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

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TLDR
The present state of available deconvolution techniques, their advantages and limitations, are reviewed, with a focus on blood expression data and immunological studies in general.
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This article is published in Current Opinion in Immunology.The article was published on 2013-10-01 and is currently open access. It has received 244 citations till now. The article focuses on the topics: Deconvolution.

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Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs

TL;DR: In this paper , the relevance of changes in cell proportions in peripheral blood mononuclear cells (PBMCs) during the development of rheumatoid arthritis and following treatment was determined.
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Genetic Loss of Nicotinamide Nucleotide Transhydrogenase Prevents from Cardiometabolic Heart Failure with Preserved Ejection Fraction

TL;DR: In this article , the loss of Nicotinamide Nucleotide Transhydrogenase (NNT) protects against both cardiac and metabolic consequences of HFD+L-NAME, thus highlighting a novel etiology-specific avenue for HFpEF therapeutics.
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Identification of the key immune cells and genes for the diagnostics and therapeutics of meningioma.

TL;DR: In this article , the authors used CIBERSORT algorithm to analyze the immune cell infiltration in samples and found that ADCY1, BMX, KCNA5, SLCO4A1, and TTR are potential therapeutic targets and their associations with macrophages, neutrophils, NK cells, and plasma cells might impact the tumorigenesis of meningiomas.
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Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

TL;DR: The hierarchical model of Lonnstedt and Speed (2002) is developed into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples and the moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom.
Journal ArticleDOI

Molecular signatures database (MSigDB) 3.0

TL;DR: A new version of the database, MSigDB 3.0, is reported, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site.
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