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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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“Nonetheless, powerful computational inference frameworks have emerged that support the move from descriptive studies of cellular states to models of dynamic events (Bendall et al., 2014; Chen et al., 2016b; Guo et al., 2017; Haghverdi et al., 2016; Herring et al., 2018; Qiu et al., 2017; Setty et al., 2019; Setty et a…”
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The covariance environment defines cellular niches for spatial inference
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“Subsequently, to extract discrete time points from pseudotemporal ordering, different techniques can be employed: cell cluster time-point assignment (Slingshot [87], TSCAN [88], or Palantir [89]), partition of pseudotemporal trajectories into discrete time points, or differential gene expression timepoint assignment (…”
MethodRecent advances in trajectory inference from single-cell omics data
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“We got the Fore-brain data [1], the Bonemarrow data [19], the Dentategyrus LaManno data [1, 20], the Pancreas data [3, 21], the Gastrulation erythroid data [22], the Dentategyrus Hoch-gerner data [3, 20], the Gastrulation E7.”
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“Pseudo-time along human haematopoietic lineages was computed using the Palantir package (v.1.0.0) 78 .”
InfluentialMYC Promotes Bone Marrow Stem Cell Dysfunction in Fanconi Anemia
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“Importantly, trajectory analysis 111 identified the emergence of macrophages with a LAM profile ( TREM2 + SPP1 + FABP5 + LGALS3 + ) and a possible monocyte origin ( CD14 + FCN1 + ), suggesting that cardiomyopathy is associated with monocyte-derived LAM polarization 110 .”
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InfluentialSingle-cell immune repertoire analysis
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“As implemented in Palantir, clustering of the gene expression trendswas generated based on the Phenograph algorithm.”
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“0).(61) Then, we smoothed the gene expression via R package gam (1.”
MethodHess, A., Gentile, S. D., Saad, A. B., Rahman, R., Habboub, T., Pratt, D. S., & Mullen, A. C. (2023) · The EMBO Journal · doi:10.15252/embj.2023113898 · Cited by 56
“We investigated the projection of our steatosis (OA), steatohepatitis (PA), and fibrosis model (TGF-β 1) on a force-directed layout in conjunction with Palantir (Setty et al , 2019) trajectory inference analysis to dissect this process.”
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Papaioannou, N. E., Salei, N., Rambichler, S., Rengarajan, K. R., Popović, J., Küntzel, V., Lehmann, C. H. K., Fiancette, R., Salvermoser, J., Gajdasik, D. W., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20659-2 · Cited by 51
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“DPT is robust to noise and scalable to larger datasets but not suitable to study complex differentiation trajectories such as those with several branching points unless multiple diffusion components are considered (45).”
BackgroundMethodKorshoj, L. E., & Kielian, T. (2024) · Nature Communications · doi:10.1038/s41467-024-54581-8 · Cited by 60
“The Palantir package was used for trajectory analysis 40 .”
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Thoms, J. A. I., Truong, P., Subramanian, S., Knezevic, K., Harvey, G., Huang, Y., Seneviratne, J. A., Carter, D. R., Joshi, S. M., Skhinas, J. N., et al. (2021) · Blood · doi:10.1182/blood.2020009707 · Cited by 42
microRNA-132 regulates gene expression programs involved in microglial homeostasis
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“To further understand the inhibitory effect of miR-132 on the DAM response, we performed single-cell trajectory analysis using Palantir.(103) Pseudotime ordering of the microglial population revealed the existence of two distinct phenotypic routes from homeostatic toward cellular states corresponding to DAM, IRM, and…”
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Wu, S., Yu, Y., Liu, C., Zhang, X., Zhu, P., Peng, Y., Yan, X., Li, Y., Peng, H., Li, Q., et al. (2022) · Cell Discovery · doi:10.1038/s41421-022-00394-2 · Cited by 44
