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Characterization of cell fate probabilities in single-cell data with Palantir
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Abstract
Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir's resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.
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“Gene expression trends along pseudotime were calculated based on branch probabilities and generalized additive models, showing the stepwise transcriptional progression of endodermal and endothelial lineages along pseudotime using Palantir44.”
From Cell diversity and plasticity during atrioventricular heart valve EMTs (2023) · cites this paper as [44]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Based on the average diffusion distance10,11 from the bipotent inner cell mass (ICM), TE cells were substantially further away (13.9) from ICM than either EPI (0.41) or PrE (2.0) (Extended Data Fig. 4b, Methods)”
From The emergent landscape of the mouse gut endoderm at single-cell resolution (2019) · cites this paper as [11]
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In the citing paper
“A key distinguishing feature of Palantir is that rather than treating lineage decisions as bifurcations, cell fate choices are modeled as continuous probabilistic processes.”
From The emergent landscape of the mouse gut endoderm at single-cell resolution (2019) · cites this paper as [11]
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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“Unlike pseudotime trajectories (Table S1), 27,34,36,42,43 which provide the order of single cells along a sometimes branching trajectory (Figures S4A and S4B), scGETs instead provide heterogeneous gene induction characteristics such as fold changes, peak amplitudes, induction speeds, or accumulated…”
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“Cell differentiation trajectories were estimated based on pseudotime calculated by three different algorithms: Palantir [30], Monocle3 [31], and SlingShot [32].”
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“A diffusion map was computed using the implementation in Palantir ( Setty et al., 2019 ) on the natural log-normalized count matrix.”
InfluentialSelf-citationAlveolar epithelial cell plasticity and injury memory in human pulmonary fibrosisPreprint
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Genetic Mapping of Monocyte Fate Decisions Following Myocardial Infarction
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“36 The resulting trajectories (Figures S6C and S6D) were consistent with those obtained using Monocle.”
Precursor central memory versus effector cell fate and naïve CD4+ T cell heterogeneity
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Self-citationDecoding the human prenatal immune system with single-cell multi-omics
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“Valuable downstream analytical approaches include using gradients in gene expression to infer relationships between cell states 30,31 and using patterns of receptor and ligand expression in neighbouring cell types to infer interaction 32,33 .”
Spatiotemporal dynamics of the developing zebrafish enteric nervous system at the whole-organ level
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“These studies also uncover insights into the complexity and dynamics of cell states longitudinally (Lago-64 Baldaia et al., 2022; Setty et al., 2019; Soreq et al., 2017).”
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“To decipher the regulatory gene pathways driving NK cell differentiation at the transcriptional level, we implemented two different methods to calculate pseudotime, namely Palantir 25 and RNA velocity-144 based pseudotime 26, 27 .”
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“Probabilistic methods such as Palantir (Setty et al. 2019) and CSHMM (Lin and Bar-Joseph 2019) introduce uncertainty about the pseudotime of cells or their belonging to certain branches of the trajectory.”
Dissection and integration of bursty transcriptional dynamics for complex systems
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“…Gata2 is highly expressed in progenitors, with expression declining after erythroid commitment [51], and that Ccnd2 expression is anti-correlated cell (HSC) differentiation [32], TopicVelo used a 10-topic model to recover the expected trajectories reversals in directionality, as did scVelo (Fig.”
BackgroundModel-based inference of RNA velocity modules improves cell fate predictionPreprint
Aivazidis, A., Memi, F., Kleshchevnikov, V., Clarke, B., Stegle, O., & Bayraktar, O. A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.03.551650 · Cited by 11
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Characterization of cancer-associated adipocytes by Raman spectroscopy and trajectory inference
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“These include principal curve methods for modelling smooth trajectories in high-dimensional spaces [39], clustering-based approaches for connecting grouped cells, graph-based techniques [40], and probabilistic models that account for the stochastic nature of state transitions [41].”
