Installation

A step-by-step guide

The easiest way to prepare a python environment for SSAM is using conda. Keeping python projects in isolated environments prevents dependency version conflicts or conflicts with your OS installation of python which usually depends on older versions incompatible with current scientific packages.

Create your environment:

conda create -n ssam python=3.11 numpy=2.0.2 gxx_linux-64

Remember to activate before using it:

conda activate ssam

Finally we switch to pip:

pip install ssam

(Optional) Using sctransform for normalization

As of SSAM v1.1.0, SSAM uses log normalization by default. Read this article for hints on choosing an appropriate normalization method for your data: https://www.nature.com/articles/s41592-023-01814-1

If you want to use sctransform to normalize your vectors, you can additionally install R and the R packages sctransform and feather.

First, install R:

conda install -c r r-base

Then, install the required R packages. Open R and type:

install.packages("sctransform")
install.packages("feather")

And install pyarrow package for interoperation between R and Python:

pip install pyarrow

Next we can download and prepare our data.

SSAM’s source code

In case you want to work with SSAM’s source code, it is also hosted on github.