Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET.
Nucleic Acids Res
; 50(15): e86, 2022 08 26.
Article
em En
| MEDLINE
| ID: mdl-35639499
ABSTRACT
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-Lab/Margaret) for inferring single-cell trajectory and fate mapping for diverse dynamic cellular processes. MARGARET reconstructs complex trajectory topologies using a deep unsupervised metric learning and a graph-partitioning approach based on a novel connectivity measure, automatically detects terminal cell states, and generalizes the quantification of fate plasticity for complex topologies. On a diverse benchmark consisting of synthetic and real datasets, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. For human hematopoiesis, MARGARET accurately identified all major lineages and associated gene expression trends and helped identify transitional progenitors associated with key branching events. For embryoid body differentiation, MARGARET identified novel transitional populations that were validated by bulk sequencing and functionally characterized different precursor populations in the mesoderm lineage. For colon differentiation, MARGARET characterized the lineage for BEST4/OTOP2 cells and the heterogeneity in goblet cell lineage in the colon under normal and inflamed ulcerative colitis conditions. Finally, we demonstrated that MARGARET can scale to large scRNA-seq datasets consisting of â¼ millions of cells.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Software
/
Linhagem da Célula
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Análise de Célula Única
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article