This package was developed by Christoph Hafemeister in Rahul Satija's lab at the New York Genome Center. Core functionality of this package has been integrated into Seurat, an R package designed for QC, analysis, and exploration of single cell RNA-seq data.

`devtools::install_github(repo = 'ChristophH/sctransform')`

`normalized_data <- sctransform::vst(umi_count_matrix)$y`

(you can also install from CRAN: `install.packages('sctransform'))`

)

For usage examples see vignettes in inst/doc or use the built-in help after installation
`?sctransform::vst`

Available vignettes: Variance stabilizing transformation Using sctransform in Seurat

None so far - please use the issue tracker if you encounter a problem

For a detailed change log have a look at the file NEWS.md

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 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 compares the methods.

The latest version of `sctransform`

now supports the 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.

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.

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

An early version of this work was used in the paper Developmental diversification of cortical inhibitory interneurons, Nature 555, 2018.

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