Johnson, A., Laterra, J., & López-Bertoni, H. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.995498 · Cited by 45
Metzner, E., Southard, K. M., & Norman, T. M. (2024) · Cell Systems · doi:10.1016/j.cels.2024.12.002 · Cited by 45
A single-nucleus transcriptomic atlas of the adult Aedes aegypti mosquito
Goldman, O. V., DeFoe, A. E., Qi, Y., Jiao, Y., Weng, S., Wick, B. D., Houri-Zeevi, L., Lakhiani, P., Morita, T., Razzauti, J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.10.008 · Cited by 46
Xiang, N., Xu, H., Zhou, Z., Wang, J., Cai, P., Wang, L., Tan, Z., Zhou, Y., Zhang, T., Zhou, J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107943 · Cited by 45
Yang, D., Cho, H., Tayyebi, Z., Shukla, A., Luo, R., Dixon, G., Ursu, V., Stransky, S., Tremmel, D. M., Sackett, S. D., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00946-4 · Cited by 40
Molecular characterization of human cytomegalovirus infection with single-cell transcriptomics
Schwartz, M., Shnayder, M., Nachshon, A., Arazi, T., Kitsberg, Y., Samia, R. L., Lavi, M., Kuint, R., Tsabari, R., & Stern‐Ginossar, N. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01325-x · Cited by 38
Tobon, A., Shyti, R., Villa, C. E., Cheroni, C., Fuentes-Bravo, P., Trattaro, S., Caporale, N., Troglio, F., Tenderini, E., Mihailovich, M., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh2726 · Cited by 40
Du, C., Liu, C., Yu, K., Zhang, S., Fu, Z., Chen, X., Liao, W., Chen, J., Zhang, Y., Wang, X., et al. (2024) · Cell stem cell · doi:10.1016/j.stem.2024.07.009 · Cited by 42
Wu, Z., Gao, S., Diamond, C. E., Kajigaya, S., Chen, J., Shi, R., Palmer, C. L., Hsu, A. P., Calvo, K. R., Hickstein, D. D., et al. (2020) · Blood Advances · doi:10.1182/bloodadvances.2019001352 · Cited by 40
Jiang, Y., Harigaya, Y., Zhang, Z., Zhang, H., Zang, C., & Zhang, N. R. (2022) · Cell Systems · doi:10.1016/j.cels.2022.08.004 · Cited by 41
Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon
Otto, D. J., Jordan, C., Dury, B., Dien, C., & Setty, M. N. (2024) · Nature Methods · doi:10.1038/s41592-024-02302-w · Cited by 38
“For a more comprehensive understanding of the density landscape, we utilized our trajectory detection algorithm Palantir 21 to determine pseudo-temporal ordering and cell-fate propensities of cells (Supplementary Fig.”
InfluentialSelf-citationTemporal recording of mammalian development and precancer
Islam, M., Yang, Y., Simmons, A. J., Shah, V. M., Pavan, M. K., Xu, Y., Tasneem, N., Chen, Z., Trinh, L. T., Molina, P., et al. (2024) · Nature · doi:10.1038/s41586-024-07954-4 · Cited by 41
Critical role for a high-plasticity cell state in lung cancer
Chan, J. E., Pan, C., Rub, J., Guzman, G., Bastl, K., Brown, E., Zhang, Z., Styers, H. C., Hartmann, G. G., Li, Z., et al. (2026) · Nature · doi:10.1038/s41586-025-09985-x · Cited by 38
Michki, S. N., Li, Y., Sanjasaz, K., Zhao, Y., Shen, F. Y., Walker, L. A., Cao, W., Lee, C., & Cai, D. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109039 · Cited by 35
“Using the Markov chain-based pseudotime analysis algorithm Palantir was a natural choice as Markov chains describe discrete transitions that occur randomly based upon a continuous probability distribution (Setty et al., 2019).”
Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1
Tejwani, L., Ravindra, N. G., Lee, C., Cheng, Y., Nguyen, B., Luttik, K. P., Ni, L., Zhang, S., Morrison, L. M., Gionco, J. T., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.039 · Cited by 37
Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity
Quach, H., Farrell, S., Wu, M. J. M., Kanagarajah, K. R., Leung, J. W.-H., Xu, X., Kallurkar, P. S., Turinsky, A. L., Bear, C. E., Ratjen, F. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50281-5 · Cited by 46
Bonev, B., Castelo‐Branco, G., Chen, F., Codeluppi, S., Corces, M. R., Fan, J., Heiman, M., Harris, K., Inoue, F., Kellis, M., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01806-0 · Cited by 44
“Additionally, manifold learning and trajectory inference enable us to align individuals along a pseudotime of disease progression and infer the intricate cellular dynamics underlying the disease process 8,32,58 .”
