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 show stepwise transcriptional progression of endodermal and endothelial lineages along pseudotime using Palantir.
“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]
The Results section (‘The Palantir Algorithm’; page unavailable) says Palantir calculates gene-expression trends along pseudotime using generalized additive models weighted by branch probabilities. The cited paper’s lineage analyses concern hematopoiesis, not a stepwise transcriptional progression of endodermal and endothelial lineages, so it supports the method but not that specific finding. (LLM, conf=0.86)
Experimental — accuracy not yet measuredTE cells were substantially further away (average diffusion distance 13.9) from ICM than EPI (0.41) or PrE (2.0).
“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]
Palantir models cell fate choices as continuous probabilistic processes rather than treating lineage decisions as bifurcations.
“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]
Palantir results are robust to the parameters used.
“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 high resolution achieved by Palantir allows detailed mapping of gene expression trends and dynamics that are correlated with changes in lineage potential.
“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]
Pseudotime values for OPCs and Oligos differ by condition (**** p < 0.0001, unpaired t-test).
“( 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]
Cells labeled as exVE and emVE based on expression of known markers match expected Palantir branch probabilities.
“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]
The Palantir Algorithm section says the method assigns cells branch probabilities, but it does not report exVE or emVE labels or compare their marker-based labels with those probabilities. Searches for “exVE emVE” and visceral endoderm markers found no passage addressing the claimed match. (LLM, conf=0.75)
Experimental — accuracy not yet measuredIn the Setty dataset, CellOracle simulation results reproduce the Gata1 ME lineage phenotype but not the granulocyte phenotype.
“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 Setty dataset, CellOracle simulation results reproduce the Cebpa GM lineage phenotype but not the ME lineage phenotype.
“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 rescaled diffusion space, phenotype distance between cells is more faithfully represented.
“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 rescaled diffusion space, phenotype distance between cells is more faithfully represented.
“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
Rescaling the diffusion coordinates by their eigenvalues can circumvent the need to choose the optimal number of diffusion coordinates.
“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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Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585 · see citing works
Stem cells, cancer, and cancer stem cells
Reya, T., Morrison, S. J., Clarke, M. F., & Weissman, I. L. (2001) · Nature · doi:10.1038/35102167 · Cited by 9,738 · see citing works
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422 · see citing works
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · see citing works
Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2013.03.013 · Cited by 4,147 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159 · see citing works
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Street, K., Risso, D., Fletcher, R. B., Das, D., Ngai, J., Yosef, N., Purdom, E., & Dudoit, S. (2018) · BMC Genomics · doi:10.1186/s12864-018-4772-0 · Cited by 3,159 · see citing works
Hematopoiesis: An Evolving Paradigm for Stem Cell Biology
Orkin, S. H., & Zon, L. I. (2008) · Cell · doi:10.1016/j.cell.2008.01.025 · Cited by 2,570 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
Merad, M., Sathe, P., Helft, J., Miller, J., & Mortha, A. (2013) · Annual Review of Immunology · doi:10.1146/annurev-immunol-020711-074950 · Cited by 2,309 · see citing works
Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis
Levine, J. H., Simonds, E. F., Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Litvin, O., Fienberg, H. G., Jager, A., Zunder, E. R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.047 · Cited by 2,303 · see citing works
Self-citationRegev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citationSingle-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment
Azizi, E., Carr, A. J., Plitas, G., Cornish, A. E., Konopacki, C., Prabhakaran, S., Nainys, J., Wu, K., Kiseliovas, V., Setty, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.060 · Cited by 1,976 · see citing works
Self-citationRecovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829 · see citing works
Self-citationA single-cell survey of the small intestinal epithelium
Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750 · see citing works
Coifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675 · see citing works
Chromatin architecture reorganization during stem cell differentiation
Dixon, J. R., Jung, I., Selvaraj, S., Shen, Y., Antosiewicz-Bourget, J. E., Lee, A. Y., Ye, Z., Kim, A., Rajagopal, N., Xie, W., et al. (2015) · Nature · doi:10.1038/nature14222 · Cited by 1,646 · see citing works
Amir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550 · see citing works
Self-citationRegev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457 · see citing works
Diffusion pseudotime robustly reconstructs lineage branching
Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016) · Nature Methods · doi:10.1038/nmeth.3971 · Cited by 1,385 · see citing works
Corces, M. R., Buenrostro, J. D., Wu, B., Greenside, P. G., Chan, S. M., Koenig, J. L., Snyder, M. P., Pritchard, J. K., Kundaje, A., Greenleaf, W. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3646 · Cited by 1,151 · see citing works
Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O. (2015) · Nature Biotechnology · doi:10.1038/nbt.3102 · Cited by 1,136 · see citing works
Yang, J., Zhang, L., Yu, C., Yang, X.-F., & Wang, H. (2014) · Biomarker Research · doi:10.1186/2050-7771-2-1 · Cited by 1,099 · see citing works
Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors
Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.013 · Cited by 1,082 · see citing works
Measuring reproducibility of high-throughput experiments
Li, Q., Brown, J. B., Huang, H., & Bickel, P. J. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas466 · Cited by 1,008 · see citing works
Stem and progenitor cells: origins, phenotypes, lineage commitments, and transdifferentiations
Weissman, I. L., Anderson, D. J., & Gage, F. (2001) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.17.1.387 · Cited by 948 · see citing works
Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis
Novershtern, N., Subramanian, A., Lawton, L. N., Mak, R. H., Haining, W. N., McConkey, M. E., Habib, N., Yosef, N., Chang, C. Y., Shay, T., et al. (2011) · Cell · doi:10.1016/j.cell.2011.01.004 · Cited by 942 · see citing works
Human haematopoietic stem cell lineage commitment is a continuous process
Velten, L., Haas, S. F., Raffel, S., Blaszkiewicz, S., Islam, S., Hennig, B. P., Hirche, C., Lutz, C., Buss, E. C., Nowak, D., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3493 · Cited by 925 · see citing works
Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906 · see citing works
Self-citationUnravelling cancer stem cell potential
Beck, B., & Blanpain, C. (2013) · Nature reviews. Cancer · doi:10.1038/nrc3597 · Cited by 895 · see citing works
Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis
Farrell, J. A., Wang, Y., Riesenfeld, S. J., Shekhar, K., Regev, A., & Schier, A. F. (2018) · Science · doi:10.1126/science.aar3131 · Cited by 876 · see citing works
Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
Wagner, D. E., Weinreb, C., Collins, Z. M., Briggs, J. A., Megason, S. G., & Klein, A. M. (2018) · Science · doi:10.1126/science.aar4362 · Cited by 849 · see citing works
From haematopoietic stem cells to complex differentiation landscapes
Laurenti, E., & Göttgens, B. (2018) · Nature · doi:10.1038/nature25022 · Cited by 830 · see citing works
Takubo, K., Nagamatsu, G., Kobayashi, C. I., Nakamura‐Ishizu, A., Kobayashi, H., Ikeda, E., Goda, N., Rahimi, Y., Johnson, R. S., Soga, T., et al. (2013) · Cell stem cell · doi:10.1016/j.stem.2012.10.011 · Cited by 810 · see citing works
The Biology of Hematopoietic Stem Cells
Morrison, S. J., Uchida, N., & Weissman, I. L. (1995) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cb.11.110195.000343 · Cited by 774 · see citing works
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750 · see citing works
Buenrostro, J. D., Corces, M. R., Lareau, C. A., Wu, B., Schep, A. N., Aryee, M. J., Majeti, R., Chang, H. Y., & Greenleaf, W. J. (2018) · Cell · doi:10.1016/j.cell.2018.03.074 · Cited by 670 · see citing works
Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain
Raj, B., Wagner, D. E., McKenna, A., Pandey, S., Klein, A. M., Shendure, J., Gagnon, J. A., & Schier, A. F. (2018) · Nature Biotechnology · doi:10.1038/nbt.4103 · Cited by 622 · see citing works
Transcription factors in myeloid development: balancing differentiation with transformation
Rosenbauer, F., & Tenen, D. G. (2007) · Nature Reviews Immunology · doi:10.1038/nri2024 · Cited by 606 · see citing works
Identification of a Hierarchy of Multipotent Hematopoietic Progenitors in Human Cord Blood
Majeti, R., Park, C. Y., & Weissman, I. L. (2007) · Cell stem cell · doi:10.1016/j.stem.2007.10.001 · Cited by 603 · see citing works
Clonal analysis of lineage fate in native haematopoiesis
Rodriguez-Fraticelli, A. E., Wolock, S. L., Weinreb, C. S., Panero, R., Patel, S. H., Jankovic, M., Sun, J., Calogero, R. A., Klein, A. M., & Camargo, F. D. (2018) · Nature · doi:10.1038/nature25168 · Cited by 596 · see citing works
Wishbone identifies bifurcating developmental trajectories from single-cell data
Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558 · see citing works
