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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ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin.
Ronen Sadeh,Israa Sharkia,Gavriel Fialkoff,Ayelet Rahat,Jenia Gutin,Alon Chappleboim,Mor Nitzan,Ilana Fox-Fisher,Daniel Neiman,Guy Meler,Zahala Kamari,Dayana Yaish,Tamar Peretz,Ayala Hubert,Jonathan Cohen,Azzam Salah,Mark Temper,Albert Grinshpun,Myriam Maoz,Samir Abu-Gazala,Ami Ben Ya'acov,Eyal Shteyer,Rifaat Safadi,Tommy Kaplan,Ruth Shemer,David Planer,Eithan Galun,Benjamin Glaser,Aviad Zick,Yuval Dor,Nir Friedman +30 more
TL;DR: In this article, the authors applied chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) to 268 human samples.
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Deconvolving the contributions of cell-type heterogeneity on cortical gene expression
Ellis Patrick,Mariko Taga,Ayla Ergun,Bernard Ng,William Casazza,Maria Cimpean,Christina J. Yung,Julie A. Schneider,David A. Bennett,Chris Gaiteri,Philip L. De Jager,Elizabeth M. Bradshaw,Sara Mostafavi +12 more
TL;DR: It is shown that using more accurate marker sets can substantially improve statistical power in detecting cell-type specific expression quantitative trait loci (eQTLs) and including the cell- type proportion estimates as confounding factors is important for reducing false associations between Alzheimer’s disease phenotypes and gene expression.
Journal ArticleDOI
Inference of cell type content from human brain transcriptomic datasets illuminates the effects of age, manner of death, dissection, and psychiatric diagnosis
Megan Hastings Hagenauer,Anton Schulmann,Jun Li,Marquis P. Vawter,David M. Walsh,Robert C. Thompson,Cortney A. Turner,William E. Bunney,Richard M. Myers,Jack D. Barchas,Alan F. Schatzberg,Stanley J. Watson,Huda Akil +12 more
TL;DR: It is concluded that accounting for cell type can greatly improve the interpretability of transcriptomic data and controlling for predicted sample cell content while evaluating differential expression improved the detection of previously-identified psychiatric effects.
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Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.
Niyaz Yoosuf,Niyaz Yoosuf,José Fernández Navarro,Fredrik Salmén,Fredrik Salmén,Patrik L. Ståhl,Carsten O. Daub +6 more
TL;DR: It is suggested that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on, and can classify cancer areas in tissue sections not used for training with high accuracy.
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Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons.
Mariano I. Gabitto,Mariano I. Gabitto,Ari Pakman,Jay B. Bikoff,Jay B. Bikoff,L. F. Abbott,Thomas M. Jessell,Thomas M. Jessell,Liam Paninski +8 more
TL;DR: A sparse Bayesian framework is devised that is able to handle estimation uncertainty and can incorporate diverse cellular characteristics to optimize experimental design, and is validated by direct experimental measurement, establishing it as an effective platform for cell-type characterization in the nervous system and elsewhere.
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.
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.