Interferon-γ orchestrates leptomeningeal anti-tumour response
Remšík, J., Tong, X., Kunes, R., Li, M., Estrera, R., Snyder, J., Thomson, C., Osman, A. M., Chabot, K., Sener, U. T., et al. (2025) · Nature · doi:10.1038/s41586-025-09012-z · Cited by 44
Self-citationTargeting modulated vascular smooth muscle cells in atherosclerosis via FAP-directed immunotherapy
Amrute, J., Jung, I., Yamawaki, T., Lin, W.-L., Bredemeyer, A. L., Diekmann, J., Hayat, S., Zhang, X., Wakefield, D. L., Luo, X., et al. (2026) · Science · doi:10.1126/science.adx1736 · Cited by 38
Luo, Y., Wang, J., Li, K., Li, M., Xu, S., Liu, X., Zhang, Z., Xu, X. M., Zhang, Y., Pan, J., et al. (2022) · Cell & Bioscience · doi:10.1186/s13578-022-00795-6 · Cited by 39
“Taken together, our Palantir analysis supports the speculation that neonate peritoneal B1 cell development apparently comprises two trajectories: a pre-BCR dependent developmental trajectory (C1-C2 to C3-C4) as well as a pre-BCR independent developmental trajectory (C1 to C4).”
MethodInfluentialBayesian Inference of RNA Velocity from Multi-Lineage Single-Cell DataPreprint
Gu, Y., Blaauw, D. T., & Welch, J. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.08.499381 · Cited by 37
“Many pseudotime methods also aim to infer a tree or graph structure that represents the underlying structure of the developmental process [6, 11, 32, 8, 26].”
BackgroundSoraggi, S., Riera, M., Meyts, E. R., Schierup, M. H., & Almstrup, K. (2020) · Human Genetics · doi:10.1007/s00439-020-02116-8 · Cited by 36
Impaired BCAA catabolism in adipose tissues promotes age-associated metabolic derangement
Han, H.-S., Ahn, E., Park, E. S., Huh, T., Choi, S., Kwon, Y., Choi, B. H., Lee, J., Choi, Y. H., Jeong, Y. L., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00460-8 · Cited by 40
Kim, J., Kim, S., Lee, S., Jo, B., Oh, J. Y., Kwon, E., Kim, K.-T., Adpaikar, A. A., Kim, E., Jung, H.-S., et al. (2023) · Science Advances · doi:10.1126/sciadv.adi8454 · Cited by 37
Rengaraj, D., Cha, D. G., Lee, H. J., Lee, K. Y., Choi, Y. H., Jung, K. M., Kim, Y., Choi, H. J., Choi, H., Yoo, E., et al. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.03.040 · Cited by 36
“Developmental trajectories of human FGCs were estimated by using the Palantir python package [23].”
MethodInfluentialCell2fate infers RNA velocity modules to improve cell fate prediction
Aivazidis, A., Memi, F., Kleshchevnikov, V., Er, S., Clarke, B., Stegle, O., & Bayraktar, O. A. (2025) · Nature Methods · doi:10.1038/s41592-025-02608-3 · Cited by 50
“To test the ability of different methods to resolve complex transcriptional dynamics, we also examined mouse erythroid maturation 18 and human bone marrow 19 : two datasets that feature multiple transcriptional boosts across cellular trajectories 12 .”
Entropy as a measure of variability and stemness in single-cell transcriptomics
Gandrillon, O., Gaillard, M., Espinasse, T., Garnier, N., Dussiau, C., Kosmider, O., & Sujobert, P. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.009 · Cited by 35
Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity
Zeng, T., & Dai, H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00629 · Cited by 33
“For the cell order, the start or end point of pseudo-time is still a manual judgment, and the auto-determination of these time points will render these methods more flexible and applicable (e.g., temporal driving for disease causality) (Yu et al., 2017; Wang et al., 2018; Setty et al., 2019).”
MethodJiang, W., Glaeser, J. D., Salehi, K., Kaneda, G., Mathkar, P. P., Wagner, A., Ho, R., & Sheyn, D. (2022) · iScience · doi:10.1016/j.isci.2022.104504 · Cited by 38
“…RNA sequencing (scRNA-seq) allow for creation of cell atlases that unveil rare sub-populations, delineate cellular heterogeneity, identify new markers, and predict developmental trajectories (Ho et al., 2021; Kelly et al., 2020; Mathys et al., 2019; Setty et al., 2019; Stark et al., 2019).”
BackgroundChoi, C., Jeong, Y. L., Park, K.-M., Kim, M., Kim, S., Jo, H., Lee, S., Kim, H., Choi, G., Choi, Y. H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47108-8 · Cited by 37
“0.0) python package for monocyte/macrophage and APC/Adi-pocyte cell types 69 .”
