Related Experiment Video
Updated: Jun 24, 2025
10:12
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
18.5K
Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via
Chenwei Tang1, Quan Sun1, Xinyue Zeng1
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Biorxiv : the Preprint Server for Biology
|June 3, 2024
Summary
EPIC-unmix accurately infers cell type specific (CTS) expression from bulk RNA-seq data. This computational method enhances Alzheimer's disease research by identifying novel genes and regulatory elements in specific brain cell types.
Area of Science:
- Genomics
- Computational Biology
- Neuroscience
Background:
- Cell type specific (CTS) analysis is crucial for biological insights but limited by the cost of single-cell (sc) or single-nuclei (sn) resolution data.
- Computational deconvolution methods for bulk tissue data are highly valuable for large-scale studies.
Purpose of the Study:
- To introduce EPIC-unmix, a novel computational method for accurate CTS inference from bulk RNA-seq data.
- To enhance the accuracy of CTS inference by integrating reference sc/sn RNA-seq data with bulk RNA-seq data.
Main Methods:
- EPIC-unmix employs a two-step empirical Bayesian approach.
- The method integrates reference sc/sn RNA-seq data and bulk RNA-seq data from target samples.
- Comprehensive simulations across three tissues were used for validation.
Main Results:
- EPIC-unmix demonstrated significantly higher accuracy (4.6% - 109.8%) compared to alternative methods in simulations.
- Application to human brain data revealed CTS differentially expressed genes between Alzheimer's disease cases and controls, including novel genes.
- EPIC-unmix enabled CTS eQTL analysis, identifying a novel microglia eQTL for the AD risk gene AP3B2.
Conclusions:
- EPIC-unmix is a powerful computational tool for accurate CTS analysis from bulk RNA-seq data.
- The method aids in discovering novel biological insights, particularly in complex diseases like Alzheimer's.
- EPIC-unmix facilitates CTS-specific eQTL analysis, uncovering regulatory mechanisms missed by other approaches.
Related Concept Videos
03:21RNA-seq
9.9K
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...
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.9K
02:24Ribosome Profiling
3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K