Cell composition analysis of bulk genomics using single-cell data

Amit Frishberg, Naama Peshes-Yaloz, Ofir Cohn, Diana Rosentul, Yael Steuerman, Liran Valadarsky, Gal Yankovitz, Michal Mandelboim, Fuad A. Iraqi, Ido Amit, Lior Mayo, Eran Bacharach*, Irit Gat-Viks

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. We introduce Cell Population Mapping (CPM), a deconvolution algorithm in which reference scRNA-seq profiles are leveraged to infer the composition of cell types and states from bulk transcriptome data (‘scBio’ CRAN R-package). Analysis of individual variations in lungs of influenza-virus-infected mice reveals that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change is confirmed in subsequent experiments and is further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.

Original languageEnglish
Pages (from-to)327-332
Number of pages6
JournalNature Methods
Issue number4
StatePublished - 1 Apr 2019


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