A Python package for the identification, characterization and comparison of spatial clusters from spatial -omics data.
Documentation • Examples • Paper
Spatial clustering determines cellular niches characterized by specific admixing of these populations. It assigns cells to clusters based on both their intrinsic features (e.g., protein or mRNA expression), and the features of neighboring cells in the tissue.
CellCharter is able to automatically identify spatial clusters, and offers a suite of approaches for cluster characterization and comparison.
Please refer to the documentation. In particular, the
CellCharter uses Python < 3.11 and PyTorch <= 1.12.1. If you are planning to use a GPU, make sure to download and install the correct version of PyTorch first.
In CellCharter, only the dimensionality reduction and batch correction step is dependent on the data type. In particular, it uses:
- scVI for spatial transcriptomics data such as 10x Visium and Xenium, Nanostring CosMx, Vizgen MERSCOPE, Stereo-seq, DBiT-seq, MERFISH and seqFISH data.
- A modified version of scArches's TRVAE model for spatial proteomics data such as Akoya CODEX, Lunaphore COMET, CyCIF, IMC and MIBI-TOF data.
By installing CellCharter without specifying the type of data, as in the following code, it will install without any of the two models.
pip install cellcharterHowever, you can include in the installation the type of data (transcriptomics and/or proteomics) you are planning to analyze, and it will install the required dependencies.
pip install cellcharter[transcriptomics]If you found a bug or you want to propose a new feature, please use the issue tracker.