · The EMBO Journal · doi:10.15252/embj.2021108780 · Cited by 125
“PCA was performed on this subset, and kNN graph was generated with 50 neighbours on the 5 first PCs. Palantir was also applied to the PCA space, and tSNE embedding was generated on the multiscale diffusion space.”
InfluentialSingle-cell integration reveals metaplasia in inflammatory gut diseases
Oliver, A. J., Huang, N., Bartolomé-Casado, R., Li, R., Koplev, S., Nilsen, H. R., Moy, M. R., Çakır, B., Polański, K., Gudiño, V., et al. (2024) · Nature · doi:10.1038/s41586-024-07571-1 · Cited by 132
“3.1) 95 .”
InfluentialFaure, L., Soldatov, R., Kharchenko, P. V., & Adameyko, I. I. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac746 · Cited by 115
“The advent of more sensitive scRNAseq methods was accompanied by the development of two main classes of high-resolution trajectory analysis tools.”
In vivo labeling reveals continuous trafficking of TCF-1+ T cells between tumor and lymphoid tissue
Li, Z., Tuong, Z. K., Dean, I., Willis, C., Gaspal, F., Fiancette, R., Idris, S., Kennedy, B. C., Ferdinand, J. R., Peñalver, A., et al. (2022) · The Journal of Experimental Medicine · doi:10.1084/jem.20210749 · Cited by 122
Lineage-specific intolerance to oncogenic drivers restricts histological transformation
Gardner, E. E., Earlie, E. M., Li, K., Thomas, J., Hubisz, M. J., Stein, B. D., Zhang, C., Cantley, L. C., Laughney, A. M., & VARMUS, H. E. (2024) · Science · doi:10.1126/science.adj1415 · Cited by 116
Zeng, A. G. X., Iacobucci, I., Shah, S., Mitchell, A., Wong, G., Bansal, S., Chen, D., Gao, Q., Kim, H., Kennedy, J. A., et al. (2025) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-24-0342 · Cited by 126
Zhou, P., Wang, S., Li, T., & Nie, Q. H. (2021) · Nature Communications · doi:10.1038/s41467-021-25548-w · Cited by 104
“Interestingly, the existence of both meta-stable states and transition cells reconciles a previously noted discrepancy (31) caused by treating the underlying cellular transition dynamics as either a purely continuous processing (e.g. using Palantir) or a discrete process (using other clustering-based lineage inference…”
BackgroundMethodInfluentialGeneralized and scalable trajectory inference in single-cell omics data with VIA
Stassen, S. V., Yip, G. G. K., Wong, K. K. Y., Ho, J. W. K., & Tsia, K. K. (2021) · Nature Communications · doi:10.1038/s41467-021-25773-3 · Cited by 109
OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing
Zeng, Z., Ma, Y., Hu, L., Tan, B., Liu, P., Wang, Y., Xing, C., Xiong, Y., & Du, H. (2024) · Nature Communications · doi:10.1038/s41467-024-50194-3 · Cited by 102
“69 .”
A relay velocity model infers cell-dependent RNA velocity
Li, S., Zhang, P., Chen, W., Ye, L., Brannan, K. W., Le, N.-T., Abe, J., Cooke, J. P., & Wang, G. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01728-5 · Cited by 110
“Thus, trajectory inference algorithms were developed by constructing a potential branching trajectory based on the similarity in the transcriptomic profiles 9–11 .”
Netskar, H., Pfefferle, A., Goodridge, J. P., Sohlberg, E., Dufva, O., Teichmann, S. A., Brownlie, D., Michaëlsson, J., Marquardt, N., Clancy, T., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01884-z · Cited by 126
“To decipher the regulatory gene pathways driving NK cell differentiation at the transcriptional level, we used Palantir 26 and RNA velocity to calculate pseudotime 27,28 .”
InfluentialColombo, G., Cubero, R. J. A., Kanari, L., Venturino, A., Schulz, R., Scolamiero, M., Agerberg, J., Mathys, H., Tsai, L.-H., Chachólski, W., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01167-6 · Cited by 100
A spatial human thymus cell atlas mapped to a continuous tissue axis
Yayon, N., Kedlian, V. R., Boehme, L., Suo, C., Wachter, B. T., Beuschel, R. T., Amsalem, O., Polański, K., Koplev, S., Tuck, E. J., et al. (2024) · Nature · doi:10.1038/s41586-024-07944-6 · Cited by 122
Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review
Brendel, M., Su, C., Bai, Z., Zhang, H., Elemento, O., & Wang, F. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.011 · Cited by 106
“Standard scRNA-seq data analysis tools that provide trajectory inference include Scanpy [3], Monocle [4], VIA [148], Palantir [149], etc .”
