Update README.md

README.md

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 ## Overview of ISCEBERG
 
-Here is our single cell data explorer shiny,  this has been developped in order to analyze/vizualize and extract informations from single cell datasets. This application can transform raw counts into single cell dataset from different input format (H5, MTX, TXT or CSV) or allow you to give as input a preprocess RDS file that contain already a single cell dataset in format Seurat. The purpose of this application is to go much deeper into your single cell datasets without using code lines.
+Here is our single cell data explorer. This shiny application has been developped to analyze/vizualize and extract informations from single cell datasets. ISCEBERG can transform raw counts into filtered normalized counts and computes clustering, UMAP to provide a Seurat single cell dataset from different input format (H5, MTX, TXT or CSV). It allows you to give as input a preprocess RDS file that contain already a Seurat single cell dataset. The purpose of this application is to go much deeper into your single cell datasets without using code lines. Graphics and matched tables are downloadable. To ensure Reproducibility and Traceability, preprocessed and subclusterized data are provided as Seurat object (to be re-analyzed or reloaded later) with a report of all command lines used to generate them. 
 
 ## Citation
 
 ## Details 
 
-All the functionnality are listed here, for more details see Documentation section :
+All the functionnalities are listed here, for more details see Documentation section :
 
 - Pre-processing (read, create and apply a first filtering on your data)
-- filtering (only if you have chosen  Mtx, H5, Txt, or Csv, apply a filtering on your dataset and show some QC plot)
-- QC (only if you have chosen RDS, show some QC plot)
--  Cluster tree (create a cluster tree from different resolution in the object)
--  DE between cluster
--  Data mining (Plot expression of one or several genes with different vizualisation method)
--  Data mining for a combination of gene (Plot expression of a list of genes passed in parameter or with a file)
--  Extract information
+- Filtering (only if you have chosen  Mtx, H5, Txt, or Csv, apply a filtering on your dataset about number of genes expressed per cell, mitochondrial DNA percentage)
+- QC (QC plots about cells distribution among studied samples, projection of metadata on UMAP, cell cycle phase score computing)
+-  Cluster tree (create a cluster tree from different resolutions present in the object)
+-  DE between clusters
+-  Data mining (Plot expression of one or several genes with different vizualisation methods)
+-  Data mining for a combination of genes (Plot expression of a list of genes given in parameter or with a file)
+-  Extract information (ex : distribution of metadata groups across clusters)
 -  Add annotation 
 -  Subclustering (subcluster data based on annotation or cluster)