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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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“Palantir and PAGA’s estimates were more correlated with each other and less correlated with the other estimates, which indicates that non-spatial signals may exist in some of the genes that were not selected by GLISS.”
MethodInfluentialSingle-cell fate decisions of bipotential hematopoietic progenitors
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“Setty M, Kiseliovas V, Levine J, et al. Characterization of cell fate probabilities in single-cell data with Palantir.”
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“1 (encoded by SPI1) and GATA2 drives myeloid/lymphoid versus erythroid/megakaryocyte lineage commitments [30,31].”
BackgroundSingle cell RNA sequencing research in maternal fetal interface
Chen, Q., Shan, D., Xie, Y., Luo, X., Wu, Y., Chen, Q., Dong, R., & Hu, Y. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1079961 · Cited by 21
“(2021) practical high-resolution pseudo-temporal prediction algorithm Palantir (Setty et al., 2019) postulated that dNKp differentiates into dNK1, dNK2 and dNK3 through three developmental branches, with dNKp differentiation into dNK3 being unique to RSA.”
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“…“ free ” information from obtained data, including: RNA dynamics (e.g., scVelo [104], RNA velocity [105]), developmental trajectories (Monocole [106], Slingshot [79], Palantir [107], CellRank [108]), tools that execute differential gene expression (e.g., MILO [109]), others that integrate…”
BackgroundMyo-differentiation reporter screen reveals NF-Y as an activator of PAX3–FOXO1 in rhabdomyosarcoma
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“In addition, these meth-ods may not fully capture the underlying biological process at specific tipping points or intervals, which could be particularly relevant in understanding the mechanisms of cell fate or differentiation [33, 34], and tumor progression [14].”
BackgroundHu, H., & Quon, G. (2024) · Nature Communications · doi:10.1038/s41467-024-53971-2 · Cited by 19
“The second integrated strategy leverages a larger unimodal scATAC-seq dataset by estimating ATAC cell states using the trained scPair model, and passing them through the trajectory inference framework 62 to assign each unimodal scATAC-seq cell their scUnimodal ATAC pseudotime (Fig.”
InfluentialA spatial human thymus cell atlas mapped to a continuous tissue axisPreprint
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. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.25.562925 · Cited by 19
Zhang, H., Qian, Y., Zhang, Y., Zhou, X., Shen, S., Li, J., Sun, Z., & Wang, W. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00764-x · Cited by 24
“Basedonpseudo-timeordering,weassignedeachcellstatetheprobabilityof differentiatingintoeachterminalstate 106 (https://github.com/JarningGau/ scutilsR).”
Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace
Tayyebi, Z., Pine, A. R., & Leslie, C. S. (2024) · Nature Methods · doi:10.1038/s41592-024-02274-x · Cited by 18
Building and analyzing metacells in single-cell genomics data
Bilous, M., Hérault, L., Gabriel, A. A., Teleman, M., & Gfeller, D. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00045-6 · Cited by 18
“To fi t decomposition, the archetype matrix is initialized with way-points using a maximum minimum sampling approach (Setty et al, 2019), ensuring more representative initialization.”
Single-cell spatiotemporal dissection of the human maternal–fetal interface
Wang, C., Zhou, Y., Wang, Y. J., Guha, T. K., Luo, Z., Mustafaraj, A., McIntyre, T. I., Schwab, M. E., Davidson, B. R., Reeder, G. C., et al. (2026) · Nature · doi:10.1038/s41586-026-10316-x · Cited by 17
Ribosomal proteins regulate 2-cell-stage transcriptome in mouse embryonic stem cells
Yi, Y., Zeng, Y., Sam, T. W., Hamashima, K., Tan, R. J. R., Warrier, T., Phua, J. X., Taneja, R., Liou, Y., Li, H., et al. (2023) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.12.007 · Cited by 15
“Trajectories and pseudotimes were computed using Palantir (Setty et al., 2019).”
