Welcome to the official documentation for cellink—the toolkit designed to bridge the gap between single-cell data and individual-level genetic analysis.
Integrating genetic data with cellular heterogeneity is crucial for advancing personalized medicine. cellink provides the missing framework for efficiently handling and analyzing genetic variation alongside complex single-cell omics data at scale.
cellink introduces the DonorData class, unifying individual-level and single-cell data. It extends standard formats (AnnData, MuData) with GenoAnnData for efficient genotype (via dask) and phenotype (via ehrapy) handling.
```{image} _static/img/schematic_figure.png
:width: 750px
:alt: Data structure schematic
```
- Donor-level Data (G):
GenoAnnData, Stores individual level data such as genotypes. - Cell-level Data (C):
AnnData/MuData, Stores single-cell omics data such as gene expression.
Crucially, DonorData ensures that genetic data and single-cell modalities remain synchronized, preserving their donor-cell pairing even through complex filtering operations (e.g., selecting specific cell types or patient subsets).
cellink offers a streamlined suite of tools for the entire analysis workflow:
- Variant Preprocessing & Annotation: Tools for quality control, annotation (VCF export/import), and selection of genetic variants.
- Specialized Downstream Analysis: Easily perform complex genetic analyses on single-cell expression data, including:
- Interoperability: cellink enhances standard workflows through data exports compatible with common genetic analysis tools, e.g., for eQTL analysis with jaxqtl or tensorqtl and includes built-in dataloaders for deep learning.
- Check out the Tutorials section for step-by-step guides on analysis workflows.
- Explore the API Reference for detailed documentation.
Install the latest development version directly from GitHub:
pip install git+https://github.com/theislab/cellink.git@mainIf you found a bug, please use the issue tracker.
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