From: SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data
Topic model | SHARE-Topic | Symbol |
---|---|---|
Documents | Cells | c |
Words | Regions/genes reads | \(r^c/n_g^c\) |
Topics: science, sports, music,... | Biological processes (cell differentiation, chemo-taxis...) | t |
Topic-contribution to a document | Topic-contribution to a cell | \(\theta _c^t\) |
Likelihood to find a word in a topic | Likelihood of: an open region/number of reads in a topic | \(\phi _r^t/\) Poi\((\lambda _g^t)\) |