Update README

README.md

@@ -5,24 +5,31 @@ This package was developed by Christoph Hafemeister in [Rahul Satija's lab](http
 
 ## Quick start
 
+Installation
 ```r
 # Install sctransform from CRAN
-# install.packages("sctransform")
+install.packages("sctransform")
 
 # Or the development version from GitHub:
+# the development version currently support v2 regularization
+# v2 regularization will be available on CRAN soon
 # install.packages("remotes")
 remotes::install_github("satijalab/sctransform", ref="develop")
-
-normalized_data <- sctransform::vst(umi_count_matrix)$y
 ```
 
-To invoke the `v2` flavor:
-
+Running sctransform
 ```r
+# Runnning sctransform on a UMI matrix
+normalized_data <- sctransform::vst(umi_count_matrix)$y
+#v2 regularization
 normalized_data <- sctransform::vst(umi_count_matrix, vst.flavor="v2")$y
 
-# Using Seurat
+# Runnning sctransform on a Seurat object
+seurat_object <- Seurat::SCTransform(seurat_object)
+#v2 regularization
 seurat_object <- Seurat::SCTransform(seurat_object, vst.flavor="v2")
+
+
 ```
 
 ## Help
@@ -41,25 +48,6 @@ Available vignettes:
 
 Please use [the issue tracker](https://github.com/satijalab/sctransform/issues) if you encounter a problem
 
-## News
-For a detailed change log have a look at the file [NEWS.md](https://github.com/satijalab/sctransform/blob/master/NEWS.md)
-
-### v0.3.2
-This release improves the coefficient initialization in quasi poisson regression that sometimes led to errors. There are also some minor bug fixes and a new non-parametric differential expression test for sparse non-negative data (`diff_mean_test`, [this vignette](https://rawgit.com/satijalab/sctransform/supp_html/supplement/np_diff_mean_test.html) gives some details).
-
-### v0.3.1
-This release fixes a performance regression when `sctransform::vst` was called via `do.call`, as is the case in the Seurat wrapper. 
-
-Additionally, model fitting is significantly faster now, because we use a fast Rcpp quasi poisson regression implementation (based on `Rfast` package). This applies to methods `poisson`, `qpoisson` and `nb_fast`.
-
-The `qpoisson` method is new and uses the dispersion parameter from the quasi poisson regression directly to estimate `theta` for the NB model. This can speed up the model fitting step considerably, while giving similar results to the other methods. [This vignette](https://rawgit.com/satijalab/sctransform/supp_html/supplement/method_comparison.html) compares the methods.
-
-### v0.3
-The latest version of `sctransform` now supports the [glmGamPoi](https://github.com/const-ae/glmGamPoi) package to speed up the model fitting step. You can see more about the different methods supported and how they compare in terms of results and speed [in this new vignette](https://rawgit.com/satijalab/sctransform/supp_html/supplement/method_comparison.html).
-
-Also note that default theta regularization is now based on overdispersion factor (`1 + m / theta` where m is the geometric mean of the observed counts) not `log10(theta)`. The old behavior is still available via `theta_regularization` parameter. You can see how this changes (or doesn't change) the results [in this new vignette](https://rawgit.com/satijalab/sctransform/supp_html/supplement/theta_regularization.html).
-
-
 ## References
 
 - Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 20, 296 (December 23, 2019).  [https://doi.org/10.1186/s13059-019-1874-1](https://doi.org/10.1186/s13059-019-1874-1). An early version of this work was used in the paper [Developmental diversification of cortical inhibitory interneurons, Nature 555, 2018](https://github.com/ChristophH/in-lineage).