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Schema is a Python library for the synthesis and integration of heterogeneous single-cell modalities. It is designed for the case where the modalities have all been assayed for the same cells simultaneously. Here are some of the analyses that you can do with Schema:

• infer cell types jointly across modalities.
• perform spatial transcriptomic analyses to identify differntially-expressed genes in cells that display a specific spatial characteristic.
• create informative t-SNE & UMAP visualizations of multimodal data by infusing information from other modalities into scRNA-seq data.

Schema offers support for the incorporation of more than two modalities and can also simultaneously handle batch effects and metadata (e.g., cell age).

Schema is based on a metric learning approach and formulates the modality-synthesis problem as a quadratic programming problem. Its Python-based implementation can efficiently process large datasets without the need of a GPU.

Read the documentation. We encourage you to report issues at our Github page ; you can also create pull reports there to contribute your enhancements. If Schema is useful for your research, please consider citing bioRxiv (2019).