MethodEarly-stage lung cancer is driven by a transitional cell state dependent on a KRAS-ITGA3-SRC axis
Moye, A. L., Dost, A. F. M., Ietswaart, R., Sengupta, S., Ya, V., Aluya, C., Fahey, C. G., Louie, S. M., Paschini, M., & Kim, C. F. (2024) · The EMBO Journal · doi:10.1038/s44318-024-00113-5 · Cited by 18
“We computationally determined the transition probability of C5 and C7 using Palantir, which captures the continuity in cell states modeled as a stochastic process, then assigns a probability of differentiating into pre-de fi ned terminal states (Setty et al, 2019).”
Mitochondria define intestinal stem cell differentiation downstream of a FOXO/Notch axisPreprint
Ludikhuize, M. C., Meerlo, M., Pagès-Gallego, M., Julià, M. B., Nguyen, N. B., Brombacher, E. C., Paik, J., Burgering, B. M. T., & Colman, M. J. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/777391 · Cited by 17
Unified fate mapping in multiview single-cell dataPreprint
Weiler, P., Lange, M., Klein, M., Pe’er, D., & Theis, F. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.19.549685 · Cited by 19
Self-citationJuzėnas, S., Goda, K., Kiseliovas, V., Žvirblytė, J., Quintanal-Villalonga, À., Siurkus, J., Nainys, J., & Mažutis, L. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1312 · Cited by 18
Self-citationMultiomic single-cell profiling identifies critical regulators of postnatal brain
Clarence, T., Bendl, J. M., Cao, X. D., Wang, X., Zheng, S., Hoffman, G. E., Kozlenkov, A., Hong, A., Iskhakova, M., Jaiswal, M. K., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02083-8 · Cited by 20
“Palantir (v.1.3.3) 84 was applied to the OPC/OL population using 1,200 waypoints (random waypoints selected) with selection of one OPC cell (with the lowest age 0 and the highest expression of PDGFRA ) as the starting point.”
InfluentialXu, X., Passalacqua, M. J., Rice, B. R., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.07.024 · Cited by 16
Shuai, P., Hsieh, J. A., Kao, C., Hu, C.-W., Wang, R., Kuo, S., Yen, M., Liou, P., Ho, Y.-C., Chu, C.-C., et al. (2025) · The Plant Cell · doi:10.1093/plcell/koaf253 · Cited by 18
Chen, B., Tang, H., Zheng, X., Xie, F., Yu, P., Lyu, Y., Feng, T., Wu, J., Liu, J., Xu, Y., et al. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02490-9 · Cited by 20
An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data
Hong, Y., Li, H., Long, C., Liang, P., Zhou, J., & Zuo, Y. (2024) · Fundamental Research · doi:10.1016/j.fmre.2024.01.020 · Cited by 17
“Setty et al. and Stassen et al. presented Palantir and VIA respectively, both of whom applied Markov chain to single cell pseudotime analysis [15 , 16] .”
MethodStaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases
Stassen, S. V., Kobashi, M., Lam, E. Y. M., Huang, Y., Ho, J. W. K., & Tsia, K. K. (2024) · Genome biology · doi:10.1186/s13059-024-03347-y · Cited by 17
“The use of first-order memoryless random walks employed by most TI methods (e.g., Palantir [10], MARGARET [11], CellRank [12], Via 1.0 [13]) makes them particularly susceptible to these misleading pathways.”
BackgroundMethodInfluentialWang, Q., Zhao, Y., Tan, G., & Ai, J. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00716-5 · Cited by 16
“First, we used the Palantir method 19 to identify a speci fi c starting cell based on the lowest expression level of SFRP2 (blue).”
