A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells

Due to the high cost of flow and mass cytometry, there has been a recent surge in the development of computational methods for estimating the relative distributions of cell types from the gene expression profile of a bulk of cells. Here, we review the five common 'digital cytometry' methods: deconvolution of RNA-Seq, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), CIBERSORTx, single sample gene set enrichment analysis and single-sample scoring of molecular phenotypes deconvolution method. The results show that CIBERSORTx B-mode, which uses batch correction to adjust the gene expression profile of the bulk of cells ('mixture data') to eliminate possible cross-platform variations between the mixture data and the gene expression data of single cells ('signature matrix'), outperforms other methods, especially when signature matrix and mixture data come from different platforms. However, in our tests, CIBERSORTx S-mode, which uses batch correction for adjusting the signature matrix instead of mixture data, did not perform better than the original CIBERSORT method, which does not use any batch correction method. This result suggests the need for further investigations into how to utilize batch correction in deconvolution methods.

Automated summary

Due to the high cost of flow and mass cytometry, computational methods have been developed for estimating cell type distributions from gene expression profiles. Among the methods reviewed, CIBERSORTx B-mode performs best by adjusting for batch effects in mixture data. However, CIBERSORTx S-mode did not outperform the original CIBERSORT method without batch correction, indicating the need for further research on batch correction in deconvolution methods.