Research
Reconstructing developmental and disease progression with sample-level embeddings
Cell · By Longda Jiang, Zhixin Cyrillus Tan, Isabella N. Grabski, Yuhan Hao, Nathan Nakatsuka, Sourav Sarkar, Anagha Shenoy & Rahul Satija ·
The Brief
- An scRNA-seq framework embeds each sample by the density distribution of its cells across cellular states.
- The framework is applied to infection, neurodegeneration, embryonic development, and whole-organism perturbation.
- It aims to resolve continuous variation that discrete sample labels obscure.
Why it matters
Sample-level embeddings based on cell-state distributions offer a way to analyse gradual biological change, such as disease progression, that simple categorical labels miss.
Who’s affected
- single-cell genomics researchers
- researchers studying development, infection and neurodegeneration
- #scrna-seq
- #single-cell
- #embeddings
- #developmental-biology
- #neurodegeneration
- #infection
An scRNA-seq framework that embeds each sample by the density distribution of its cells across cellular states is used to resolve continuous variation in infection, neurodegeneration, embryonic development, and whole-organism perturbation that discrete sample labels obscure.
Timeline
Article published in Cell
cell.com
Reconstructing developmental and disease progression with sample-level embeddings