Accurate estimation of cell-type composition from gene expression data.
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IF: 17.694
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Cited by: 102
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Abstract

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.

Keywords

Seurat
Gene Expression

MeSH terms

Algorithms
Datasets as Topic
Feasibility Studies
Female
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Humans
Least-Squares Analysis
Melanoma
Ovarian Neoplasms
Sequence Analysis, RNA
Single-Cell Analysis
Skin Neoplasms
Transcriptome

Authors

Tsoucas, Daphne
Dong, Rui
Chen, Haide
Zhu, Qian
Guo, Guoji
Yuan, Guo-Cheng

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