CAMPA - Conditional Autoencoder for Multiplexed Pixel Analysis

CAMPA is a framework for quantitative analysis of subcellular multi-channel imaging data. It consists of a workflow that generates consistent subcellular landmarks (CSLs) using conditional Variational Autoencoders (cVAE). The output of the CAMPA workflow is an anndata object that contains interpretable per-cell features summarising the molecular composition and spatial arrangement of CSLs inside each cell.

Campa title figure

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Please see our preprint “Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps” (Spitzer, Berry et al. (2022)) to learn more.

Contributing

We are happy about any contributions! Before you start, check out our contributing guide.