Cargando…
Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET
Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing the cell-state manifold and cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-...
Autores principales: | Pandey, Kushagra, Zafar, Hamim |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9410915/ https://www.ncbi.nlm.nih.gov/pubmed/35639499 http://dx.doi.org/10.1093/nar/gkac412 |
Ejemplares similares
-
Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors
por: Yang, Xinan H, et al.
Publicado: (2022) -
SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models
por: Zafar, Hamim, et al.
Publicado: (2017) -
SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics
por: Cabello-Aguilar, Simon, et al.
Publicado: (2020) -
SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data
por: Zafar, Hamim, et al.
Publicado: (2019) -
Cell lineage and communication network inference via optimization for single-cell transcriptomics
por: Wang, Shuxiong, et al.
Publicado: (2019)