Self-citationThe dynamics of gene expression in vertebrate embryogenesis at single-cell resolution
Briggs, J., Weinreb, C., Wagner, D. E., Megason, S. G., Peshkin, L., Kirschner, M. W., & Klein, A. M. (2018) · Science · doi:10.1126/science.aar5780 · Cited by 548 · see citing works
Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars
Spanjaard, B., Hu, B., Mitic, N., Olivares-Chauvet, P., Janjuha, S., Ninov, N., & Junker, J. P. (2018) · Nature Biotechnology · doi:10.1038/nbt.4124 · Cited by 531 · see citing works
Lung regeneration: mechanisms, applications and emerging stem cell populations
Kotton, D. N., & Morrisey, E. E. (2014) · Nature Medicine · doi:10.1038/nm.3642 · Cited by 512 · see citing works
Developmental diversification of cortical inhibitory interneurons
Mayer, C., Hafemeister, C., Bandler, R. C., Machold, R., Batista Brito, R., Jaglin, X., Allaway, K., Butler, A., Fishell, G., & Satija, R. (2018) · Nature · doi:10.1038/nature25999 · Cited by 492 · see citing works
Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics
Plass, M., Solana, J., Wolf, F. A., Ayoub, S., Misios, A., Glažar, P., Obermayer, B., Theis, F. J., Kocks, C., & Rajewsky, N. (2018) · Science · doi:10.1126/science.aaq1723 · Cited by 474 · see citing works
GATA1 Function, a Paradigm for Transcription Factors in Hematopoiesis
Ferreira, R., Ohneda, K., Yamamoto, M. P., & Philipsen, S. (2005) · Molecular and Cellular Biology · doi:10.1128/mcb.25.4.1215-1227.2005 · Cited by 442 · see citing works
Polylox barcoding reveals haematopoietic stem cell fates realized in vivo
Pei, W., Feyerabend, T. B., Rößler, J., Wang, X., Postrach, D., Busch, K., Rode, I., Klapproth, K., Dietlein, N., Quedenau, C., et al. (2017) · Nature · doi:10.1038/nature23653 · Cited by 412 · see citing works
Population snapshots predict early haematopoietic and erythroid hierarchies
Tusi, B. K., Wolock, S. L., Weinreb, C., Hwang, Y., Hidalgo, D., Zilionis, R., Waisman, A., Huh, J. R., Klein, A. M., & Socolovsky, M. (2018) · Nature · doi:10.1038/nature25741 · Cited by 384 · see citing works
Single-cell mapping of lineage and identity in direct reprogramming
Biddy, B. A., Kong, W., Kamimoto, K., Guo, C., Waye, S. E., Sun, T., & Morris, S. A. (2018) · Nature · doi:10.1038/s41586-018-0744-4 · Cited by 381 · see citing works
PU.1 inhibits GATA-1 function and erythroid differentiation by blocking GATA-1 DNA binding
Zhang, P., Zhang, X., Iwama, A., Yu, C., Smith, K. A., Mueller, B. U., Narravula, S., Torbett, B. E., Orkin, S. H., & Tenen, D. G. (2000) · Blood · doi:10.1182/blood.v96.8.2641 · Cited by 368 · see citing works
FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
Herman, J. S., Sagar, & Grün, D. (2018) · Nature Methods · doi:10.1038/nmeth.4662 · Cited by 363 · see citing works
Nerlov, C., Querfurth, E., Kulessa, H., & Graf, T. (2000) · Blood · doi:10.1182/blood.v95.8.2543 · Cited by 349 · cited 1× · see citing works
“The interplay between GATA1, GATA2 and PU.1 (SPI1) has been proposed to drive the myeloid-versus-erythroid lineage decision 30 with mutual antagonization between PU.1 and GATA1 driving myeloid and erythroid lineage commitments respectively 30, 31 .”
Yu, W., Liu, X., Shen, J., Jovanović, O., Pohl, E. E., Gerson, S. L., Finkel, T., Broxmeyer, H. E., & Qu, C. (2013) · Cell stem cell · doi:10.1016/j.stem.2012.11.022 · Cited by 341 · see citing works
Fundamental limits on dynamic inference from single-cell snapshots
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714723115 · Cited by 341 · see citing works
Developmental Fate and Cellular Maturity Encoded in Human Regulatory DNA Landscapes
Stergachis, A. B., Neph, S. J., Reynolds, A. P., Humbert, R., Miller, B. G., Paige, S. L., Vernot, B., Cheng, J. B., Thurman, R. E., Sandstrom, R. S., et al. (2013) · Cell · doi:10.1016/j.cell.2013.07.020 · Cited by 331 · see citing works
The multifunctional role of EKLF/KLF1 during erythropoiesis
Siatecka, M., & Bieker, J. J. (2011) · Blood · doi:10.1182/blood-2011-03-331371 · Cited by 314 · see citing works
Zhang, H., Liu, T., Liu, C., Song, S., Zhang, X., Liu, W., Jia, H., Xue, Y., & Guo, A. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku887 · Cited by 296 · see citing works
A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice
Dahlin, J. S., Hamey, F. K., Pijuan-Sala, B., Shepherd, M., Lau, W. W. Y., Nestorowa, S., Weinreb, C., Wolock, S., Hannah, R., Diamanti, E., et al. (2018) · Blood · doi:10.1182/blood-2017-12-821413 · Cited by 233 · see citing works
Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios
Hoppe, P. S., Schwarzfischer, M., Loeffler, D., Kokkaliaris, K. D., Hilsenbeck, O., Moritz, N., Endele, M., Filipczyk, A. A., Gambardella, A. R., Ahmed, N., et al. (2016) · Nature · doi:10.1038/nature18320 · Cited by 202 · see citing works
Mori, Y., Iwasaki, H., Kohno, K., Yoshimoto, G., Kikushige, Y., Okeda, A., Uike, N., Niiro, H., Takenaka, K., Nagafuji, K., et al. (2008) · The Journal of Experimental Medicine · doi:10.1084/jem.20081756 · Cited by 201 · see citing works
