=========================================================================
DR-SC: Joint dimension reduction and spatial clustering for single-cell/spatial transcriptomics data
DR.SC (Method name is DR-SC) is a package for analyzing spatially resolved transcriptomics (SRT) datasets, developed by the Jin Liu's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.
Check out our NAR paper and our Package vignette for a more complete description of the methods and analyses.
DR.SC can be used to analyze experimental dataset from different technologies with different resolutions, for instance:
Once DR-SC model is fitted, the package provides functionality for further data exploration, analysis, and visualization. Users can:
To further investigate transcriptomic properties, combining the results from DR.SC and other packages, users can:
To install the the packages "DR.SC", firstly, install the 'remotes' package. Besides, "DR.SC" depends on the 'Rcpp' and 'RcppArmadillo' package, which also requires appropriate setting of Rtools and Xcode for Windows and Mac OS/X, respectively.
# Method 1: Install it from CRAN
install.packages("DR.SC")
# Method 2: Install it from github
install.packages("remotes")
remotes::install_github("feiyoung/DR.SC")
For usage examples and guided walkthroughs, check the vignettes
directory of the repo.
For parallel compuation based on Rcpp on Linux, users require to use the following system command to set the C_stack unlimited in case of R Error: C stack usage is too close to the limit
.
ulimit -s unlimited
For an example of typical DR.SC usage, please see our Package vignette for a demonstration and overview of the functions included in DR.SC.
DR.SC version 3.4(2024-03-19) * Update the email adress of maintainer.
DR.SC version 3.3(2023-08-02)
DR.SC version 3.0
DR.SC
and DR.SC_fit
.Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.