Computational deconvolution: extracting cell type-specific information from heterogeneous samples.
Shai S. Shen-Orr,Renaud Gaujoux +1 more
Reads0
Chats0
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
Citations
More filters
Journal ArticleDOI
Utilizing population variation, vaccination, and systems biology to study human immunology
TL;DR: Recent developments in this emerging field are discussed, with emphasis on baseline correlates of vaccination responses, sources of immune-state variability, as well as relevant features of study design, data generation, and computational analysis.
Journal ArticleDOI
Mapping the Inner Workings of the Microbiome: Genomic- and Metagenomic-Based Study of Metabolism and Metabolic Interactions in the Human Microbiome
TL;DR: Two interrelated lines of work are highlighted, the first aiming to deconvolve the microbiome and to characterize the metabolic capacity of various microbiome species and the second aiming to utilize computational modeling to infer and study metabolic interactions between these species.
Journal ArticleDOI
The application of transcriptional blood signatures to enhance our understanding of the host response to infection: the example of tuberculosis
Simon Blankley,Matthew Berry,Christine M. Graham,Chloe I Bloom,Marc Lipman,Marc Lipman,Anne O'Garra,Anne O'Garra +7 more
TL;DR: Transcriptional approaches using blood signatures have enabled a better understanding of the host response to diseases, leading not only to new avenues of basic research, but also to the identification of potential biomarkers for use in diagnosis, prognosis and treatment monitoring.
Journal ArticleDOI
seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data.
Ziyi Chen,Lijun Quan,Anfei Huang,Qiang Zhao,Yao Yuan,Xuye Yuan,Qin Shen,Jingzhe Shang,Yinyin Ben,F. Xiao-Feng Qin,Aiping Wu +10 more
TL;DR: A computational model named seq-ImmuCC was developed to infer the relative proportions of 10 major immune cells in mouse tissues from RNA-Seq data and generated the comprehensive landscape of immune cell compositions in 27 normal mouse tissues, which provided a comprehensive and informative measurement for the immune microenvironment inside tumor tissues.
Journal ArticleDOI
Understanding Celiac Disease by Genomics
TL;DR: It is explained how investigating the regulatory and epigenomic landscape will help to pinpoint the cell types involved in CeD, and the driver genes and gene regulatory networks that are affected by CeD-associated SNPs.
References
More filters
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
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.