Readme and Rootogram Updates

fanzhanglab · Oct 9, 2023 · 5fb0256 · 5fb0256
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Showing 4 changed files with 24 additions and 5 deletions.
diff --git a/NAMESPACE b/NAMESPACE
@@ -13,6 +13,7 @@ export(multimodel_count_regression)
 export(parallel_cca_permute)
 export(plot_cluster)
 export(plot_corr)
+export(rootogram)
 export(sct)
 export(spatial_adjacency_matrix)
 export(spatial_gradient)

diff --git a/README.Rmd b/README.Rmd
@@ -21,8 +21,7 @@ knitr::opts_chunk$set(
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 <p align = "justify">
-We introduce STew, a multi-view representation learning method for
-spatial transcriptomic data, to jointly characterize the gene expression variation and spatial information in the shared low-dimenion space in a scalable manner. STew will output distinct spatially informed cell gradients, robust clusters, and statistical goodness of model fit to reveal significant genes that reflect subtle spatial niches in complex tissues.
+We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, or STew, to jointly characterize the gene expression variation and spatial information in the shared low-dimenion space in a scalable manner. STew will output distinct spatially informed cell gradients, robust clusters, and statistical goodness of model fit to reveal significant genes that reflect subtle spatial niches in complex tissues.
 </p>
 
 

diff --git a/README.md b/README.md
@@ -1,16 +1,16 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# STew <img width="25%" align = "right" src="https://github.com/fanzhanglab/STew/blob/main/STewlogo.png">
+# STew <img width="43%" align = "right" src="https://github.com/fanzhanglab/STew/blob/main/STewlogo.png">
 
 [![R-CMD-check](https://github.com/fanzhanglab/STew/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/fanzhanglab/STew/actions/workflows/check-standard.yaml)
 ![](https://komarev.com/ghpvc/?username=fanzhanglab&style=flat-square&color=green)
 
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 <p align="justify">
-We introduce STew, a multi-view representation learning method for
-spatial transcriptomic data, to jointly characterize the gene expression
+We introduce STew, a Spatial Transcriptomic multi-viEW representation
+learning method, or STew, to jointly characterize the gene expression
 variation and spatial information in the shared low-dimenion space in a
 scalable manner. STew will output distinct spatially informed cell
 gradients, robust clusters, and statistical goodness of model fit to

diff --git a/man/rootogram.Rd b/man/rootogram.Rd