Journal Article

Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-seq data

Wang, Chenqi, Guan, Jinting, Lin, Yifan, Li, Shuchao

2024

Overview

Background The widely adopted bulk RNA-seq measures the gene expression average of cells, masking cell type heterogeneity, which confounds downstream analyses. Therefore, identifying the cellular composition and cell type-specific gene expression profiles (GEPs) facilitates the study of the underlying mechanisms of various biological processes. Although single-cell RNA-seq focuses on cell type heterogeneity in gene expression, it requires specialized and expensive resources and currently is not practical for a large number of samples or a routine clinical setting. Recently, computational deconvolution methodologies have been developed, while many of them only estimate cell type composition or cell type-specific GEPs by requiring the other as input. The development of more accurate deconvolution methods to infer cell type abundance and cell type-specific GEPs is still essential. Results We propose a new deconvolution algorithm, DSSC, which infers cell type-specific gene expression and cell type proportions of heterogeneous samples simultaneously by leveraging gene-gene and sample-sample similarities in bulk expression and single-cell RNA-seq data. Through comparisons with the other existing methods, we demonstrate that DSSC is effective in inferring both cell type proportions and cell type-specific GEPs across simulated pseudo-bulk data (including intra-dataset and inter-dataset simulations) and experimental bulk data (including mixture data and real experimental data). DSSC shows robustness to the change of marker gene number and sample size and also has cost and time efficiencies. Conclusions DSSC provides a practical and promising alternative to the experimental techniques to characterize cellular composition and heterogeneity in the gene expression of heterogeneous samples.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Algorithms / Analysis / Animal Genetics and Genomics / Biological activity / Biomedical and Life Sciences / Bulk sampling / Cell type abundance / Cell type-specific gene expression profile / Composition / Computational Biology - methods / Datasets / Deconvolution / Gene expression / Gene Expression Profiling - methods / Genes / Genetic research / Health aspects / Heterogeneity / Humans / Life Sciences / Microarrays / Microbial Genetics and Genomics / Optimization / Plant Genetics and Genomics / Proteomics / Ribonucleic acid / RNA / RNA sequencing / RNA-Seq - methods / Sequence Analysis, RNA - methods / Similarity / Similarity matrix / Single-Cell Analysis - methods / Single-Cell Gene Expression Analysis / Single-cell RNA-seq data / Software / Transcriptome