Herring, C. A., Banerjee, A., McKinley, E. T., Simmons, A. J., Ping, J., Roland, J. T., Franklin, J. L., Liu, Q., Gerdes, M. J., Coffey, R. J., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.10.012 · Cited by 185 · see citing works
B cell development: signal transduction by antigen receptors and their surrogates
Benschop, R. J., & Cambier, J. C. (1999) · Current Opinion in Immunology · doi:10.1016/s0952-7915(99)80025-9 · Cited by 179 · see citing works
Psaila, B., Barkas, N., Iskander, D., Roy, A., Anderson, S., Ashley, N., Caputo, V. S., Lichtenberg, J., Loaiza, S., Bodine, D., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0939-7 · Cited by 150 · see citing works
f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq
Buettner, F., Pratanwanich, P. N., McCarthy, D. J., Marioni, J. C., & Stegle, O. (2017) · Genome biology · doi:10.1186/s13059-017-1334-8 · Cited by 128 · see citing works
Gastrointestinal stem cells in health and disease: from flies to humans
Li, H., & Jasper, H. (2016) · Disease Models & Mechanisms · doi:10.1242/dmm.024232 · Cited by 123 · see citing works
Ibarra-Soria, X., Jawaid, W., Pijuan-Sala, B., Ladopoulos, V., Scialdone, A., Jörg, D. J., Tyser, R. C. V., Calero‐Nieto, F. J., Mulas, C., Nichols, J., et al. (2017) · Nature Cell Biology · doi:10.1038/s41556-017-0013-z · Cited by 121 · see citing works
Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates
Antebi, Y. E., Reich-Zeliger, S., Hart, Y., Mayo, A., Eizenberg, I., Rimer, J., Putheti, P., Pe'er, D., & Friedman, N. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001616 · Cited by 100 · see citing works
Self-citationThe Transcriptional Repressor GFI-1 Antagonizes PU.1 Activity through Protein-Protein Interaction
Dahl, R. E., Iyer, S. R., Owens, K., Cuylear, D. D., & Simon, M. C. (2007) · Journal of Biological Chemistry · doi:10.1074/jbc.m607613200 · Cited by 93 · see citing works
Funnell, A. P. W., Maloney, C., Thompson, L. J., Keys, J. R., Tallack, M. R., Perkins, A. C., & Crossley, M. (2007) · Molecular and Cellular Biology · doi:10.1128/mcb.01658-06 · Cited by 88 · see citing works
May, G. E., Soneji, S., Tipping, A. J., Teles, J., McGowan, S. J., Wu, M., Guo, Y., Fugazza, C., Brown, J., Karlsson, G., et al. (2013) · Cell stem cell · doi:10.1016/j.stem.2013.09.003 · Cited by 88 · see citing works
González, A. J., Setty, M. N., & Leslie, C. S. (2015) · Nature Genetics · doi:10.1038/ng.3402 · Cited by 82 · see citing works
Self-citationSeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps
Setty, M. N., & Leslie, C. S. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004271 · Cited by 69 · see citing works
Self-citationRavet, E., Reynaud, D., Titeux, M., Izac, B., Fichelson, S., Roméo, P., Dubart‐Kupperschmitt, A., & Pflumio, F. (2004) · Blood · doi:10.1182/blood-2003-05-1689 · Cited by 49 · see citing works
A generic, cost-effective, and scalable cell lineage analysis platformPreprint
Biezuner, T., Spiro, A., Raz, O., Amir, S., Milo, L., Adar, R., Chapal-Ilani, N., Berman, V., Fried, Y., Ainbinder, E., et al. (2016) · Genome Research · doi:10.1101/gr.202903.115 · Cited by 44 · see citing works
Palantir characterizes cell fate continuities in human hematopoiesis
Setty, M. N., Kiseliovas, V., Levine, J., et al. (2019) · Code Ocean · doi:10.24433/co.6f3a9d2b-82d6-45bd-a583-5346a30e0c5d · Cited by 2 · see citing works
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Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works
CellRank for directed single-cell fate mapping
Lange, M., Bergen, V., Klein, M., Setty, M. N., Reuter, B., Bakhti, M., Lickert, H., Ansari, M., Gote-Schniering, J., Schiller, H. B., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01346-6 · Cited by 734 · see citing works
“Palantir 21 identified terminal beta and delta states.”
InfluentialSelf-citationDissecting cell identity via network inference and in silico gene perturbation
Kamimoto, K., Stringa, B., Hoffmann, C. M., Jindal, K., Solnica-Krezel, L., & Morris, S. A. (2023) · Nature · doi:10.1038/s41586-022-05688-9 · Cited by 677 · see citing works
Lineage tracing meets single-cell omics: opportunities and challenges
Wagner, D. E., & Klein, A. M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0223-2 · Cited by 594 · see citing works
Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity
Brown, C. C., Gudjonson, H., Pritykin, Y., Deep, D., Lavallée, V., Mendoza, A., Fromme, R. M., Mažutis, L., Ariyan, C. E., Leslie, C. S., et al. (2019) · Cell · doi:10.1016/j.cell.2019.09.035 · Cited by 582 · see citing works
Self-citationSingle-cell transcriptomics reveals cell-type-specific diversification in human heart failure
Koenig, A. L., Shchukina, I., Amrute, J., Andhey, P. S., Zaitsev, K., Lai, L., Bajpai, G., Bredemeyer, A., Smith, G., Jones, C., et al. (2022) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00028-6 · Cited by 482 · see citing works
“We did not detect clear differences in cell composition between male and female donors or individuals with DCM (Supplementary Tables 10, 11, 23 and 24).”
