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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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scDeconv: an R package to deconvolve bulk DNA methylation data with scRNA-seq data and paired bulk RNA-DNA

¹Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.

Briefings in Bioinformatics

|April 22, 2022

Summary

This study introduces scDeconv, an R package for DNA methylation (DNAm) cell deconvolution. It leverages single-cell RNA sequencing (scRNA-seq) data to accurately infer cell compositions in bulk DNAm samples, addressing reference data limitations.

Keywords:

DNA methylation cell deconvolution cell-type-specific inter-group differential features co-training ensemble scRNA-seq

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Published on: June 16, 2017

Area of Science:

Epigenetics
Computational Biology
Bioinformatics

Background:

Bulk DNA methylation (DNAm) data often comprise mixed cell populations, necessitating accurate cell deconvolution.
A major challenge in DNAm deconvolution is the scarcity of cell-type-specific DNAm reference profiles.
Single-cell RNA sequencing (scRNA-seq) datasets offer rich cell-type transcriptomic signatures, and paired bulk RNA-DNAm data are increasingly available.

Purpose of the Study:

To develop an R package, scDeconv, that utilizes scRNA-seq data to overcome the reference deficiency in DNAm deconvolution.
To enable trans-omics deconvolution by transferring cell composition information from scRNA-seq to paired DNAm samples.
To provide a robust method for predicting relative cell-type abundances in bulk DNAm data and identifying cell-type-specific differential features.

Main Methods:

Developed the scDeconv R package for trans-omics DNAm deconvolution.
Employed paired bulk RNA-DNAm samples, assuming similar cell compositions.
Utilized scRNA-seq data to infer cell content and trained an ensemble model for DNAm deconvolution and RNA deconvolution co-training.

Main Results:

Demonstrated accurate cell deconvolution on three independent testing datasets using scDeconv.
Showcased the potential of scDeconv for deconvolving other omics data (e.g., ATAC-seq) with appropriate paired datasets.
Included functionalities for identifying cell-type-specific inter-group differential features from bulk DNAm data.

Conclusions:

scDeconv effectively addresses the reference limitation in DNA methylation deconvolution by integrating scRNA-seq data.
The package provides a powerful tool for estimating cell-type proportions in bulk DNAm samples and analyzing differential features.
scDeconv offers a versatile platform for trans-omics analyses, enhancing our understanding of cellular heterogeneity in epigenomic studies.