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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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Whole-tissue deconvolution and scRNAseq analysis identify altered endometrial cellular compositions and functionality associated with endometriosis

TL;DR: Cell type deconvolution and single cell RNAseq analysis identify altered endometrial cellular compositions in women with endometriosis, underscoring roles for nonimmune as well as immune cells to the dysfunctionality of this tissue.
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Basic Gene Expression Characteristics of Glioma Stem Cells and Human Glioblastoma

TL;DR: The factor loadings of the principal component analysis (PCA) suggested that v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN), dipeptidyl- peptidase 4 (DPP4), and macrophage migration-inhibitory factor (MIF) contribute to the stemness of GSCs.
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Racial and socioeconomic disparity associates with differences in cardiac DNA methylation among men with end-stage heart failure.

TL;DR: A bimodal signature of cardiac DNA methylation in heart failure corresponds with racial differences in all-cause mortality following mechanical circulatory support as discussed by the authors, showing that race differences in promoter m...
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Further evidence for depletion of peripheral blood natural killer cells in patients with schizophrenia: A computational deconvolution study.

TL;DR: The results indicate that patients with schizophrenia present innate immunity dysregulation in terms of NK cells depletion and increased levels of granulocytes.
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Sequential compression across latent space dimensions enhances gene expression signatures

TL;DR: Ensembles of features from different unsupervised algorithms discover biological signatures in large transcriptomic datasets to enhance biological signature discovery by compressing input data into a single pre-selected dimensionality.
References
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Journal ArticleDOI

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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