Updated Text

DESCRIPTION

@@ -4,13 +4,10 @@ Title: Single-Cell ENhancer Target (SCENT) gene mapping for single cell multimod
 Version: 0.1.0
 Author: Saori Sakaue and Shakson Isaac
 Maintainer: Shakson Isaac <[email protected]>
-Description: SCENT uses single-cell multimodal data (e.g., 10X Multiome RNA/ATAC) and links 
+Description: R package that contains functions for the SCENT algorithm. SCENT uses 
+    single-cell multimodal data (e.g., 10X Multiome RNA/ATAC) and links 
     ATAC-seq peaks (putative enhancers) to their target genes by modeling association
     between chromatin accessibility and gene expression across individual single cells.
-    We use Poisson regression to associate gene expression (raw) count and 
-    (binarized) peak accessibility, and estimate errors in coefficients by 
-    bootstrapping framework to control for type I error.
-    R package.
 Imports:
     methods, 
     Hmisc, 

README.md

@@ -183,8 +183,7 @@ Arguments in the bash file are user specified as follows:
 |2    | num_cores      | number of cores (ex. 6) to parallelize to the SCENT algorithm |
 |3    | file_SCENT_obj | SCENT object that contains atac_matrix, rna_matrix, metafile, peak_gene_list, etc. To run the SCENT algorithm |
 |4    | celltype       |User specified celltype (ex. "Tcells") to run the SCENT algorithm |
-|5    | output_dir     | User specified directory to output the SCENT results to aggregate once parallization is completed.
-|
+|5    | output_dir     | User specified directory to output the SCENT results to aggregate once completed. |
 
 
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