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Uncovering cell identity through differential stability with Cepo.

Hani Jieun KimKevin Y X WangCarissa ChenYingxin LinPatrick P L TamDavid M LinJean Yee Hwa YangPengyi Yang
Published in: Nature computational science (2021)
The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-specific gene statistics of differentially stable genes from scRNA-seq data to define cell identity. When applied to multiple datasets, Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells and lineage inference of single cells.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • induced apoptosis
  • genome wide
  • cell therapy
  • stem cells
  • gene expression
  • oxidative stress
  • signaling pathway
  • dna methylation
  • big data
  • transcription factor
  • deep learning