Surprisal Component Analysis (SCA) is a dimensionality reduction technique for single-cell data which leverages information theory to identify biologically informative axes of variation in single-cell transcriptomic data, enabling recovery of rare and common cell types at high resolution.

SCA is implemented in Python, and can be downloaded from pyPI:
pip install shannonca.