Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data
By Duanchen Sun, Xiangnan Guan, Amy E. Moran, Lingmei Wu, D. Qian, P. Schedin, M. Dai, A. Danilov, J. Alumkal, Andrew C. Adey and 2 more. Nature Biotechnology, 2021
375 citations recorded by the source corpus.
Bulk and single cell measurements are integrated to identify phenotype-associated subpopulations of cells. Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer’s disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.
Subfields: Cancer Research, Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/fa0495a7debf7a3d2f09732ffe477bbab7fb8852
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- Wuai Zhou
- Di Zhang
- Peng Zhang
- Lan Wang
- Shaokun Li
- Tingyu Zhang
- Boyang Wang
Citations in the corpus
- Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine (2023, methodology): “For example, the Scissor algorithm developed by Sun et al. [117] could infer the ‘phenotype-cell’ relationships by proposing a network regularized sparse regression model on the correlation matrix that integrated phenotype-associated bulk and single-cell expression data.”