Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis

Junha Cha, Michael Lavi, Junhan Kim, Noam Shomron*, Insuk Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.

Original languageEnglish
Pages (from-to)2296-2304
Number of pages9
JournalComputational and Structural Biotechnology Journal
StatePublished - Jan 2023


  • Breast cancer
  • Cell-type-specific gene network
  • Imputation
  • Metastasis
  • Single cell network biology
  • Single-cell RNA sequencing


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