Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree.
Nucleic Acids Res
; 49(16): e91, 2021 09 20.
Article
en En
| MEDLINE
| ID: mdl-34125905
ABSTRACT
A wealth of clustering algorithms are available for single-cell RNA sequencing (scRNA-seq) data to enable the identification of functionally distinct subpopulations that each possess a different pattern of gene expression activity. Implementation of these methods requires a choice of resolution parameter to determine the number of clusters, and critical judgment from the researchers is required to determine the desired resolution. This supervised process takes significant time and effort. Moreover, it can be difficult to compare and characterize the evolution of cell clusters from results obtained at one single resolution. To overcome these challenges, we built Multi-resolution Reconciled Tree (MRtree), a highly flexible tree-construction algorithm that generates a cluster hierarchy from flat clustering results attained for a range of resolutions. Because MRtree can be coupled with most scRNA-seq clustering algorithms, it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. The constructed trees from multiple scRNA-seq datasets effectively reflect the extent of transcriptional distinctions among cell groups and align well with levels of functional specializations among cells. Importantly, application to fetal brain cells identified subtypes of cells determined mainly by maturation states, spatial location and terminal specification.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Análisis de la Célula Individual
/
RNA-Seq
Límite:
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos