Journal
GENOME BIOLOGY
Volume 22, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13059-021-02281-7
Keywords
Cell type assignment; Single-cell RNA-seq; Marker genes; Clustering
Funding
- NIH [R01GM120733]
- NSF [1925645]
- American Cancer Society [RSG-17-206-01-TBG]
- Susan G. Komen Grant [CCR18548293]
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1925645] Funding Source: National Science Foundation
Ask authors/readers for more resources
scSorter is a new method developed for assigning cells to known cell types on single-cell RNA-sequencing data, showing much higher power compared to existing methods on simulated and real data.
On single-cell RNA-sequencing data, we consider the problem of assigning cells to known cell types, assuming that the identities of cell-type-specific marker genes are given but their exact expression levels are unavailable, that is, without using a reference dataset. Based on an observation that the expected over-expression of marker genes is often absent in a nonnegligible proportion of cells, we develop a method called scSorter. scSorter allows marker genes to express at a low level and borrows information from the expression of non-marker genes. On both simulated and real data, scSorter shows much higher power compared to existing methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available