Accurate estimation of cell-type composition from gene expression data

• Published in: Nature communications 2019-07, Vol.10 (1), p.2975-9, Article 2975
• Main Authors: Tsoucas, Daphne, Dong, Rui, Chen, Haide, Zhu, Qian, Guo, Guoji, Yuan, Guo-Cheng
• Format: Article
• Language: English
• Subjects: 45; 631/114; 631/208/212/2019; Algorithms; Change detection; Composition; Datasets as Topic; Feasibility Studies; Female; Gene expression; Gene Expression Profiling - methods; Heterogeneity; High-Throughput Nucleotide Sequencing - methods; Humanities and Social Sciences; Humans; Information processing; Least-Squares Analysis; Melanoma - genetics; Methods; multidisciplinary; Ovarian Neoplasms - genetics; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Skin Neoplasms - genetics; Transcriptome - genetics
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-10802-z

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Bulk RNA-seq data harbors valuable information about gene expression levels from different cell types in tissue samples. Here, the authors develop DWLS, a computational method for estimating cell-type composition of bulk data by leveraging single-cell RNA-seq-derived cell-type signatures.