Add utility method to convert matrices to dgCMatrix

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: sctransform
 Type: Package
 Title: Variance Stabilizing Transformations for Single Cell UMI Data
-Version: 0.3.3.9000
+Version: 0.3.3.9001
 Date: 2022-01-10
 Authors@R: c(
   person(given = "Christoph", family = "Hafemeister", email = "christoph.hafemeister@nyu.edu", role = "aut", comment = c(ORCID = "0000-0001-6365-8254")),
@@ -13,7 +13,7 @@ Description: A normalization method for single-cell UMI count data using a
   negative binomial regression model with regularized parameters. As part of the
   same regression framework, this package also provides functions for
   batch correction, and data correction. See Hafemeister and Satija (2019)
-  <doi:10.1186/s13059-019-1874-1>, and Choudhary and Satija (2021) <doi:10.1101/2021.07.07.451498>
+  <doi:10.1186/s13059-019-1874-1>, and Choudhary and Satija (2022) <doi:10.1186/s13059-021-02584-9>
   for more details.
 URL: https://github.com/satijalab/sctransform
 BugReports: https://github.com/satijalab/sctransform/issues

R/denoise.R

@@ -236,7 +236,7 @@ correct_counts <- function(x, umi, cell_attr = x$cell_attr, scale_factor = NA, v
     y.res <- mu + pearson_residual * sqrt(variance)
     y.res <- round(y.res, 0)
     y.res[y.res < 0] <- 0
-    corrected_data[[length(corrected_data) + 1]] <- as(as(as(y.res, "dMatrix"), "generalMatrix"), "CsparseMatrix")
+    corrected_data[[length(corrected_data) + 1]] <- make.sparse(mat = y.res)
     if (verbosity > 1) {
       setTxtProgressBar(pb, i)
     }

R/generate.R

@@ -40,6 +40,6 @@ generate <- function(vst_out, genes = rownames(vst_out$model_pars_fit),
     x <- MASS::rnegbin(n = length(gene.mu), mu = gene.mu, theta = theta[gene])
     return(x)
   }))
-  x.sim <- as(as(as(x.sim, "dMatrix"), "generalMatrix"), "CsparseMatrix")
+  x.sim <- make.sparse(mat = x.sim)
   return(x.sim)
 }

R/utils.R

@@ -524,3 +524,12 @@ get_nz_median2 <- function(umi){
   return (median(umi@x))
 }
 
+#' Convert a given matrix to dgCMatrix
+#'
+#' @param matrix Input matrix
+#'
+#' @return A dgCMatrix
+make.sparse <- function(mat){
+  mat <- as(object = mat, Class = "Matrix")
+  return (as(object = as(object = as(object = mat, Class = "dMatrix"), Class = "generalMatrix"), Class = "CsparseMatrix"))
+}

R/vst.R

@@ -432,7 +432,7 @@ vst <- function(umi,
     } else {
       rv$umi_corrected <- sctransform::correct(rv, do_round = TRUE, do_pos = TRUE, scale_factor = scale_factor,
                                                verbosity = verbosity)
-      rv$umi_corrected <- as(as(as(rv$umi_corrected, "dMatrix"), "generalMatrix"), "CsparseMatrix")
+      rv$umi_corrected <- make.sparse(mat = rv$umi_corrected)
     }
   }
 

README.md

@@ -1,7 +1,8 @@
 # sctransform
 ## R package for normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
 
-The sctransform package was developed by Christoph Hafemeister in [Rahul Satija's lab](https://satijalab.org/) at the New York Genome Center and described in [Hafemeister and Satija, Genome Biology 2019](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1). Recent updates are described in [(Choudhary and Satija, Genome Biology, in press)](https://doi.org/10.1101/2021.07.07.451498). Core functionality of this package has been integrated into [Seurat](https://satijalab.org/seurat/), an R package designed for QC, analysis, and exploration of single cell RNA-seq data.
+The sctransform package was developed by Christoph Hafemeister in [Rahul Satija's lab](https://satijalab.org/) at the New York Genome Center and described in [Hafemeister and Satija, Genome Biology 2019](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1). Recent updates are described in [(Choudhary and Satija, Genome Biology, 2022)](https://doi.org/10.1186/s13059-021-02584-9).
+Core functionality of this package has been integrated into [Seurat](https://satijalab.org/seurat/), an R package designed for QC, analysis, and exploration of single cell RNA-seq data.
 
 ## Quick start
 
@@ -43,15 +44,12 @@ Available vignettes:
 - [Using sctransform in Seurat](https://htmlpreview.github.io/?https://github.com/satijalab/sctransform/blob/supp_html/supplement/seurat.html)
 - [Examples of how to perform normalization, feature selection, integration, and differential expression with sctransform v2 regularization](https://satijalab.org/seurat/articles/sctransform_v2_vignette.html)
 
-## Known Issues
-
-* `node stack overflow` error when Rfast package is loaded. The Rfast package does not play nicely with the future.apply package. Try to avoid loading the Rfast package. See discussions: https://github.com/RfastOfficial/Rfast/issues/5 https://github.com/satijalab/sctransform/issues/108
 
 Please use [the issue tracker](https://github.com/satijalab/sctransform/issues) if you encounter a problem
 
 ## 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).
+- Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biology 20, 296 (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).
 
-- Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. bioRxiv (2021). [https://doi.org/10.1101/2021.07.07.451498](https://doi.org/10.1101/2021.07.07.451498)
+- Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biology 23.1 (2022). [https://doi.org/10.1186/s13059-021-02584-9](https://doi.org/10.1186/s13059-021-02584-9)