Regenerative lineages and immune-mediated pruning in lung cancer metastasis
Laughney, A. M., Hu, J., Campbell, N. R., Bakhoum, S. F. W., Setty, M. N., Lavallée, V., Xie, Y., Masilionis, I., Carr, A., Kottapalli, S. V., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0750-6 · Cited by 482 · see citing works
Self-citationSingle-cell roadmap of human gonadal development
García‐Alonso, L., Lorenzi, V., Icoresi-Mazzeo, C., Alves-Lopes, J. P., Roberts, K., Sancho‐Serra, C., Engelbert, J., Marečková, M., Gruhn, W. H., Botting, R. A., et al. (2022) · Nature · doi:10.1038/s41586-022-04918-4 · Cited by 469 · see citing works
Krishna, C., DiNatale, R. G., Kuo, F., Srivastava, R. M., Vuong, L. H., Chowell, D., Gupta, S., Vanderbilt, C., Purohit, T. A., Liu, M., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.03.007 · Cited by 426 · see citing works
IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer
Caronni, N., Terza, F. L., Vittoria, F. M., Barbiera, G., Mezzanzanica, L., Cuzzola, V., Barresi, S., Pellegatta, M., Canevazzi, P., Dunsmore, G., et al. (2023) · Nature · doi:10.1038/s41586-023-06685-2 · Cited by 407 · see citing works
The emergent landscape of the mouse gut endoderm at single-cell resolution
Nowotschin, S., Setty, M., Kuo, Y.-Y., Liu, V., Garg, V., Sharma, R., Simon, C. S., Saiz, N., Gardner, R., Boutet, S. C., et al. (2019) · Nature · doi:10.1038/s41586-019-1127-1 · Cited by 383 · see citing works
Self-citationLineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling
Chan, J. M., Zaidi, S., Love, J. R., Zhao, J. L., Setty, M. N., Wadosky, K. M., Gopalan, A., Choo, Z.-N., Persad, S., Choi, J., et al. (2022) · Science · doi:10.1126/science.abn0478 · Cited by 350 · see citing works
Self-citationCancer cells deploy lipocalin-2 to collect limiting iron in leptomeningeal metastasis.
Chi, Y., Remsik, J., Kiseliovas, V., Derderian, C., Sener, U., Alghader, M., Saadeh, F., Nikishina, K., Bale, T., Iacobuzio-Donahue, C., et al. (2020) · Science · doi:10.1126/science.aaz2193 · Cited by 320 · see citing works
Self-citationMouse embryonic stem cells self-organize into trunk-like structures with neural tube and somites
Veenvliet, J. V., Bolondi, A., Kretzmer, H., Haut, L., Scholze‐Wittler, M., Schifferl, D., Koch, F., Guignard, L., Kumar, A. S., Pustet, M., et al. (2020) · Science · doi:10.1126/science.aba4937 · Cited by 318 · see citing works
A single-cell analysis of the Arabidopsis vegetative shoot apex
Zhang, T., Chen, Y., & Wang, J. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.02.021 · Cited by 317 · see citing works
Mapping the developing human immune system across organs
Suo, C., Dann, E., Goh, I., Jardine, L., Kleshchevnikov, V., Park, J.-E., Botting, R. A., Stephenson, E., Engelbert, J., Tuong, Z. K., et al. (2022) · Science · doi:10.1126/science.abo0510 · Cited by 315 · see citing works
“S20, C and D, the Palantir method as implemented in CellRank was used (94, 95).”
MethodRuiz García, S., Deprez, M., Lebrigand, K., Cavard, A., Paquet, A., Arguel, M.-J., Magnone, V., Truchi, M., Caballero, I., Leroy, S., et al. (2019) · Development · doi:10.1242/dev.177428 · Cited by 297 · see citing works
“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).”
InfluentialMitochondria metabolism sets the species-specific tempo of neuronal development
Iwata, R., Casimir, P., Erkol, E., Boubakar, L., Planque, M., Gallego López, I. M., Ditkowska, M., Gaspariunaite, V., Beckers, S., Remans, D., et al. (2023) · Science · doi:10.1126/science.abn4705 · Cited by 312 · see citing works
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 328 · see citing works
“Palantir 120 , VIA 121 and MARGARET 122 are methods that infer complex topographies, identify terminal points and use a Markov chain model to assign probabilities to each cell based on its likelihood to reach the defined terminal points.”