MethodBedoya-Reina, O. C., Li, W., Arceo, M., Plescher, M., Bullova, P., Pui, H., Kaucká, M., Kharchenko, P. V., Martinsson, T., Holmberg, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24870-7 · Cited by 95
Villa, C. E., Cheroni, C., Dotter, C. P., López-Tóbon, A., Oliveira, B., Sacco, R., Yahya, A. Ç., Morandell, J., Gabriele, M., Tavakoli, M. R., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110615 · Cited by 92
Regulatory T cells function in established systemic inflammation and reverse fatal autoimmunity
Hu, W., Wang, Z.-M., Feng, Y., Schizas, M., Hoyos, B. E., van der Veeken, J., Verter, J. G., Bou-Puerto, R., & Rudensky, A. Y. (2021) · Nature Immunology · doi:10.1038/s41590-021-01001-4 · Cited by 95
Meier, A. B., Zawada, D. M., Angelis, M. T. D., Martens, L. D., Santamaria, G., Zengerle, S., Nowak‐Imialek, M., Kornherr, J., Zhang, F., Tian, X. Y., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01718-7 · Cited by 102
“Palantir 78 was used with the default parameters to infer a pseudotime on the integrated dataset.”
MethodMultiomics and spatial mapping characterizes human CD8 + T cell states in cancer
Naulaerts, S., Datsi, A., Borràs, D., Antoranz, A., Messiaen, J., Vanmeerbeek, I., Sprooten, J., Laureano, R. S., Govaerts, J., Panovska, D., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.add1016 · Cited by 99
Supervised discovery of interpretable gene programs from single-cell data
Kunes, R., Walle, T., Land, M., Nawy, T., & Pe’er, D. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01940-3 · Cited by 106
Self-citationLi, H., Zhang, M.-J., Zhang, B., Lin, W., Li, S., Xiong, D., Wang, Q., Wang, W.-D., Yang, Q., Huang, C., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59341-w · Cited by 107
“We also applied Palantir to complement the trajectory analysis using default parameters 70 .”
JMJD3 activated hyaluronan synthesis drives muscle regeneration in an inflammatory environment
Nakka, K. K., Hachmer, S., Mokhtari, Z., Kovac, R., Bandukwala, H., Bernard, C., Li, Y., Xie, G., Liu, C., Fallahi, M., et al. (2022) · Science · doi:10.1126/science.abm9735 · Cited by 96
“To perform trajectory analyses, Palantir workflow (35) was applied, and the cell densities in each sample were calculated in t-SNE manifold.”
MethodSingle-cell immunology: Past, present, and future
Ginhoux, F., Yalin, A., Dutertre, C., & Amit, I. (2022) · Immunity · doi:10.1016/j.immuni.2022.02.006 · Cited by 96
Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis
Ben-Kiki, O., Bercovich, A., Lifshitz, A., & Tanay, A. (2022) · Genome Biology · doi:10.1186/s13059-022-02667-1 · Cited by 98
Suo, C., Polański, K., Dann, E., Lindeboom, R. G. H., Vilarrasa‐Blasi, R., Vento‐Tormo, R., Haniffa, M. A., Meyer, K. B., Dratva, L. M., Tuong, Z. K., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01734-7 · Cited by 89
“The fourth panel represents the pseudotime inferred from neighborhood GEX space using Palantir 32 .”
InfluentialPetitpré, C., Faure, L., Uhl, P., Fontanet, P., Filová, I., Pavlínková, G., Adameyko, I. I., Hadjab, S., & Lallemend, F. (2022) · Nature Communications · doi:10.1038/s41467-022-31580-1 · Cited by 86
Li, Q. (2023) · Genome biology · doi:10.1186/s13059-023-02988-9 · Cited by 103
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Self-citationSingle-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells
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MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells
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Multimodal characterization of murine gastruloid development
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Self-citationDefining cardiac functional recovery in end-stage heart failure at single-cell resolution
Amrute, J. M., Lai, L., Ma, P., Koenig, A. L., Kamimoto, K., Bredemeyer, A., Shankar, T. S., Kuppe, C., Kadyrov, F. F., Schulte, L. J., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00260-8 · Cited by 79
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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