ColonRef: EpiSCORE References for Colon
| ColonRef | R Documentation |
EpiSCORE References for Colon
Description
The data contains a 381-marker expression and a 104-marker DNAm reference matrices for colon, which can be used to estimate cell type fractions of endothelial cells, epithelial cells, lymphocytes, myeloid cells and stromal cells in colon tissue with EpiSCORE framework.
Usage
data("ColonRef")
Format
Two reference matrices.
Colon_Expref.mA scRNAseq expression reference for Colon. Each row is a marker gene (in gene symbol) and each column is a cell type. For each marker gene, the value of the reference matrix is the median expression level for each type of cells in scRNAseq data.
Colon_Mref.mA DNAm reference for Colon. Each row is a marker gene (in entrezID) and each column is a cell type. The last column is the weight for each marker. The value of the matrix is the imputed DNAm level from expression reference.
Details
The EpiSCORE colon expression reference matrix is derived from a scRNA-Seq study (Elmentaite R et al Nature 2021). Details can be found in published paper. The expression reference matrix was constructed running EpiSCORE with marker specificity scores MSS= (4,4,3,4,4) for endothelial, epithelial, lymphocytes, myeloid and stromal cells, respectively. The expression reference matrix was validated in HCL colon and rectum data. The corresponding DNAm reference was imputed from expression reference with EpiSCORE. The weights in the last column indicates the ability for a marker to discriminate different cell types, which can be implemented in the weighted robust partial correlation (wRPC) algorithm in the EpiSCORE package to estimate cell type fractions in bulk colon DNAm data.
References
Elmentaite et al Cells of the human intestinal tract mapped across space and time. Nature 2021
Han X, Zhou Z, Fei L, Sun H, Wang R, Chen Y, et al. Construction of a human cell landscape at single-cell level. Nature. 2020
Teschendorff AE, Zhu T, Breeze CE, Beck S. Cell-type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data. Genome Biol. 2020