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“For trajectory analysis, we utilized Palantir [43], an algorithm designed to align cells along differentiation trajectories.”
HALO: hierarchical causal modeling for single cell multi-omics data
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“The common trajectory algorithms such as SlingShot, Palantir, STREAM, and Monocle3 struggle with complex pathways and their parameter choices can significantly impact results (Chen et al., 2019; Setty et al., 2019; Street et al., 2018 ).”
MethodScalable identification of lineage-specific gene regulatory networks from metacells with NetID
Wang, W., Wang, Y., Lyu, R., & Grün, D. (2024) · Genome biology · doi:10.1186/s13059-024-03418-0 · Cited by 8
“We applied Palantir [17], which utilizes pseudotime information, to identify two dominating terminal cell states corresponding to the erythrocyte and the neutrophil lineage, and calculated the probability of each cell to transition to either state.”
MethodInfluentialNavigating Nongenetic Plasticity in Cancer Drug Resistance
Sun, H., Kumari, N., Melzer, M. E., Backman, V., & Goyal, Y. (2024) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-071924-084617 · Cited by 9
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis
Zhang, Z., Sun, Y., Peng, Q., Li, T., & Zhou, P. (2025) · Entropy · doi:10.3390/e27050453 · Cited by 18
“Random Walk with Directionality Building on the inferred DPT, Palantir [57] introduces directionality into the cellular random walk, which is further developed in [58,59].”
InfluentialLu, H., Wang, H., Wang, Q., Huang, D., Han, Y., Wang, H., Jiang, P., Qian, X., Mao, L., Yang, M., et al. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.217853 · Cited by 9
TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference
Ge, M., Miao, J., Qi, J., Zhou, X., & Lin, Z. (2025) · Nature Communications · doi:10.1038/s41467-025-61628-x · Cited by 13
“The details are discussed in Supplementary Notes 6 and 7.”
Cross-species comparison reveals therapeutic vulnerabilities halting glioblastoma progression
Foerster, L. C., Kaya, O., Wüst, V., Danciu, D.-P., Akcay, V., Bekavac, M., Ziegler, K. C., Stinchcombe, N., Tang, A., Kleber, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62528-w · Cited by 11
“For each of CytoTrace 103 , Monocle3 104 , and Palantir 105 , pseudotimes for the 51-GBM cohort samples were determined according to the basic example in the respective documentation.”
Wang, J., Zheng, A., Liu, N., Tan, Z. Y., Shi, Y., Ma, T., Luo, S., Zhu, L., Zhou, Z., Yuan, F., et al. (2025) · Annals of the Rheumatic Diseases · doi:10.1016/j.ard.2025.08.015 · Cited by 10
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“Note that some tools (Palantir and MIRA 7,73 ) do not infer differentiation trees and can, therefore, not be evaluated here.”
Ferret model of bleomycin-induced lung injury shares features of human idiopathic pulmonary fibrosis
Wu, S., Driver, I., Luo, M., Miyazaki, H., Shambhu, S., Popov, D., Yang, L., Wang, J., Ma, J., Guo, J., et al. (2025) · npj Regenerative Medicine · doi:10.1038/s41536-025-00440-z · Cited by 11
“3.3 software 86,87 .”
InfluentialTemporary serine protease inhibition and the role of SPINK2 in human bone marrow
Barresi, V., Bella, V. D., Nigro, L. L., Privitera, A. P., Bonaccorso, P., Scuderi, C., & Condorelli, D. F. (2023) · iScience · doi:10.1016/j.isci.2023.106949 · Cited by 10
“…particularly highlighted in Kazal domain including 2 iScience 26, 106949, June 16, 2023 (A) Uniform manifold approximation and projection (UMAP) graph shows the CD34+ cell clusters within human bone marrow as annotated by Setty et al.19 (B) UMAP graph showing SPINK2 expression in CD34+ cells.”
