PMID- 31278265 OWN - NLM STAT- MEDLINE VI - 10 IP - 1 TI - Accurate estimation of cell-type composition from gene expression data. PG - 2975 LA - eng PT - Comparative Study PT - Journal Article PL - England TA - Nat Commun JT - Nature communications JID - 101528555 IS - 2041-1723 (Electronic) LID - 10.1038/s41467-019-10802-z [doi] FAU - Tsoucas, Daphne AU - Tsoucas D AD - Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA. dtsoucas@gmail.com. AD - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. dtsoucas@gmail.com. FAU - Dong, Rui AU - Dong R AD - Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA. AD - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. FAU - Chen, Haide AU - Chen H AD - Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China. FAU - Zhu, Qian AU - Zhu Q AD - Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA. AD - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. FAU - Guo, Guoji AU - Guo G AD - Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, China. FAU - Yuan, Guo-Cheng AU - Yuan GC AUID- ORCID: http://orcid.org/0000-0002-2283-4714 AD - Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA. gcyuan@jimmy.harvard.edu. AD - Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. gcyuan@jimmy.harvard.edu. IS - 2041-1723 (Linking) SB - IM MH - Algorithms MH - Datasets as Topic MH - Feasibility Studies MH - Female MH - Gene Expression Profiling/*methods MH - High-Throughput Nucleotide Sequencing/methods MH - Humans MH - Least-Squares Analysis MH - Melanoma/genetics MH - Ovarian Neoplasms/genetics MH - Sequence Analysis, RNA/*methods MH - Single-Cell Analysis/*methods MH - Skin Neoplasms/genetics MH - Transcriptome/genetics PMC - PMC6611906 DCOM- 20191021 LR - 20210110 DP - 20190705 DEP - 20190705 AB - 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.