Article

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

2019-08-12

Abstract excerpt

Gene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing cell activities for a particular condition has been regarded as a primary mission against diseases. For example, cancer immunology aims to clarify the role of the immune system in the progression and development of cancer through analyzin...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 3b7af0ce-33e8-5d83-b38f-a14eaa53abab
DOI: 10.1101/732743

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selectionDOI 10.1101/732743Read

ELITE: Expression deconvoLution using lInear optimizaTion in bulk transcriptomics mixturEsSemantic proximity Read

Improving Differential Expression and Survival Analyses with Sample Specific Compartment DeconvolutionSemantic proximity Read

<i>Infino</i> : a Bayesian hierarchical model improves estimates of immune infiltration into tumor microenvironmentSemantic proximity Read

AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolutionSemantic proximity Read

Latent variable modelling and variational inference for scRNA-seq differential expression analysisSemantic proximity Read

bulk2scDiff: A Pseudobulk-Conditioned Diffusion Model for Bulk-to-Single-Cell RNASeq GenerationSemantic proximity Read

Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomicsSemantic proximity Read

GLDADec: marker-gene guided LDA modelling for bulk gene expression deconvolutionSemantic proximity Read

Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cellsSemantic proximity Read

Leveraging heterogeneity across multiple data sets increases accuracy of cell-mixture deconvolution and reduces biological and technical biasesSemantic proximity Read

Adaptive Digital Tissue DeconvolutionSemantic proximity Read

BayCount: A Bayesian Decomposition Method for Inferring Tumor Heterogeneity using RNA-Seq CountsSemantic proximity Read