BackgroundMethodInfluentialCharacterization of cell-fate decision landscapes by estimating transcription factor dynamics
Jiménez, S., Schreiber, V., Mercier, R., Gradwohl, G., & Molina, N. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100512 · Cited by 12
“…we assumed, like other studies, that the process of a cell changing states along a trajectory until it reaches a final fate can be understood as a particle diffusing on a volume; 8,69 but, unlike them, we infused the direction of the differentiation as a drift to bias the transition probabilities.”
Leveraging gene correlations in single cell transcriptomic data
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S. X., Nie, Q. H., & Lander, A. D. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05926-z · Cited by 11
Extrapolating heterogeneous time-series gene expression data using Sagittarius
Woicik, A., Zhang, M., Chan, J., Ma, J., & Wang, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00679-5 · Cited by 7
“Sagittarius and pseudotime cell fate models like Monocle, 13 Palantir, 87 and Slingshot 88 is that these models reconstruct cell lineage within the bounds of one or more originator states and one or more terminal states, while Sagittarius is able to extrapolate beyond the range of timepoints in the…”
BackgroundGraham, R. E., Zheng, R., Wagner, J., Unciti‐Broceta, A., Hay, D. C., Forbes, S. J., Gadd, V. L., & Carragher, N. O. (2025) · iScience · doi:10.1016/j.isci.2025.111871 · Cited by 8
Diffusive topology preserving manifold distances for single-cell data analysis
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Single-Cell Transcriptomic Approaches for Decoding Non-Coding RNA Mechanisms in Colorectal Cancer
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“They utilized the Milo algorithm to reveal differential abundance of cell clusters during progression and employed the Palantir algorithm to align cells along differentiation trajectories [66,67].”
Molecular profiling of adult C. elegans glia across sexes by single-nuclear RNA-seq
Purice, M. D., Quitevis, E. J. A., Manning, R. S., Severs, L. J., Tran, N., Sorrentino, V., Finkbeiner, C., Wu, F., Zager, M. A., Setty, M. N., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.05.013 · Cited by 10
Self-citationBacteria and tumor debris induced pancreatic cancer progression via the NF-κB signaling pathway
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Spatial Transcriptomics of Human Decidua Identifies Molecular Signatures in Recurrent Pregnancy Loss
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“Using Palantir [31], we inferred the trajectory of dNK cells, which confirmed the observed transformation trend starting from dNK2 cells and progressing toward dNK1 cells (Figure S8A).”
Cortical development dynamics across autism spectrum disorder mouse models
Schwarz, L. A., Dotter, C. P., Isaev, S. V., Lisi, M., Malzl, D., Bueschl, C., Ladstätter, S., Oliveira, B., Barel, M., Basilico, B., et al. (2026) · Nature · doi:10.1038/s41586-026-10679-1 · Cited by 10
Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation
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“To further confirm MIDAA’s flexibility and potential to adapt to different input data types, we analyzed a distinct cohort of CD34 + cells, this time generated with 10 × GEX + ATAC libraries [30].”
Medina-Cano, D., Islam, M. T., Petrova, V., Dixit, S., Balic, Z., Yang, M. G., Stadtfeld, M., Wong, E. S. W., & Vierbuchen, T. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.08.001 · Cited by 9
Astrocyte Regional Specialization is Shaped by Postnatal DevelopmentPreprint
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“2, 3 ), we examined the developmental trajectory of astrocytes in pseudotime, a prediction of position along a low-dimensional developmental trajectory based on RNA expression only, using Palantir 68 .”
Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods
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“5), Palantir 42 and Scarf 43 .”
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“After data preprocessing, diffusion maps analysis and MAGIC imputation were performed using Palantir package (v.1.3.3) [68].”