Genome-Wide Association Study of Metamizole-Induced Agranulocytosis in European Populations
Cismaru, A. L., Rudin, D., Ibáñez, L., Liakoni, E., Bonadies, N., Kreutz, R. H., Carvajal, A., Lucena, M. I., Martin, J. J., Ponce, E. S., et al. (2020) · Genes · doi:10.3390/genes11111275 · Cited by 21
Leptomeningeal anti-tumor immunity follows unique signaling principlesPreprint
Remšík, J., Tong, X., Kunes, R., Li, M., Osman, A. M., Chabot, K., Sener, U. T., Wilcox, J. A., Isakov, D., Snyder, J., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.17.533041 · Cited by 16
Self-citationFurer, N., Rappoport, N., Milman, O., Tavor, S., Lifshitz, A., Bercovich, A., Ben-Kiki, O., Danin, A., Kedmi, M., Shipony, Z., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03716-5 · Cited by 17
“We further constructed a metacell model for the two CD34-enriched BM samples collected by us using metacell2, in a similar fashion to that described previously, and downloaded the Setty et al. sequencing data 12 , processed it by running cell-ranger, and created a third BM metacell model from their data.”
InfluentialModeling binary and graded cone cell fate patterning in the mouse retina
Eldred, K. C., Avelis, C. M., Johnston, R. J., & Roberts, E. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007691 · Cited by 15
“However, clustering based on single-cell transcriptomics data alone misses subpopulations unless hidden variables are accounted for [29, 30].”
scTOP: physics-inspired order parameters for cellular identification and visualization
Yampolskaya, M., Herriges, M. J., Ikonomou, L., Kotton, D. N., & Mehta, P. (2023) · Development · doi:10.1242/dev.201873 · Cited by 15
A Temporal Transcriptional Map of Human Natural Killer Cell DifferentiationPreprint
Pfefferle, A., Netskar, H., Ask, E. H., Lorenz, S., Goodridge, J. P., Sohlberg, E., Clancy, T., & Malmberg, K. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/630657 · Cited by 15
Single-cell multi-omic velocity infers dynamic and decoupled gene regulationPreprint
Li, C., Virgilio, M., Collins, K. L., & Welch, J. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.13.472472 · Cited by 15
“For example, cell trajectory inference algorithms 1,2,3,4,5 use pairwise cell similarities to map cells onto a “pseudotime” axis corresponding to predicted developmental progress.”
MethodLiao, X., Kang, L., Peng, Y., Chai, X., Xie, P., Lin, C., Ji, H., Jiao, Y., & Liu, J. (2024) · Nature Communications · doi:10.1038/s41467-024-55146-5 · Cited by 14
spVelo: RNA velocity inference for multi-batch spatial transcriptomics data
Long, W., Liu, T., Xue, L., & Zhao, H. (2025) · Genome biology · doi:10.1186/s13059-025-03701-8 · Cited by 14
“Trajectory inference methods typically order cells along the pseudo-time axes based on similarities in their expres-35 sion patterns [4–7].”
Chen, C., Wu, J., Wang, X., Chang, L., Wang, K., Wu, K., Guo, M., Li, H., Sun, F., Jiang, X., et al. (2025) · Cell stem cell · doi:10.1016/j.stem.2025.08.011 · Cited by 15
An agentic AI framework for ingestion and standardization of single-cell RNA-seq data analysis
Nouri, N., Artzi, R., & Savova, V. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00064-0 · Cited by 22
“…including: (1) quality control; performed globally or per sample, (2) data transformation; encompassing normalization, highly variable gene selection, and scaling, (3) dimensionality reduction; utilizing UMAP 77 and t-SNE 78 , (4) graph-based clustering 79 , and (5) marker gene identi fi cation.”
Revealing lineage-related signals in single-cell gene expression using random matrix theory
Nitzan, M., & Brenner, M. P. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1913931118 · Cited by 15
Bredemeyer, A. L., Amrute, J., Koenig, A. L., Idol, R. A., He, L. N., Luff, S. A., Dege, C., Leid, J. M., Schilling, J. D., Hinson, J. T., et al. (2022) · Development · doi:10.1242/dev.200016 · Cited by 13
Nagaharu, K., Kojima, Y., Hirose, H., Minoura, K., Hinohara, K., Minami, H., Kageyama, Y., Sugimoto, Y., Masuya, M., Nii, S., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111260 · Cited by 13
Choi, S.-H., Lee, J., Kim, S., Lee, Y. W., Kim, G.-C., Hong, S.-M., An, S.-H., Noh, H., Kim, K. E., On, D., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2023.104932 · Cited by 16
“29 When analyzing CD8 + T cells, the raw count matrix was normalized and log-transformed, and HVGs were selected by using the pp. highly_variable_genes function of the scanpy (v1.”
