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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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An immune infiltration signature to predict the overall survival of patients with colon cancer.

TL;DR: A novel and promising immune signature is developed, based on estimated immune landscape from tumor transcriptomes, to predict the overall survival of patients with colon cancer and was significantly associated with some immune checkpoints, inflammatory factors, epithelial–mesenchymal transformation regulators, and many known signaling pathways in cancer.
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Context-specific effects of genetic variants associated with autoimmune disease.

TL;DR: The methods being used to unravel the gene regulatory networks perturbed in autoimmune diseases are outlined and the importance of doing this in the relevant cell types are highlighted, to demonstrate how cell type and disease context can impact on the consequences of genetic risk factors.
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AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution.

TL;DR: AutoGeneS is introduced, a platform that automatically extracts discriminative genes and reveals the cellular heterogeneity of bulk RNA samples and requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types.
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AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution

TL;DR: This work introduces AutoGeneS, a tool that automatically extracts informative genes and reveals the cellular heterogeneity of bulk RNA samples that requires no prior knowledge about marker genes and selects genes by simultaneously optimizing multiple criteria: minimizing the correlation and maximizing the distance between cell types.
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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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