Single-cell brain organoid screening identifies developmental defects in autism
Li, C., Fleck, J. S., Martins-Costa, C., Burkard, T. R., Themann, J., Stuempflen, M., Peer, A. M., Vertesy, Á., Littleboy, J. B., Esk, C., et al. (2023) · Nature · doi:10.1038/s41586-023-06473-y · Cited by 283 · see citing works
“We then constructed a Palantir kernel (PalantirKernel()) (67) based on velocity pseudotime and used Generalized Perron Cluster Cluster Analysis (68)(GPCCA()) to compute a terminal fate probability matrix (compute_absorption_probabilities()).”
MethodDong, R., Yang, R., Zhan, Y., Lai, H., Ye, C., Yao, X.-Y., Luo, W., Cheng, X., Miao, J., Wang, J., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.08.014 · Cited by 268 · see citing works
Zhang, T., Chen, Y., Liu, Y., Lin, W., & Wang, J. (2021) · Nature Communications · doi:10.1038/s41467-021-22352-4 · Cited by 273 · see citing works
RNA velocity—current challenges and future perspectives
Bergen, V., Soldatov, R. A., Kharchenko, P. V., & Theis, F. J. (2021) · Molecular Systems Biology · doi:10.15252/msb.202110282 · Cited by 271 · see citing works
“We made the same observation in human bone marrow CD34 + hematopoietic cells (Setty et al , 2019).”
STING inhibits the reactivation of dormant metastasis in lung adenocarcinoma
Hu, J. Y., Rivera, F. J. S., Wang, Z., Johnson, G. N., Ho, Y.-J., Ganesh, K., Umeda, S., Gan, S., Mujal, A. M., Delconte, R. B., et al. (2023) · Nature · doi:10.1038/s41586-023-05880-5 · Cited by 252 · see citing works
Self-citationGuttikonda, S. R., Sikkema, L., Tchieu, J. H., Saurat, N. G., Walsh, R. M., Harschnitz, O., Ciceri, G., Sneeboer, M. A. M., Mažutis, L., Setty, M. N., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00796-z · Cited by 242 · see citing works
Self-citationConcepts and limitations for learning developmental trajectories from single cell genomics
Tritschler, S., Büttner, M., Fischer, D. S., Lange, M., Bergen, V., Lickert, H., & Theis, F. J. (2019) · Development · doi:10.1242/dev.170506 · Cited by 233 · see citing works
“A typical example of a confounding process is cell cycle progression (Singh et al., 2014; Buettner et al., 2015; Blasi et al., 2017).”
BackgroundCellRank 2: unified fate mapping in multiview single-cell data
Weiler, P., Lange, M., Klein, M., Pe’er, D., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02303-9 · Cited by 260 · see citing works
“For constructing the Palantir pseudotime 5 , irrespective of experimental time points, we followed the same steps as described for the embryoid body development data.”
InfluentialSelf-citationCellular communities reveal trajectories of brain ageing and Alzheimer’s disease
Green, G. S., Fujita, M., Yang, H.-S., Taga, M., Cain, A., McCabe, C., Comandante-Lou, N., White, C. C., Schmidtner, A. K., Zeng, L., et al. (2024) · Nature · doi:10.1038/s41586-024-07871-6 · Cited by 204 · see citing works
SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
Persad, S., Choo, Z.-N., Dien, C., Sohail, N., Masilionis, I., Chaligné, R., Nawy, T., Brown, C. C., Sharma, R., Pe’er, I., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01716-9 · Cited by 222 · see citing works
“We use minimum–maximum sampling 4 for initialization, which identifies a set of representative cell states that are distributed uniformly across the phenotypic manifold (Fig.”
MethodSelf-citationGuo, C., Cai, P., Jin, L., Sha, Q., Yu, Q., Zhang, W., Jiang, C., Liu, Q., Zong, D., Li, K., et al. (2021) · Cell Discovery · doi:10.1038/s41421-020-00236-z · Cited by 221 · see citing works
Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma
Kirschenbaum, D. S., Xie, K., Ingelfinger, F., Katzenelenbogen, Y., Abadie, K., Look, T., Sheban, F., Phan, T. S., Li, B., Zwicky, P., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.032 · Cited by 217 · see citing works
Epigenetic plasticity cooperates with cell-cell interactions to direct pancreatic tumorigenesis
Burdziak, C., Alonso-Curbelo, D., Walle, T., Reyes, J., Barriga, F., Haviv, D., Xie, Y.-B., Zhao, Z., Zhao, C. J., Chen, H.-A., et al. (2023) · Science · doi:10.1126/science.add5327 · Cited by 186 · see citing works
“To gain insights into the impact of IL-33 on the dynamics of cell-state transitions, we used Palantir (61) to infer a pseudotime ordering of epithelial neighborhoods at 3 wpi, beginning from the Nes + progenitor state (Fig.”
Self-citationDrivers of heterogeneity in synovial fibroblasts in rheumatoid arthritis
Smith, M. H., Gao, V. R., Periyakoil, P. K., Kochen, A., DiCarlo, E. F., Goodman, S. M., Norman, T. M., Donlin, L. T., Leslie, C. S., & Rudensky, A. Y. (2023) · Nature Immunology · doi:10.1038/s41590-023-01527-9 · Cited by 198 · see citing works
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 195 · see citing works
“…the single-cell level Palantir is a pseudo-time algorithm based on stochastic processes that, through dimensionality reduction and manifold analysis, effectively captures the continuity of cell states and models the transition from low-differen-tiation cells to terminally differentiated cells [27].”
