Welcome to SAKURA’s documentation!
SAKURA: Single-cell data Analysis with Knowledge inputs from User using Regularized Autoencoders is a knowledge-guided dimensionality reduction framework.
SAKURA focuses on the task of producing an embedding (i.e., a low-dimensional representation) of scRNA-seq or scATAC-seq data, to be guided by a large variety of knowledge inputs related to genes and genomic regions.
Analysis of single-cell data
SAKURA is designed to be composed of modules for the following types of knowledge inputs:
Marker genes
Genes about confounding factors
Orthologous genes
Invariant genes
Regulatory elements
and more to explore!