Literature DB >> 24148234

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

Shai S Shen-Orr1, Renaud Gaujoux.   

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.
Copyright © 2013. Published by Elsevier Ltd.

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Year:  2013        PMID: 24148234      PMCID: PMC3874291          DOI: 10.1016/j.coi.2013.09.015

Source DB:  PubMed          Journal:  Curr Opin Immunol        ISSN: 0952-7915            Impact factor:   7.486


  39 in total

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7.  Cell subset prediction for blood genomic studies.

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9.  Mycophenolate Mofetil Treatment of Systemic Sclerosis Reduces Myeloid Cell Numbers and Attenuates the Inflammatory Gene Signature in Skin.

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10.  A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.

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Journal:  Cell Syst       Date:  2016-09-22       Impact factor: 10.304

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