Computational deconvolution: extracting cell type-specific information from heterogeneous samples.
Shai S. Shen-Orr,Renaud Gaujoux +1 more
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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.About:
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.read more
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Guided-topic modelling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes
TL;DR: Guided Topic Model for deconvolution (GTM-decon) as discussed by the authors automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes.
Journal ArticleDOI
Geometric structure guided model and algorithms for complete deconvolution of gene expression data
Duan Chen,Shaoyu Li,Xue Wang +2 more
TL;DR: This paper develops an NMF-based mathematical model and corresponding computational algorithms to improve the solution identifiability of deconvoluting bulk RNA-seq data, and develops a geometric structures guided optimization model.
Journal ArticleDOI
Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells
Alberto Berral-Gonzalez,Enrique De La Rosa,Óscar González-Velasco,José Manuel Sánchez Santos,Javier De Las Rivas +4 more
TL;DR: In this article , five deconvolution methods (CIBERSORT, FARDEEP, DECONICA, LINSEED and ABIS) were implemented and used to analyze blood and immune cells, and also cancer cells, in complex mixture samples (using three bulk expression datasets).
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Using Mixtures of Biological Samples as Genome-Scale Process Controls
Jerod Parsons,Sarah A. Munro,P. Scott Pine,Jennifer McDaniel,Michele G. Mehaffey,Marc L. Salit +5 more
TL;DR: An experimental method suitable for use in genome-scale process control is demonstrated and an experimental method utilizing spike-in controls to determine mRNA content is laid out, allowing assessment of measurement performance.
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A single‐nucleus transcriptomics study of alcohol use disorder in the nucleus accumbens
TL;DR: This identification of the specific cell-types from which the association signals originate is key for designing proper follow-up experiments and, eventually, for developing new and targeted clinical interventions.
References
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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.
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Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1
Roel G.W. Verhaak,Katherine A. Hoadley,Elizabeth Purdom,Victoria Wang,Yuan-yuan Qi,Matthew D. Wilkerson,C. Ryan Miller,Li Ding,Todd R. Golub,Jill P. Mesirov,Gabriele Alexe,Michael S. Lawrence,Michael O'Kelly,Pablo Tamayo,Barbara A. Weir,Stacey Gabriel,Wendy Winckler,Supriya Gupta,Lakshmi Jakkula,Heidi S. Feiler,J. Graeme Hodgson,C. David James,Jann N. Sarkaria,Cameron Brennan,Ari B. Kahn,Paul T. Spellman,Richard K. Wilson,Terence P. Speed,Terence P. Speed,Joe W. Gray,Matthew Meyerson,Gad Getz,Charles M. Perou,Charles M. Perou,D. Neil Hayes +34 more
TL;DR: A robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes is described and multidimensional genomic data is integrated to establish patterns of somatic mutations and DNA copy number.
Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1
Roel G.W. Verhaak,Katherine A. Hoadley,Elizabeth Purdom,Victoria Wang,Yuan-yuan Qi,Matthew D. Wilkerson,C. Ryan Miller,Li Ding,Todd R. Golub,Jill P. Mesirov,Gabriele Alexe,Michael S. Lawrence,Michael O'Kelly,Pablo Tamayo,Barbara A. Weir,Stacey Gabriel,Wendy Winckler,Supriya Gupta,Lakshmi Jakkula,Heidi S. Feiler,J. Graeme Hodgson,C. David James,Jann N. Sarkaria,Cameron Brennan,Ari B. Kahn,Paul T. Spellman,Richard K. Wilson,Terence P. Speed,Terence P. Speed,Joe W. Gray,Matthew Meyerson,Gad Getz,Charles M. Perou,Charles M. Perou,D. Neil Hayes +34 more
TL;DR: The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM) and proposed a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes as discussed by the authors.
Journal ArticleDOI
Molecular signatures database (MSigDB) 3.0
Arthur Liberzon,Aravind Subramanian,Reid M. Pinchback,Helga Thorvaldsdottir,Pablo Tamayo,Jill P. Mesirov +5 more
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.
Journal ArticleDOI
Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1
David A. Barbie,Pablo Tamayo,Jesse S. Boehm,So Young Kim,Susan Moody,Ian F. Dunn,Anna C. Schinzel,Peter Sandy,Etienne Meylan,Claudia Scholl,Stefan Fröhling,Edmond M. Chan,Martin L. Sos,Kathrin Michel,Craig H. Mermel,Serena J. Silver,Barbara A. Weir,Jan H. Reiling,Qing Sheng,Piyush Gupta,Raymond C. Wadlow,Raymond C. Wadlow,Hanh Le,Sebastian Hoersch,Ben S. Wittner,Ben S. Wittner,Sridhar Ramaswamy,Sridhar Ramaswamy,David M. Livingston,David M. Sabatini,Matthew Meyerson,Matthew Meyerson,Roman K. Thomas,Eric S. Lander,Jill P. Mesirov,David E. Root,D. Gary Gilliland,Tyler Jacks,William C. Hahn +38 more
TL;DR: Observations indicate that TBK1 and NF-κB signalling are essential in KRAS mutant tumours, and establish a general approach for the rational identification of co-dependent pathways in cancer.