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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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Journal ArticleDOI

Robust enumeration of cell subsets from tissue expression profiles

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.
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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.
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Determining cell type abundance and expression from bulk tissues with digital cytometry.

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.
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Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression

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.

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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Journal ArticleDOI

Global prediction of tissue-specific gene expression and context-dependent gene networks in Caenorhabditis elegans.

TL;DR: This is the first study producing high-accuracy predictions of tissue-specific expression and interactions for a metazoan organism based on whole-animal data and used these predictions to address several experimentally challenging questions, including the identification of tissues-specific transcriptional motifs and the discovery of potential miRNA regulation specific to particular tissues.
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Probabilistic analysis of gene expression measurements from heterogeneous tissues

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

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.

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.
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