Supervised learning of high-confidence phenotypic subpopulations from single-cell data

Tao Ren, Canping Chen, Alexey V. Danilov, Susan Liu, Xiangnan Guan, Shunyi Du, Xiwei Wu, Mara H. Sherman, Paul T. Spellman, Lisa M. Coussens, Andrew C. Adey, Gordon B. Mills, Ling Yun Wu, Zheng Xia

Research output: Contribution to journalArticlepeer-review

Abstract

Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here by deploying a Learning with Rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to simultaneously select informative features and identify cell subpopulations, enabling accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel ability for supervised phenotypic trajectory learning of subpopulations from single-cell data. We conducted comprehensive simulations to evaluate PENCIL’s versatility in simultaneous gene selection, subpopulation identification and phenotypic trajectory prediction. PENCIL is fast and scalable to analyse one million cells within 1 h. Using the classification mode, PENCIL detected T-cell subpopulations associated with melanoma immunotherapy outcomes. Moreover, when applied to single-cell RNA sequencing of a patient with mantle cell lymphoma with drug treatment across multiple timepoints, the regression mode of PENCIL revealed a transcriptional treatment response trajectory. Collectively, our work introduces a scalable and flexible infrastructure to accurately identify phenotype-associated subpopulations from single-cell data.

Original languageEnglish (US)
Pages (from-to)528-541
Number of pages14
JournalNature Machine Intelligence
Volume5
Issue number5
DOIs
StatePublished - May 2023

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Artificial Intelligence

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