Article

Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix

2024-05-24

Abstract excerpt

Cell type specific (CTS) analysis is essential to reveal biological insights obscured in bulk tissue data. However, single-cell (sc) or single-nuclei (sn) resolution data are still cost-prohibitive for large-scale samples. Thus, computational methods to perform deconvolution from bulk tissue data are highly valuable. We here present EPIC-unmix, a novel two-step empirical Bayesian method integrating reference sc/sn...

Topics

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

Identifiers and source

Literature Corpus work: 0182f9b0-ce4e-5dbf-966f-c7bc98530b3d
DOI: 10.1101/2024.05.23.595514

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmixDOI 10.1101/2024.05.23.595514Read

Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmixSemantic proximity Read

Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell dataSemantic proximity Read

Systematic assessment of the biological impact of cellular deconvolution on downstream analyses of disease transcriptomesSemantic proximity Read

Integrative analysis of single-nucleus RNA sequencing and Mendelian randomization to explore novel risk genes for Alzheimer's disease.Semantic proximity Read

High-resolution reconstruction of cell-type specific transcriptional regulatory processes from bulk sequencing samplesSemantic proximity Read

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)Semantic proximity Read

scMD: cell type deconvolution using single-cell DNA methylation referencesSemantic proximity Read

Deciphering cellular transcriptional alterations in Alzheimer’s disease brainsSemantic proximity Read

Single-cell machine learning uncovers genetically anchored, cell-type specific programs of Alzheimer’s diseaseSemantic proximity Read

Fine-grained deconvolution of cell-type effects from human bulk brain data using a large single-nucleus RNA sequencing based reference panelSemantic proximity Read

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic dataSemantic proximity Read

A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disordersSemantic proximity Read