TY - JOUR
T1 - Single-cell manifold-preserving feature selection for detecting rare cell populations
AU - Liang, Shaoheng
AU - Mohanty, Vakul
AU - Dou, Jinzhuang
AU - Miao, Qi
AU - Huang, Yuefan
AU - Müftüoğlu, Muharrem
AU - Ding, Li
AU - Peng, Weiyi
AU - Chen, Ken
N1 - Funding Information:
We thank H. Abbas, Y. Wang and L. Wang for their comments. We acknowledge the support of the High Performance Computing for Research facility at the University of Texas MD Anderson Cancer Center for providing computational resources that contributed to the research results reported in this Article. This project has been made possible in part by Human Cell Atlas Seed Network grants (nos. CZF2019-002432 and CZF2019-02425) to K.C. from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation; grants RP180248 (K.C.) and RP200520 (W.P.) from Cancer Prevention & Research Institute of Texas; grants U01CA247760 (K.C.) and U24CA211006 (L.D.) and Cancer Center Support Grant P30 CA016672 (P.P.) from the National Cancer Institute.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85120533390&partnerID=8YFLogxK
U2 - 10.1038/s43588-021-00070-7
DO - 10.1038/s43588-021-00070-7
M3 - Article
AN - SCOPUS:85120533390
SN - 2662-8457
VL - 1
SP - 374
EP - 384
JO - Nature Computational Science
JF - Nature Computational Science
IS - 5
ER -