Computational deconvolution: extracting cell type-specific information from heterogeneous samples

被引:198
作者
Shen-Orr, Shai S. [1 ,2 ,3 ]
Gaujoux, Renaud [2 ]
机构
[1] Technion Israel Inst Technol, Rappaport Inst Med Res, IL-31096 Haifa, Israel
[2] Technion Israel Inst Technol, Fac Med, Dept Immunol, IL-31096 Haifa, Israel
[3] Technion Israel Inst Technol, Fac Biol, IL-31096 Haifa, Israel
基金
美国国家卫生研究院;
关键词
NONNEGATIVE MATRIX FACTORIZATION; EXPRESSION ANALYSIS; DNA METHYLATION; MICROARRAY DATA; PURIFICATION; PATTERNS; DATABASE; REVEALS; RNA;
D O I
10.1016/j.coi.2013.09.015
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.
引用
收藏
页码:571 / 578
页数:8
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