README.md

In LeonSong1995/MLM: Mixed model-based deconvolution of cell-state abundance

MeDuSA: mixed model-based deconvolution of cell-state abundances along a one-dimensional trajectory

Overview

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.

Installation

# 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)

Extension

MeDuSA now also supports cell-state deconvolution for annotated cell states (cell types). Please check the link below: https://github.com/LeonSong1995/MeDuSAJ. MeDuSAJ is more robust for estimating cell state (cell type) abundance for rare cell types, albeit with a slightly increased computational burden. Tutorials can be found in the README of MeDuSAJ.

How to Use

See tutorial.

Contact

If you have any questions for MeDuSA, please feel free to leave messages on the github issues or contact me [e-mail].

Citation

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

LeonSong1995/MLM documentation built on March 13, 2024, 1:21 p.m.