DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data

Gong, Ting; Szustakowski, Joseph D.

doi:10.1093/bioinformatics/btt090

Cited by 251 publications

(232 citation statements)

References 11 publications

(3 reference statements)

Supporting

Mentioning

229

Contrasting

Unclassified

Order By: Relevance

“…Nonetheless, single-cell sequencing approaches will be crucial to bring cell-level resolution to identifying transcriptional differences between primary and metastatic cells. Novel computational methods that deconvolute heterogeneous sample sets, until single-cell sequencing becomes more widely adopted, will also be essential (51)(52)(53). All of this withstanding, features of this data set are encouraging, such as patient-matched tumors clustering together, intuitive PAM50 assignments, corroboration of other groups' findings, and treatment-specific gains and losses.…”

Section: Discussionmentioning

confidence: 99%

Exome-capture RNA sequencing of decade-old breast cancers and matched decalcified bone metastases

Priedigkeit¹,

Watters²,

Lucas³

et al. 2017

JCI Insight

109

100

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Exome-capture RNA sequencing of decade-old breast cancers and matched decalcified bone metastases

Priedigkeit¹,

Watters²,

Lucas³

et al. 2017

JCI Insight

109

100

View full text Add to dashboard Cite

“…Second, whole blood gene expression represents a mixture of hematopoietic cells, and is greatly influenced by the cell type frequency. Multiple computational methods have been developed to deconvolute whole blood gene expression into cell frequency and cell type-specific gene expression89101112. When applied to a specific disease, blood gene expression signatures attributable to a physiological change can be decomposed into cell frequency changes and cell molecular state changes.…”

mentioning

confidence: 99%

Disease-specific classification using deconvoluted whole blood gene expression

Wang

Zhu

2016

Sci Rep

View full text Add to dashboard Cite

Blood-based biomarker assays have an advantage in being minimally invasive. Diagnostic and prognostic models built on peripheral blood gene expression have been reported for various types of disease. However, most of these studies focused on only one disease type, and failed to address whether the identified gene expression signature is disease-specific or more widely applicable across diseases. We conducted a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases and physiological conditions. Our analysis uncovered a striking overlap of signature genes shared by multiple diseases, driven by an underlying common pattern of cell component change, specifically an increase in myeloid cells and decrease in lymphocytes. These observations reveal the necessity of building disease-specific classifiers that can distinguish different disease types as well as normal controls, and highlight the importance of cell component change in deriving blood gene expression based models. We developed a new strategy to develop blood-based disease-specific models by leveraging both cell component changes and cell molecular state changes, and demonstrate its superiority using independent datasets.

show abstract

“…Deconvolution of cell proportions from whole-blood gene expression microarray data was first introduced using a heuristic algorithm based on standard linear regression 34 . Later, an R package for deconvolution of heterogeneous tissues was developed that uses quadratic programming 37 . This package, which is called DeconRNASeq, can handle RNA-seq data, but it has been validated only on mixtures with few cell types.…”

Section: Cellular Characterization Of Immune Infiltratesmentioning

confidence: 99%

Computational genomics tools for dissecting tumour–immune cell interactions

et al. 2016

View full text Add to dashboard Cite

Recent breakthroughs in cancer immunotherapy and decreasing costs of high-throughput technologies have sparked intensive research into tumour-immune cell interactions using genomic tools. The wealth of the generated data and the added complexity pose considerable challenges and require computational tools to process, to analyse and to visualize the data. Recently, various tools have been developed and used to mine tumour immunologic and genomic data effectively and to provide novel mechanistic insights. Here, we review computational genomics tools for cancer immunology and provide information on the requirements and functionality in order to assist in the selection of tools and assembly of analytical pipelines.

show abstract

DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 251 publications

References 11 publications

Exome-capture RNA sequencing of decade-old breast cancers and matched decalcified bone metastases

Exome-capture RNA sequencing of decade-old breast cancers and matched decalcified bone metastases

Disease-specific classification using deconvoluted whole blood gene expression

Computational genomics tools for dissecting tumour–immune cell interactions

Contact Info

Product

Resources

About