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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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Natekar, P., Yao, B., Mohammad-Taheri, S., Rusnak, A., Gort-Freitas, N. A., Fillâtre, J., Raymond, J. J., Saksena, S. D., Lipnick, S., & Sokolov, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.03.729984
“We next compared TBR performance against unsupervised trajectory inference methods (PAGA, DPT, Palantir) 8,9,13 and supervised label refinement approaches (HiDDEN, MELD, Milo) 11,14,15 .”
Tumor-induced reprogramming of microglia to a DAM phenotype facilitates brain metastasis
Hu, Z., Zhang, Y., Zhang, J., Yang, G., Cui, H., Nian, J., & Zhang, G. (2026) · Advances in Cancer Biology - Metastasis · doi:10.1016/j.adcanc.2026.100193
Fu, Z., Fu, J., Zhang, K., Ran, T., & Chen, C. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1838613
“…these methods typically operate on pre-computed embeddings from PCA or scVI, inheriting any distortions from upstream dimensionality reduction (Setty et al., 2019; Lange et al., 2022). scTour (Li, 2023) integrates VAE training with ODE constraints but lacks geometric regularization to…”
Minimal essential requirements for neural tube self-organisationPreprint
Stuart, H. T., Costantini, E., Wang, J., Krammer, T., Delàs, M. J., Melchionda, M., Cornwall-Scoones, J., Schmauss, G., Lendl, T., Ishihara, K., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.06.730555 · Cited by 1
“3.0) ( Setty et al., 2019) to compute the diffusion-map dimension reduction.”
InfluentialWang, J., Li, R., Guo, C., Qiang, M., Wang, S., Wang, G., Tu, K., & Xu, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.09.731114
Organoid modeling of lung branching morphogenesis and epithelial lineage specification
Pan, M., Cai, B., Li, G., et al. (2026) · Developmental Cell · doi:10.1016/j.devcel.2026.05.012
Larson, J. H., Compeer, E. B., Dougherty, P. R., Smith, K., Zaiken, M. C., Margaritaki, O., Kopp, B., Jin, S., Harkiolaki, M., Chen, L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.18.731665
Multimodal Human Scalp Atlas Defines Cell Landscape and Lineage Architecture In SituPreprint
Li, E. B., Stephens, C. M., Klay, M., Carcamo, A., Han, J., Quan, V. L., Colavincenzo, M. L., Pearlman, R. L., Yoo, S. S., Wang, D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.29.735239
Evangelisti, A., Phillips, S. M., Jungverdorben, J., Walsh, R. M., Wu, Y., Bocchi, V. D., Zhou, T., & Studer, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.30.729930
Huang, R., & Goods, B. A. (2026) · Reproduction · doi:10.1093/reprod/xaag079 · Cited by 1
Multimodal computational framework identifies B cell convergence in autoimmunity and ageingPreprint
Lou, H., Zhang, M., Zhang, B. Y., Lu, Q., Zheng, J.-Q., & Cao, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.29.735110
Endotome as a Source of Human Peri-Aortic Brown AdipocytesPreprint
Yu, H., Xiang, W., Teng, K., Ng, E. S. K., Kam, A. Y. F., Punyawatthananukool, S., Dalton, S., & Wu, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.04.735132
Lee, H. J., Kim, C. J., Ahn, H., Cho, G., Mun, S.-H., Kim, S. W., Choi, H.-S., Um, D., Kim, H., Ko, H., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-75308-x
“0.1) Python package 58 .”
InfluentialBowman, M., Bandopadhyay, R., Singh, V., Telpoukhovskaia, M. A., Velde, R. V., Shaffer, S. M., Trowbridge, J. J., & Bowman, R. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.01.735284
Cimino, D., Danese, A., Denoth‐Lippuner, A., Chlebik, P., Thorwirth, M., Simon, T., Richter, M. L., Jessberger, S., & Götz, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.09.737275
“Pseudotime analysis was computed independently for slowly and rapidly dividing cells using Palantir 1.4.4 (Setty et al., 2019).”
InfluentialSynthetic developmental engineering of human liver organogenesis
Taheri, M. N., Xiang, Y., & Ebrahimkhani, M. R. (2026) · Development · doi:10.1242/dev.205040
PseudoVelo: Inferring Gene Expression Derivatives Along Pseudotime as Pseudo-Velocity
Zang, X., Shu, X., Zhou, Z., Wang, X., & Wang, J. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27146420
“…we expanded our evaluation to two additional classical datasets: a mouse pancreatic endocrino-genesis dataset (referred to as Pancreas in the tables) [16]; and a dataset of early human hematopoiesis from CD34+ bone marrow cells (referred to as Bone Marrow) [9] (Table 1, Figures S1 and S2).”
InfluentialTransVelo: A Deep Learning Framework for RNA Velocity Analysis in Plant Single-Cell TranscriptomicsPreprint
Li, F., Peng, N., & Luo, X. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7162184
SP2 impedes the pluripotent-to-totipotent state transition in an RA-dependent mannerPreprint
Hu, S., Zhu, P., Liu, H., Chen, H., Xu, S., 郭恩静, Cai, B., Quan, X., Pei, D., Chen, J. S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.23.740258
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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