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
Citations
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Robust enumeration of cell subsets from tissue expression profiles
Aaron M. Newman,Chih Long Liu,Michael R. Green,Andrew J. Gentles,Weiguo Feng,Yue Xu,Chuong D. Hoang,Maximilian Diehn,Arash Ash Alizadeh +8 more
TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.
Journal ArticleDOI
xCell: digitally portraying the tissue cellular heterogeneity landscape
TL;DR: This work presents xCell, a novel gene signature-based method, and uses it to infer 64 immune and stromal cell types and shows that xCell outperforms other methods.
Journal ArticleDOI
Determining cell type abundance and expression from bulk tissues with digital cytometry.
Aaron M. Newman,Chloé B. Steen,Chloé B. Steen,Chih Long Liu,Andrew J. Gentles,Aadel A. Chaudhuri,Florian Scherer,Michael S. Khodadoust,Mohammad Shahrokh Esfahani,Bogdan A. Luca,David F. Steiner,Maximilian Diehn,Ash A. Alizadeh +12 more
TL;DR: The utility of CIBERSORTx is evaluated in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade.
Journal ArticleDOI
Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression
Etienne Becht,Nicolas A. Giraldo,Nicolas A. Giraldo,Nicolas A. Giraldo,Laetitia Lacroix,Laetitia Lacroix,Laetitia Lacroix,Bénédicte Buttard,Bénédicte Buttard,Bénédicte Buttard,Nabila Elarouci,Florent Petitprez,Janick Selves,Pierre Laurent-Puig,Catherine Sautès-Fridman,Catherine Sautès-Fridman,Catherine Sautès-Fridman,Wolf H. Fridman,Wolf H. Fridman,Wolf H. Fridman,Aurélien de Reyniès +20 more
TL;DR: The Microenvironment Cell Populations-counter method is introduced, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data and demonstrates that MCP-counter overcomes several limitations or weaknesses of previously proposed computational approaches.
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A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
Maayan Baron,Adrian Veres,Samuel L. Wolock,Aubrey L. Faust,Renaud Gaujoux,Amedeo Vetere,Jennifer Hyoje Ryu,Bridget K. Wagner,Shai S. Shen-Orr,Allon M. Klein,Douglas A. Melton,Itai Yanai +11 more
TL;DR: A droplet-based, single-cell RNA-seq method is implemented to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains and provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.
References
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TL;DR: A probabilistic model is formalized, DSection, and it is shown with simulations as well as with real microarray data that DSection attains increased modeling accuracy in terms of estimating cell-type proportions of heterogeneous tissue samples, estimating replication variance and identifying differential expression across cell types under various experimental conditions.
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CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data
Jason E. Shoemaker,Tiago J. S. Lopes,Samik Ghosh,Yukiko Matsuoka,Yoshihiro Kawaoka,Yoshihiro Kawaoka,Hiroaki Kitano +6 more
TL;DR: It is shown that CTen, when applied to microarray data developed from infected lung tissue, can correctly identify the cell signatures of key lymphocytes in a highly heterogeneous environment and compare its performance to another popular bioinformatics tool.
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qpure: A Tool to Estimate Tumor Cellularity from Genome-Wide Single-Nucleotide Polymorphism Profiles
Sarah Song,Katia Nones,David Miller,Ivon Harliwong,Karin S. Kassahn,Mark Pinese,Marina Pajic,Anthony J. Gill,Anthony J. Gill,Anthony J. Gill,Amber L. Johns,Matthew J. Anderson,Oliver Holmes,Conrad Leonard,Darrin Taylor,Scott Wood,Qinying Xu,Felicity Newell,Mark J. Cowley,Jianmin Wu,Peter Wilson,Lynn Fink,Andrew V. Biankin,Andrew V. Biankin,Nic Waddell,Sean M. Grimmond,John V. Pearson +26 more
TL;DR: A statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour is described.
PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions
TL;DR: PERT as mentioned in this paper detects and accounts for a shared, multiplicative perturbation in the reference profiles when performing expression deconvolution, which can be used to predict cell frequencies within heterogeneous human blood samples.