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xCell: digitally portraying the tissue cellular heterogeneity landscape
Dvir Aran1, Zicheng Hu2, Atul J Butte3
1Institute for Computational Health Sciences, University of California, San Francisco, California, 94158, USA. dvir.aran@ucsf.edu.
Genome Biology
|November 17, 2017
Summary
Researchers developed xCell, a new method to identify 64 immune and stromal cell types from transcriptomes. This gene signature-based approach offers a more complete cellular landscape analysis than previous methods.
Area of Science:
- Computational biology
- Genomics
- Immunology
Background:
- Tissues comprise diverse cell types, and accurately enumerating them from transcriptomic data is challenging.
- Existing methods for cell subset enumeration from transcriptomes are limited by training data and provide incomplete cellular landscape views.
Purpose of the Study:
- To present xCell, a novel gene signature-based computational method for inferring a comprehensive set of cell types from transcriptomic data.
- To improve the accuracy and completeness of cell type enumeration in complex biological tissues.
Main Methods:
- Harmonized 1822 pure human cell type transcriptomes from diverse sources.
- Employed a curve fitting approach for linear cell type comparison.
- Introduced a novel spillover compensation technique for enhanced cell type separation.
Main Results:
- Successfully inferred 64 distinct immune and stromal cell types using the xCell method.
- Demonstrated superior performance of xCell compared to existing methods through extensive in silico analyses.
- Validated xCell's accuracy through comparison with cytometry immunophenotyping data.
Conclusions:
- xCell provides a robust and accurate method for enumerating a wide range of immune and stromal cell types from transcriptomic data.
- The method offers a more comprehensive portrayal of the cellular landscape than previously available approaches.
- xCell is publicly available, facilitating broader research in cell type deconvolution and tissue analysis.
