Project: PRJEB37166
Differentiation is among the most fundamental processes in cell biology. Single cell RNA-seq studies have demonstrated that differentiation is a continuous process and in particular cell states are observed to reside on largely continuous spaces. We have developed Palantir, a graph based algorithm to model continuities in cell state transitions and cell fate choices. Modeling differentiation as a Markov chain, Palantir determines probabilities of reaching terminal states from cells in each intermediate state. The entropy of these probabilities represent the differentiation potential of the cell in the corresponding state. Applied to single cell RNA-seq dataset of CD34+ hematopoietic cells from human bone marrows, Palantir accurately identified key events leading up to cell fate commitment. Integration with ATAC-seq data from bulk sorted populations helped identify key regulators that correlate with cell fate specification and commitment.
Secondary Study Accession:
ERP120467
Study Title:
Profiling of CD34+ cells from human bone marrow to understand hematopoiesis
Center Name:
European Bioinformatics Institute;HCA
Broker Name:
USI
Project Core Project Short Name:
HumanHematopoieticProfiling
Study Type:
Transcriptome Analysis
Study Abstract:
Differentiation is among the most fundamental processes in cell biology. Single cell RNA-seq studies have demonstrated that differentiation is a continuous process and in particular cell states are observed to reside on largely continuous spaces. We have developed Palantir, a graph based algorithm to model continuities in cell state transitions and cell fate choices. Modeling differentiation as a Markov chain, Palantir determines probabilities of reaching terminal states from cells in each intermediate state. The entropy of these probabilities represent the differentiation potential of the cell in the corresponding state. Applied to single cell RNA-seq dataset of CD34+ hematopoietic cells from human bone marrows, Palantir accurately identified key events leading up to cell fate commitment. Integration with ATAC-seq data from bulk sorted populations helped identify key regulators that correlate with cell fate specification and commitment.
HCA Project UUID:
091cf39b-01bc-42e5-9437-f419a66c8a45
USI-BIOSTUDY-ID:
S-SUBS8
Supplementary Links 0:
https://github.com/dpeerlab/Palantir/
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Homo sapiens | N/ASRA files are not created for data submitted via ENA | ||||||||
Homo sapiens | N/ASRA files are not created for data submitted via ENA | ||||||||
Homo sapiens | N/ASRA files are not created for data submitted via ENA | ||||||||
Homo sapiens | N/ASRA files are not created for data submitted via ENA | ||||||||
Homo sapiens | N/ASRA files are not created for data submitted via ENA |
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