| Title: | HiBED |
|---|---|
| Description: | Hierarchical deconvolution for extensive cell type resolution in the human brain using DNA methylation. The HiBED deconvolution estimates proportions up to 7 cell types (GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells) in bulk brain tissues. |
| Authors: | Ze Zhang [cre, aut] (ORCID: <https://orcid.org/0000-0001-9854-5823>), Lucas A. Salas [aut] |
| Maintainer: | Ze Zhang <[email protected]> |
| License: | GPL-3 |
| Version: | 1.10.0 |
| Built: | 2026-10-10 11:18:14 UTC |
| Source: | https://github.com/bioc/HiBED |
The function estimates proportions up to 7 cell types in brain tissues.
HiBED_deconvolution(Beta, h = 2)HiBED_deconvolution(Beta, h = 2)
Beta |
Methylation beta in the format of matrix or data frame or Mset or SummarizedExperiment from brain samples. |
h |
Numeric variable. Specify the layer of deconvolution in the hierarchical model. Default is 2. |
A matrix with predicted cell proportions in brain tissues.
#Step 1: Load required libraries library(FlowSorted.Blood.EPIC) library(FlowSorted.DLPFC.450k) #Step 2: Load example data and preprocess Mset<-minfi::preprocessRaw(FlowSorted.DLPFC.450k) Examples_Betas<-minfi::getBeta(Mset) #Step 3: Run HiBED and show results HiBED_result<-HiBED_deconvolution(Examples_Betas, h=2) head(HiBED_result)#Step 1: Load required libraries library(FlowSorted.Blood.EPIC) library(FlowSorted.DLPFC.450k) #Step 2: Load example data and preprocess Mset<-minfi::preprocessRaw(FlowSorted.DLPFC.450k) Examples_Betas<-minfi::getBeta(Mset) #Step 3: Run HiBED and show results HiBED_result<-HiBED_deconvolution(Examples_Betas, h=2) head(HiBED_result)
This object contains 4 matrices of the the average DNA methylation values of the probes included in 4 layers of the HiBED deconvolution. These CpGs are used as the backbone for deconvolution and were selected because their methylation signature differs across the seven brain cell subtypes.
data("HiBED_Libraries")data("HiBED_Libraries")
The list contains matrices are 81 x 3, 183 x 4, 237 x 5, 120 x 4
The format is: num [1:81, 1:3] 0.04592944 0.02268472 0.88886150 ...
A list with 4 libraries in SummarizedExperiment formats
data("HiBED_Libraries") head(HiBED_Libraries)data("HiBED_Libraries") head(HiBED_Libraries)