From a87a809032f90f1cf330e2a2009258de1dfa9b86 Mon Sep 17 00:00:00 2001 From: Leon Date: Fri, 25 Aug 2023 09:53:40 +0800 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0b0bde9..f901f3d 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ ## Overview ![Image text](https://github.com/LeonSong1995/MeDuSA/blob/master/docs/Overview2.jpg) -**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 aslo 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** 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 ```R