InfluentialGao, M., Barile, M., Chabra, S., Haltalli, M. L. R., Calderbank, E. F., Chao, Y., Zheng, W., Wilson, N. K., Laurenti, E., Göttgens, B., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63150-6 · Cited by 16
Decoding cell state transitions driven by dynamic cell–cell communication in spatial transcriptomics
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“Advances in single-cell RNA sequencing (scRNA-seq) techniques have provided an unprecedented opportunity for studying pseu-dotemporal dynamics of CSTs at single-cell resolution 3–5 . scRNA-seq data-driven trajectory inference methods, such as PAGA 6 , Slingshot 7 , Monocle2/3 8,9 and Palantir 10 , utilize similarity o…”
Generative models of cell dynamics: from Neural ODEs to flow matching
Richter, T., Wang, W., Palma, A., & Theis, F. J. (2026) · Communications Biology · doi:10.1038/s42003-026-09758-w · Cited by 10
“Techniques like Monocle 21,22 ,Slingshot 23 ,TSCAN 24 ,Palantir 25 ,andDiffusionpseudotime 26,27 utilize a variety of methods, including ICA, PCA, DDRtree embedding 28 , and diffusion components to predict cellular trajectories.”
Cho, H., Kuo, Y., & Rockne, R. C. (2022) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022395 · Cited by 12
“Setty et al present a method to compute cell fate probabilities [42], which may also be achieved by inferring cell state-transition dynamics with lineage trees [43].”
MethodHua, Y., Wu, N. Q., Miao, J., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1091336 · Cited by 7
“Python package Palantir (34) was used to calculate diffusion map and diffusion components based on Harmony space, then cells were visualized by tSNE.”
MethodAhn, J.-Y., Kim, S., Kim, C. R., Lee, J., Kim, J. M., Klompstra, T. M., Choi, Y. H., Jeon, Y., Na, Y., Kim, J.-S., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.08.009 · Cited by 7
The fibroblast epigenome underlies SS18::SSX-mediated transformation in synovial sarcoma
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“To obtain fi broblast trajectories, force-directed layout (FDL) plots were computed using scanpy, as per the Palantir package for Python 75 .”
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Quantifying cancer cell plasticity with gene regulatory networks and single-cell dynamics
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“…on transcriptomic similarity, empirically determining transition paths in the system (Trapnell et al., 2014; Guo et al., 2016; Haghverdi et al., 2016; Welch et al., 2016; Qiu et al., 2017; Herring et al., 2018; Saelens et al., 2018; Setty et al., 2019; Wolf et al., 2019; Stassen et al., 2021).”
BackgroundOCT4 expression in human embryonic stem cells: spatio-temporal dynamics and fate transitions
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Charting the emergent organotypic landscape of the mammalian gut endoderm at single-cell resolutionPreprint
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Self-citationDeep dynamical modelling of developmental trajectories with temporal transcriptomicsPreprint
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“Examining datasets from several tissues from both mouse and human [2, 11–13, 39, 40], we see a substantial proportion of genes with minimal unspliced reads, and this proportion increases for transcription factors and highly variable genes (Figure 1A), such that often over a quarter of genes…”
BackgroundMethodDefining Cardiac Recovery at Single Cell Resolution
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scMFG: a single-cell multi-omics integration method based on feature grouping
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“Using Palantir [54], we ordered the cells along a pseudotime trajectory with joint embedding (Fig.”
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Thalamocortical transcriptional gates coordinate memory stabilization
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“Validated algorithms for scRNA sequencing analysis were used, Palantir (v1.”
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“Finally, Palantir is designed to model the trajectories of differentiating cells by treating the determination of cell fate as a probabilistic process, using entropy as a key metric to quantify cell plasticity as cells progress [11].”
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On the Mathematics of RNA Velocity II: Algorithmic Aspects
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“…that determine the ordering of cell states along a trajectory, based on their gene expression profiles using a pseudotime or actual time approach, 10–14 have been used to examine the cell decision-making process by identifying genes that are differentially expressed between trajectory branches.”
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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