InfluentialSingh, R., Wu, A. P., Mudide, A., & Berger, B. (2024) · Cell Systems · doi:10.1016/j.cels.2024.04.005 · Cited by 35
Design Approaches for Generating Organ Constructs
Xia, Y. F., & Belmonte, J. C. I. (2019) · Cell stem cell · doi:10.1016/j.stem.2019.05.016 · Cited by 32
Wang, W. X., & Lefebvre, J. L. (2022) · Nature Communications · doi:10.1038/s41467-022-30977-2 · Cited by 35
“…for analyses of single cell transcriptomics datasets, where cells dissociated from developing tissue at a fixed time contain a spectrum of cellular states due to the asynchronous nature of development (Figure 6A; Setty et al. 2019; Trapnell et al. 2014; Saelens et al. 2019; Serra et al. 2019).”
BackgroundMethodInference of cell state transitions and cell fate plasticity from single-cell with MARGARET
Pandey, K., & Zafar, H. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac412 · Cited by 34
“Palantir (9) uses Max-min sampling (33) to compute waypoints which is more efficient than random sampling but might require a large number of waypoints to cover the embedding space.”
BackgroundMethodInfluentialA Guide to Trajectory Inference and RNA Velocity
Weiler, P., den Berge, K. V., Street, K., & Tiberi, S. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2756-3_14 · Cited by 37
Spatial transition tensor of single cells
Zhou, P., Bocci, F., Li, T., & Nie, Q. H. (2024) · Nature Methods · doi:10.1038/s41592-024-02266-x · Cited by 36
“In addition, we applied STT to blood 41 and pancreas 7 development datasets and found its capability to resolve complex state transitions, and its multistability tensor kernel is consistent with CellRank analysis (Supplementary Figs.”
Mapping lineage-traced cells across time points with moslin
Lange, M., Piran, Z., Klein, M., Spanjaard, B., Klein, D., Junker, J. P., Theis, F. J., & Nitzan, M. (2024) · Genome biology · doi:10.1186/s13059-024-03422-4 · Cited by 32
“We supply CellRank 2 with W, GW, and LineageOT couplings, as well as with the Pal-antir [57] and CytoTRACE [71, 72] pseudotimes.”
MethodInfluentialTsui, C., Heyden, L., Wen, L., de Graça, C. G., Potemkin, N., Frolov, A., Rawlinson, D., Qin, L., Wimmer, V. C., Hadian-Jazi, M., et al. (2025) · Nature Immunology · doi:10.1038/s41590-025-02276-7 · Cited by 37
Iwayama, T., Iwashita, M., Miyashita, K., Sakashita, H., Matsumoto, S., Tomita, K., Bhongsatiern, P., Kitayama, T., Ikegami, K., Shimbo, T., et al. (2022) · Development · doi:10.1242/dev.201203 · Cited by 31
Athanasouli, P., Balli, M., De Jaime-Soguero, A., Boel, A., Papanikolaou, S., van der Veer, B. K., Janiszewski, A., Vanhessche, T., Francis, A., El Laithy, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36914-1 · Cited by 31
Motevasseli, M., Darvishi, M., Khoshnevisan, A., Zeinalizadeh, M., Saffar, H., Bayat, S., Najafi, A., Abbaspour, M. J., Mamivand, A., Olson, S. B., et al. (2024) · Acta Neuropathologica Communications · doi:10.1186/s40478-024-01837-5 · Cited by 33
“We used Palantir v.1.3.1 [23] to generate pseudotime trajectories using Johnson et al . scRNA-seq dataset [21].”
MethodInfluentialDikiy, S., Ghelani, A. P., Levine, A. G., Martis, S., Giovanelli, P., Wang, Z.-M., Beroshvili, G., Pritykin, Y., Krishna, C., Huang, X. J., et al. (2025) · Nature Immunology · doi:10.1038/s41590-024-02075-6 · Cited by 36
“The 30 principal components were used as input for the Python-based algorithm ‘Palantir’ to determine ‘pseudotime’ and ‘entropy’ values 42 .”
Cell identity specification in plants: lessons from flower development
Xu, X., Smaczniak, C., Muiño, J. M., & Kaufmann, K. (2021) · Journal of Experimental Botany · doi:10.1093/jxb/erab110 · Cited by 32
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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