MethodTransiently Increased Coordination in Gene Regulation During Cell Phenotypic Transitions
Wang, W., Ni, K., Poe, D., & Xing, J. (2024) · PRX Life · doi:10.1103/prxlife.2.043009 · Cited by 15
A multimodal cross-species comparison of pancreas development
Yang, K., Spitzer, H., Sterr, M., Hrovatin, K., Sean de la, O., Zhang, X., Setyono, E. S. A., Ud‐Dean, M., Walzthoeni, T., Flisikowski, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64774-4 · Cited by 16
A Point of Inflection and Reflection on Systems Chemical Biology
Johnson, E. O., & Hung, D. T. (2019) · ACS Chemical Biology · doi:10.1021/acschembio.9b00714 · Cited by 15
Uncovering cellular networks in branching morphogenesis using single-cell transcriptomics
Goodwin, K., & Nelson, C. M. (2020) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/bs.ctdb.2020.09.004 · Cited by 13
Nested Stochastic Block Models applied to the analysis of single cell data
Morelli, L., Giansanti, V., & Cittaro, D. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04489-7 · Cited by 14
Smolander, J., Junttila, S., Venäläinen, M. S., & Elo, L. L. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab831 · Cited by 16
Gadalla, S. M., Wang, Y., Wang, T., Onabajo, O. O., Banday, A. R., Obajemu, A. A., Karaesman, E., Sucheston‐Campbell, L. E., Hahn, T., Sees, J. A., et al. (2020) · The Lancet Haematology · doi:10.1016/s2352-3026(20)30294-5 · Cited by 14
Cell diversity and plasticity during atrioventricular heart valve EMTs
Lotto, J., Cullum, R., Drissler, S., Arostegui, M., Garside, V. C., Fuglerud, B. M., Clement-Ranney, M., Thakur, A., Underhill, T. M., & Hoodless, P. A. (2023) · Nature Communications · doi:10.1038/s41467-023-41279-6 · Cited by 13
“For all trajectories calculated, the Pearson ’ s correlation of Palantir results over a range of k nearest-neighbours demonstrates that pseudotime estimation, differentiation potential, and branch probabilities to termini are robust with respect to changes in the number ofnearest-neighbours,k (Supplementary Figs.”
MethodInfluentialChen, Y., Zhang, Y., Gan, J., Ni, K., Chen, M., Bahar, İ., & Xing, J. (2025) · Nature Communications · doi:10.1038/s41467-025-62784-w · Cited by 13
“To further evaluate GraphVelo, we tested the method on another dataset of human bone marrow development 31 .”
Lalman, C., Yang, Y., & Walker, J. L. (2025) · Cells · doi:10.3390/cells14171315 · Cited by 16
“Palantir models cell fate as a probabilistic process, using diffusion-based graphs and Markov chains to calculate both pseudotime and the likelihood of each cell adopting a particular fate [99].”
Faravelli, I., Rinchetti, P., Tambalo, M., Simutin, I., Mapelli, L., Mancinelli, S., Miotto, M., Rizzuti, M., D’Angelo, A., Cordiglieri, C., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67725-1 · Cited by 14
A Scd1-mediated metabolic alteration participates in liver responses to low-dose bavachin
Shen, P., Bai, Z., Zhou, L., Wang, N., Ni, Z., Sun, D., Huang, C., Hu, Y.-Y., Xiao, C., Zhou, W., et al. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.03.010 · Cited by 14
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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