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Curr Opin Immunol. 2013 Oct;25(5):571-8. doi: 10.1016/j.coi.2013.09.015. Epub 2013 Oct 19.

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

Author information

1
Rappaport Institute of Medical Research, Technion-Israel Institute of Technology, Haifa 31096, Israel; Department of Immunology, Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 31096, Israel; Faculty of Biology, Technion-Israel Institute of Technology, Haifa 31096, Israel. Electronic address: shenorr@technion.ac.il.

Abstract

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.

PMID:
24148234
PMCID:
PMC3874291
DOI:
10.1016/j.coi.2013.09.015
[Indexed for MEDLINE]
Free PMC Article

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