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 # scsim
-Simulate single-cell RNA-SEQ data using the [Splatter](https://github.com/Oshlack/splatter) statistical framework, which is descrubed [here](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0) but implemented in python. In addition, simulates doublets and cells with shared gene-expression programs (I.e. activity programs). This was used to benchmark methods for gene expression program inference in single-cell rna-seq data as described [here](https://elifesciences.org/articles/43803)
+Simulate single-cell RNA-SEQ data using the [Splatter](https://github.com/Oshlack/splatter) statistical framework, which is described [here](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0) but implemented in python. In addition, simulates doublet cells and cells with shared gene-expression programs. This was used to benchmark methods for gene expression program inference in single-cell rna-seq data as described [here](https://www.biorxiv.org/content/early/2018/10/07/310599)
 
 run_scsim.py has example code for running a simulation with a given set of parameters. It saves the results in the numpy compressed matrix format which can be loaded into a Pandas dataframe as follows: