Update readme

Author: mass-a

README.md

@@ -4,33 +4,33 @@
   <img height="80" src="inst/RSticker_SCGATE.png">
 </p>
 
-**scGate** is an R package that automatizes the typical manual marker-based approach to cell type annotation, to enable accurate and intuitive purification of a cell population of interest from a complex scRNA-seq dataset, **without requiring reference gene expression profiles or training data**. scGate works with any scRNA-seq technology and with other single-cell modalities.
+**scGate** is an R package that automatizes the typical manual marker-based approach to cell type annotation, to enable accurate and intuitive purification of a cell population of interest from a complex scRNA-seq dataset, **without requiring reference gene expression profiles or training data**. **scGate** works with any scRNA-seq technology and with other single-cell modalities.
 
-scGate builds upon [UCell](https://github.com/carmonalab/UCell) for robust single-cell signature scoring and [Seurat](https://github.com/satijalab/seurat/), a comprehensive and powerful framework for single-cell omics analysis.
+**scGate** builds upon [UCell](https://github.com/carmonalab/UCell) for robust single-cell signature scoring and [Seurat](https://github.com/satijalab/seurat/), a comprehensive and powerful framework for single-cell omics analysis.
 
-Briefly, scGate takes as input: *i)* a gene expression matrix stored in a Seurat object and *ii)* a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 
+Briefly, **scGate** takes as input: *i)* a gene expression matrix stored in a Seurat object and *ii)* a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 
 
-scGate evaluates the strength of signature marker expression in each cell using the rank-based method UCell, and then performs k-nearest neighbor (kNN) smoothing by calculating the mean UCell score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest.
+**scGate** evaluates the strength of signature marker expression in each cell using the rank-based method UCell, and then performs k-nearest neighbor (kNN) smoothing by calculating the mean UCell score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest.
 
 ![scGate_examples](https://github.com/carmonalab/scGate.demo/blob/master/docs/scGate_example.png?raw=true)
 
 
 ### Installation
 
-Install `scGate` from [CRAN](https://cran.r-project.org/web/packages/scGate/index.html)
+Install **scGate** from [CRAN](https://cran.r-project.org/web/packages/scGate/index.html)
 ```r
 install.packages("scGate")
 ```
 
-For older versions of `scGate`, download a tagged version from GitHub:
+For older versions of **scGate**, download a tagged version from GitHub:
 ```r
 library(remotes)
 remotes::install_github("carmonalab/scGate", ref="v1.2.0")
 ```
 
 ### Testing the package
 
-Use scGate to purify a cell population of interest using manually defined marker genes
+Use **scGate** to purify a cell population of interest using manually defined marker genes
 
 ```r
 library(scGate)
@@ -54,7 +54,7 @@ seurat_object_purified <- subset(seurat_object, subset = `is.pure` == "Pure" )
 ```
 ### Demos and tutorials
 
-Check out this [scGate demo](https://carmonalab.github.io/scGate.demo) for a reproducible analysis, construction of hierarchical gating models, tools for performance evaluation and other advanced features. More demos for running scGate on different single-cell modalities are available at [scGate.demo repository](https://github.com/carmonalab/scGate.demo).
+Check out this [scGate demo](https://carmonalab.github.io/scGate.demo) for a reproducible analysis, construction of hierarchical gating models, tools for performance evaluation and other advanced features. More demos for running **scGate** on different single-cell modalities are available at [scGate.demo repository](https://github.com/carmonalab/scGate.demo).
 
 ### Pre-defined Gating Models
 
@@ -84,18 +84,18 @@ scGate::plot_tree(scGate_models_DB$human$generic$Tcell.alphabeta)
 
 ### scGate as a multi-class classifier
 
-scGate can also be used a cell type classifier, to annotate multiple cell types in a dataset. To annotate a dataset with marker-based cell type definitions, simply provide a list of models to scGate, e.g.:
+**scGate** can also be used a cell type classifier, to annotate multiple cell types in a dataset. To annotate a dataset with marker-based cell type definitions, simply provide a list of models to **scGate**, e.g.:
 
 ```r
 models.list <- scGate_models_DB$human$generic[c("Bcell","MoMacDC","CD8T","CD4T","Erythrocyte")]
 obj <- scGate(obj, model = models.list)
 ```
 
-See examples of scGate as a classifier at: [scGate multi-class](https://carmonalab.github.io/scGate.demo/#scgate-as-a-multi-class-classifier) and [scGate on integrated objects](https://carmonalab.github.io/scGate.demo/scGate.integrated.html)
+See examples of **scGate** as a classifier at: [scGate multi-class](https://carmonalab.github.io/scGate.demo/#scgate-as-a-multi-class-classifier) and [scGate on integrated objects](https://carmonalab.github.io/scGate.demo/scGate.integrated.html)
 
 
 ### Other single-cell modalities
-scGate can be applied to modalities other than RNA-seq, such as ATAC-seq ([scATAC-seq demo](https://carmonalab.github.io/scGate.demo/scGate.ATAC-seq.html)) and antibody-derived tags (ADT) ([CITE-seq demo](https://carmonalab.github.io/scGate.demo/scGate.CITE-seq.html)).
+**scGate** can be applied to modalities other than RNA-seq, such as ATAC-seq ([scATAC-seq demo](https://carmonalab.github.io/scGate.demo/scGate.ATAC-seq.html)) and antibody-derived tags (ADT) ([CITE-seq demo](https://carmonalab.github.io/scGate.demo/scGate.CITE-seq.html)).
 
 ### References