BarDensr (BARcode DEmixing through Non-negative Spatial Regression) is designed to demix the molecular signal from a set of images obtained from spatial transcriptomics data. The input images are generated from multiple detecting processes, where nucleotides from the target barcodes are detected and the corresponding signals are imaged sequentially using multiple laser channels. In NeuroCAAS implementation, BarDensr runs the model on the spatial patches of the entire image that is uploaded by the user, making the process scalable to a large image with a large number of barcodes. The demixed output image for each barcode is a compressed, sparse image, and it facilitates the downstream blob detection process, which will be done by the users with their favourite blob detection algorithms. This implementation also returns the quality analysis results on the detected spots using singular value decomposition on the cleaned images, which will help assess the model fit to the uploaded images.
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