xCell: digitally portraying the tissue cellular heterogeneity landscape.
xCell: digitally portraying the tissue cellular heterogeneity landscape.
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DOI：
10.1186/s13059-017-1349-1
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发表时间：
2017-11-15
期刊：
影响因子：
12.3
通讯作者：
Butte AJ
中科院分区：
文献类型：
--
作者：
Aran D;Hu Z;Butte AJ
Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/. The online version of this article (doi:10.1186/s13059-017-1349-1) contains supplementary material, which is available to authorized users.
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