Update README.md

README.md

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 ## 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