Update README.md

Updates to tutorial

README.md

@@ -2,12 +2,15 @@
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
 # scDEED (single-cell dubious embeddings detector): a statistical method for detecting dubious non-linear embeddings
-- This package is used to determine the reliability of non-linear dimension reduction embeddings. It provides functions to detect dubious cells and trustworthy cells in tSNE and UMAP embeddings. Furthermore, by minimizing the number of dubious cells, functions in this package find the best perplexity parameter of tSNE and the best n.neighbors/min.dist parameter of UMAP.
+- This package is used to determine the reliability of non-linear dimension reduction embeddings. It provides functions to detect dubious cells and trustworthy cells in tSNE and UMAP embeddings. The position of dubious cells in the 2D-embedding space differs from their relative position in the high-dimensional space. Furthermore, by minimizing the number of dubious cells, functions in this package find the best perplexity parameter of tSNE and the best n.neighbors/min.dist parameter of UMAP.
 
-- Choose the suitable dimension for PCA (num_pc)
+## Inputs and hyperparameters
+-  The number of PCs to use (num_pc). The user may choose this based on any criteria, such as an elbow plot. 
 
 - Input count matrix should contain cells as columns and genes as rows
 
+- Dimension reduction method (use_method). Currently, the package supports tSNE and UMAP, both implemented through the Seurat package.
+
 ## Installation
 You can install scDEED from GitHub using devtools. It should install in 1 minute, but may take longer if you need to update dependencies. 
 
@@ -18,8 +21,8 @@ devtools::install_github("JSB-UCLA/scDEED")
 
 ## Example
 
-This is a basic example showing how to find the best parameter.If users use our example input data and the default parameter list(s), users can get the result in about 2mins
-We use an example input data which is generated by randomly sampled 10000 cells from Hydra data as a demo:
+This is a basic example showing how to find the best parameter. If users use our example input data and the default parameter list(s), users can get the result in about 2mins
+We use an example input data which is generated by randomly sampling 10000 cells from Hydra dataset as a demo:
 
 ``` r
 suppressPackageStartupMessages(library(scDEED))