BackgroundMethodProgressive plasticity during colorectal cancer metastasis
Moorman, A., Benitez, E. K., Cambuli, F., Jiang, Q., Mahmoud, A., Lumish, M., Hartner, S., Balkaran, S., Bermeo, J., Asawa, S., et al. (2025) · Nature · doi:10.1038/s41586-024-08150-0 · Cited by 229 · see citing works
“We used generalized additive models (GAMs) with cubic splines as smoothing functions as in Palantir (v.1.2) 42 to analyse module score trends along DC axes (Fig.”
InfluentialSelf-citationSingle-cell transcriptomics in cancer: computational challenges and opportunities
Fan, J., Slowikowski, K., & Zhang, F. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0422-0 · Cited by 191 · see citing works
“To infer this pseudotime ordering of cells within putative trajectories, a number of computational trajectory inference methods have been developed 99 – 101 , compared 102 , and reviewed 74,103 .”
MethodExpansion of Pathogenic Cardiac Macrophages in Immune Checkpoint Inhibitor Myocarditis
Ma, P., Liu, J., Qin, J., Lai, L., Heo, G. S., Luehmann, H., Sultan, D., Bredemeyer, A., Bajapa, G., Feng, G., et al. (2024) · Circulation · doi:10.1161/circulationaha.122.062551 · Cited by 187 · see citing works
“26 We specified the starting point as monocytes.”
BackgroundAn atlas of epithelial cell states and plasticity in lung adenocarcinoma
Han, G., Sinjab, A. F., Rahal, Z., Lynch, A. M., Treekitkarnmongkol, W., Liu, Y., Serrano, A. G., Feng, J., Liang, K., Khan, K. B., et al. (2024) · Nature · doi:10.1038/s41586-024-07113-9 · Cited by 182 · see citing works
“Palantir prediction was generated using run_pal-antir function with num_waypoints = 500 and other parameters set to defaults.”
MethodInfluentialCui, M., Wang, Z., Chen, K., Shah, A. M., Tan, W. H., Duan, L., Sanchez‐Ortiz, E., Li, H. Z., Xu, L., Liu, N., et al. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.02.019 · Cited by 177 · see citing works
Developmental trajectory of prehematopoietic stem cell formation from endothelium
Zhu, Q., Gao, P., Tober, J. M., Bennett, L. F., Chen, C., Uzun, Y., Li, Y., Howell, E. D., Mumau, M. D., Yu, W., et al. (2020) · Blood · doi:10.1182/blood.2020004801 · Cited by 175 · see citing works
Zheng, X., Wang, X., Cheng, X., Liu, Z., Yin, Y., Li, X., Huang, Z., Wang, Z., Guo, W., Ginhoux, F., et al. (2023) · Nature Cancer · doi:10.1038/s43018-023-00599-8 · Cited by 172 · see citing works
Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq
Liu, Z., Chen, Y., Xia, Q., Liu, M., Xu, H., Chi, Y., Deng, Y., & Xing, D. (2023) · Science · doi:10.1126/science.adg3797 · Cited by 169 · see citing works
Molecular design of hypothalamus development
Romanov, R. A., Tretiakov, E. O., Kastriti, M. E., Zupančič, M., Häring, M., Korchynska, S., Popadin, K. Y., Benevento, M., Rebernik, P., Lallemend, F., et al. (2020) · Nature · doi:10.1038/s41586-020-2266-0 · Cited by 172 · see citing works
Hua, Y.-C., Vella, G., Rambow, F., Allen, E., Martinez, A. A., Duhamel, M., Takeda, A., Jalkanen, S., Junius, S., Smeets, A., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.11.002 · Cited by 151 · see citing works
Mitochondria Define Intestinal Stem Cell Differentiation Downstream of a FOXO/Notch Axis
Ludikhuize, M. C., Meerlo, M., Pagès-Gallego, M., Xanthakis, D., Julià, M. B., Nguyen, N. B., Brombacher, E. C., Liv, N., Maurice, M. M., Paik, J., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.10.005 · Cited by 169 · see citing works
Next-generation computational tools for interrogating cancer immunity
Finotello, F., Rieder, D., Hackl, H., & Trajanoski, Z. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0166-7 · Cited by 158 · see citing works
Hu, T., Allam, M., Cai, S., Henderson, W. E., Yueh, B., Alici-Garipcan, A., Ievlev, A. V., Afkarian, M., Beyaz, S., & Coskun, A. F. (2023) · Nature Communications · doi:10.1038/s41467-023-43917-5 · Cited by 168 · see citing works
“31a), projected into their t-distributed stochastic neighbor (t-SNE) embedding space, and their pseudotime ordering was inferred by determining the cell state and calculating the respective probability of differentiating into each terminal state 71 (Supplementary Fig.”
