Welcome to SAKURA's documentation!
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**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
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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!

.. For more details about the SAKURA framework, please check out our publication.

Contents
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.. toctree::
    :maxdepth: 2

    installation
    tutorials/tutorial_index
    api
    release