MeDuSA is a fine-resolution cellular deconvolution method that leverages scRNA-seq data as a reference to estimate cell-state abundance along a one-dimensional trajectory in bulk RNA-seq data. MeDuSA features the use of a linear mixed model (LMM) to fit a cell state in question (either a single cell or the mean of multiple cells) as a fixed effect and the remaining cells of the same cell type individually as random effects accounting for correlations between cells. This model improves the deconvolution accuracy because the random-effect component allows each cell has a specific weight on bulk gene expression, resulting in a better capturing of variance in bulk gene expression. This model also ameliorates the collinearity problem between cells at the focal state (fitted as a fixed effect) and those at adjacent states (fitted as random effects) because of the shrinkage of random effects.
MeDuSA now also supports cell-state deconvolution for annotated discrete cell states (cell types). Please check the MeDuSAJ.
MeDuSAJ is more robust for estimating cell state (cell type) abundance for rare cells, albeit with a slightly increased computational burden. Tutorials can be found in the README of MeDuSAJ.
# Please install the Seurat. (https://satijalab.org/seurat/)
install.packages("Seurat")
# Please install the BiocParallel.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BiocParallel")
# Install the MeDuSA (R version > 3.5.0)
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("LeonSong1995/MeDuSA", build_vignettes=F)See tutorial.
If you have any questions for MeDuSA, please feel free to leave messages on the github issues or contact me [email protected].
Song, L., Sun, X., Qi, T. et al. Mixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectory. Nat Comput Sci (2023). https://doi.org/10.1038/s43588-023-00487-2