Bardot, E., & Hadjantonakis, A. (2020) · Mechanisms of Development · doi:10.1016/j.mod.2020.103617 · Cited by 144 · see citing works
“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).”
BackgroundJanosevic, D., Myslinski, J., McCarthy, T. W., Zollman, A., Syed, F., Xuei, X., Gao, H., Liu, Y., Collins, K. S., Cheng, Y., et al. (2021) · eLife · doi:10.7554/elife.62270 · Cited by 149 · see citing works
“We performed a separate temporal ordering analysis of S1, S2, and S3 proximal tubule segments across all time points and visualized using t-SNE, produced by Harmony and Palantir R packages (Setty et al., 2019).”
MethodThe harsh microenvironment in early breast cancer selects for a Warburg phenotype
Damaghi, M., West, J., Robertson‐Tessi, M., Xu, L., Ferrall‐Fairbanks, M. C., Stewart, P. A., Persi, E., Fridley, B. L., Altrock, P. M., Gatenby, R. A., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011342118 · Cited by 143 · see citing works
“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).”
InfluentialDeep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells
Gayoso, A., Weiler, P., Lotfollahi, M., Klein, D., Hong, J., Streets, A., Theis, F. J., & Yosef, N. (2023) · Nature Methods · doi:10.1038/s41592-023-01994-w · Cited by 140 · see citing works
“Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty.”
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 · see citing works
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 136 · see citing works
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 · see citing works
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 · see citing works
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 133 · see citing works
“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 · see citing works
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 · see citing works
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 122 · see citing works
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 117 · see citing works
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 119 · see citing works
“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 112 · see citing works
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 118 · see citing works
“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 117 · see citing works
“3.1) 95 .”
InfluentialFaure, L., Soldatov, R., Kharchenko, P. V., & Adameyko, I. I. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac746 · Cited by 113 · see citing works
“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 114 · see citing works
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 115 · see citing works
Zeng, A. G. X., Iacobucci, I., Shah, S., Mitchell, A., Wong, G., Bansal, S., Chen, D., Gao, Q.-S., Kim, H., Kennedy, J. A., et al. (2025) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-24-0342 · Cited by 74 · see citing works
Zhou, P., Wang, S., Li, T., & Nie, Q. H. (2021) · Nature Communications · doi:10.1038/s41467-021-25548-w · Cited by 102 · see citing works
“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 106 · see citing works
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 · see citing works
“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 106 · see citing works
“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 112 · see citing works
“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 · see citing works
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 115 · see citing works
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 101 · see citing works
“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 · see citing works
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 · see citing works
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 92 · see citing works
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 97 · see citing works
“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 95 · see citing works
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 96 · see citing works
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 99 · see citing works
“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 92 · see citing works
“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 93 · see citing works
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 · see citing works
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 84 · see citing works
“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 84 · see citing works
Li, Q. (2023) · Genome biology · doi:10.1186/s13059-023-02988-9 · Cited by 94 · see citing works
Lotto, J., Drissler, S., Cullum, R., Wei, W., Setty, M., Bell, E. M., Boutet, S. C., Nowotschin, S., Kuo, Y.-Y., Garg, V., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.012 · Cited by 86 · see citing works
Self-citationSingle-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells
Lee, M., Lee, E., Han, S. K., Choi, Y. H., Kwon, D.-I., Choi, H., Lee, K., Park, E. S., Rha, M., Joo, D. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18155-8 · Cited by 86 · see citing works
MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells
Lynch, A. W., Theodoris, C. V., Long, H. W., Brown, M., Liu, X. S., & Meyer, C. A. (2022) · Nature Methods · doi:10.1038/s41592-022-01595-z · Cited by 80 · see citing works
Roux, A. E., Zhang, C., Paw, J. S., Zavala‐Solorio, J., Malahias, E., Vijay, T., Kolumam, G. A., Kenyon, C. J., & Kimmel, J. C. (2022) · Cell Systems · doi:10.1016/j.cels.2022.05.002 · Cited by 83 · see citing works
Gaglia, G., Burger, M. L., Ritch, C. C., Rammos, D., Dai, Y., Crossland, G. E., Tavana, S. Z., Warchol, S., Jaeger, A. M., Naranjo, S., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.03.015 · Cited by 84 · see citing works
Multimodal characterization of murine gastruloid development
Suppinger, S., Zinner, M., Aizarani, N., Lukonin, I., Ortiz, R., Azzi, C., Stadler, M. B., Vianello, S., Palla, G., Kohler, H., et al. (2023) · Cell stem cell · doi:10.1016/j.stem.2023.04.018 · Cited by 83 · see citing works
Chen, Z., King, W. C., Hwang, A., Gerstein, M., & Zhang, J. (2022) · Science Advances · doi:10.1126/sciadv.abq3745 · Cited by 82 · see citing works
Gan, S., Macalinao, D. G., Shahoei, S. H., Tian, L. H., Jin, X., Basnet, H., Bibby, C., Muller, J., Atri, P., Seffar, E., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.08.015 · Cited by 94 · see citing works
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 · see citing works
- 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 · 2018: 1
- 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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