Update author attributes, readme

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: sctransform
 Type: Package
 Title: Variance Stabilizing Transformations for Single Cell UMI Data
-Version: 0.3.2.9010
-Date: 2022-01-08
+Version: 0.3.2.9011
+Date: 2022-01-10
 Authors@R: c(
-  person(given = "Christoph", family = "Hafemeister", email = "christoph.hafemeister@nyu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6365-8254")),
-  person(given = "Saket", family = "Choudhary", email = "schoudhary@nygenome.org", role = "ctb", comment = c(ORCID = "0000-0001-5202-7633")),
+  person(given = "Christoph", family = "Hafemeister", email = "christoph.hafemeister@nyu.edu", role = "aut", comment = c(ORCID = "0000-0001-6365-8254")),
+  person(given = "Saket", family = "Choudhary", email = "schoudhary@nygenome.org", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5202-7633")),
   person(given = "Rahul", family = "Satija", email = "rsatija@nygenome.org", role = "ctb", comment = c(ORCID = "0000-0001-9448-8833"))
   )
 Description: A normalization method for single-cell UMI count data using a 

R/vst.R

@@ -8,7 +8,7 @@ NULL
 #' This will remove unwanted effects from UMI data and return Pearson residuals.
 #' Uses future_lapply; you can set the number of cores it will use to n with plan(strategy = "multicore", workers = n).
 #' If n_genes is set, only a (somewhat-random) subset of genes is used for estimating the
-#' initial model parameters. For details see \href{http://dx.doi.org/10.1186/s13059-019-1874-1}{doi: 10.1186/s13059-019-1874-1}.
+#' initial model parameters. For details see \doi{10.1186/s13059-019-1874-1}.
 #'
 #' @param umi A matrix of UMI counts with genes as rows and cells as columns
 #' @param cell_attr A data frame containing the dependent variables; if omitted a data frame with umi and gene will be generated
@@ -81,7 +81,7 @@ NULL
 #' A special case is \code{method = 'offset'}. Here no regression parameters are learned, but
 #' instead an offset model is assumed. The latent variable is set to log_umi and a fixed
 #' slope of log(10) is used (offset). The intercept is given by log(gene_mean) - log(avg_cell_umi).
-#' See Lause et al. (\href{https://doi.org/10.1101/2020.12.01.405886}{bioRxiv 2020.12.01.405886}) for details.
+#' See Lause et al. \doi{10.1186/s13059-021-02451-7} for details.
 #' Theta is set
 #' to 100 by default, but can be changed using the \code{theta_given} parameter (single numeric value).
 #' If the offset method is used, the following parameters are overwritten:

README.md

@@ -5,7 +5,8 @@ The sctransform package was developed by Christoph Hafemeister in [Rahul Satija'
 
 ## Quick start
 
-Installation
+Installation:
+
 ```r
 # Install sctransform from CRAN
 install.packages("sctransform")
@@ -17,19 +18,18 @@ install.packages("sctransform")
 remotes::install_github("satijalab/sctransform", ref="develop")
 ```
 
-Running sctransform
+Running sctransform:
+
 ```r
 # Runnning sctransform on a UMI matrix
 normalized_data <- sctransform::vst(umi_count_matrix)$y
-#v2 regularization
+# v2 regularization
 normalized_data <- sctransform::vst(umi_count_matrix, vst.flavor="v2")$y
 
 # Runnning sctransform on a Seurat object
 seurat_object <- Seurat::SCTransform(seurat_object)
 #v2 regularization
 seurat_object <- Seurat::SCTransform(seurat_object, vst.flavor="v2")
-
-
 ```
 
 ## Help
@@ -39,8 +39,8 @@ For usage examples see vignettes in inst/doc or use the built-in help after inst
 
 Available vignettes:  
 
-- [Variance stabilizing transformation](https://rawgit.com/satijalab/sctransform/supp_html/supplement/variance_stabilizing_transformation.html)  
-- [Using sctransform in Seurat](https://rawgit.com/satijalab/sctransform/supp_html/supplement/seurat.html)  
+- [Variance stabilizing transformation](https://htmlpreview.github.io/?https://github.com/satijalab/sctransform/blob/supp_html/supplement/variance_stabilizing_transformation.html)  
+- [Using sctransform in Seurat](https://htmlpreview.github.io/?https://github.com/satijalab/sctransform/blob/supp_html/supplement/seurat.html)  
 
 ## Known Issues