From 3f2f91408acaedbf89a8f4bbc1335ec8bdaab83e Mon Sep 17 00:00:00 2001 From: Christoph Hafemeister Date: Fri, 18 Dec 2020 16:28:43 +0100 Subject: [PATCH] Update links to html files --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 2e7cb16..76db8ed 100644 --- a/README.md +++ b/README.md @@ -14,8 +14,8 @@ For usage examples see vignettes in inst/doc or use the built-in help after inst `?sctransform::vst` Available vignettes: -[Variance stabilizing transformation](https://rawgit.com/ChristophH/sctransform/supp_html/variance_stabilizing_transformation.html) -[Using sctransform in Seurat](https://rawgit.com/ChristophH/sctransform/supp_html/seurat.html) +[Variance stabilizing transformation](https://rawgit.com/ChristophH/sctransform/supp_html/supplement/variance_stabilizing_transformation.html) +[Using sctransform in Seurat](https://rawgit.com/ChristophH/sctransform/supp_html/supplement/seurat.html) ## Known Issues @@ -25,19 +25,19 @@ None so far - please use [the issue tracker](https://github.com/ChristophH/sctra For a detailed change log have a look at the file [NEWS.md](https://github.com/ChristophH/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/ChristophH/sctransform/supp_html/np_diff_mean_test.html) gives some details). +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/ChristophH/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/ChristophH/sctransform/supp_html/method_comparison.html) compares the methods. +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/ChristophH/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/ChristophH/sctransform/supp_html/method_comparison.html). +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/ChristophH/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/ChristophH/sctransform/supp_html/theta_regularization.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/ChristophH/sctransform/supp_html/supplement/theta_regularization.html). ## Reference