从组织表达谱中可靠地枚举细胞亚群。
Robust enumeration of cell subsets from tissue expression profiles.
作者信息
Newman Aaron M, Liu Chih Long, Green Michael R, Gentles Andrew J, Feng Weiguo, Xu Yue, Hoang Chuong D, Diehn Maximilian, Alizadeh Ash A
机构信息
1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.
1] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA. [2] Center for Cancer Systems Biology, Stanford University, Stanford, California, USA.
出版信息
Nat Methods. 2015 May;12(5):453-7. doi: 10.1038/nmeth.3337. Epub 2015 Mar 30.
We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu/).
我们介绍了CIBERSORT，这是一种从复杂组织的基因表达谱中表征细胞组成的方法。当应用于新鲜、冷冻和固定组织（包括实体瘤）的RNA混合物中的造血亚群计数时，CIBERSORT在噪声、未知混合物成分和密切相关的细胞类型方面优于其他方法。CIBERSORT应能对RNA混合物进行大规模分析，以寻找细胞生物标志物和治疗靶点（http://cibersort.stanford.edu/）。
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