Single-cell manifold-preserving feature selection for detecting rare cell populations

Shaoheng Liang, Vakul Mohanty, Jinzhuang Dou, Qi Miao, Yuefan Huang, Muharrem Müftüoğlu, Li Ding, Weiyi Peng, Ken Chen

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


A key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations that drive development, differentiation and transformation. Molecular features, such as genes and proteins, defining rare cell populations are often unknown and are difficult to detect from unenriched single-cell data using conventional dimensionality reduction and clustering-based approaches. Here, we propose an unsupervised approach, SCMER (‘single-cell manifold-preserving feature selection’), which selects a compact set of molecular features with definitive meanings that preserve the manifold of the data. We apply SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis and drug resistance and response. We find that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states. SCMER can be used for discovering molecular features in a high-dimensional dataset, designing targeted, cost-effective assays for clinical applications and facilitating multi-modality integration.

Original languageEnglish
Pages (from-to)374-384
Number of pages11
JournalNature Computational Science
Issue number5
StatePublished - May 2021


Dive into the research topics of 'Single-cell manifold-preserving feature selection for detecting rare cell populations'. Together they form a unique fingerprint.

Cite this