Composite In Situ Imaging (CISI) is a method developed to increase throughput (multiplexing over genes, and larger tissue volumes) in imaging transcriptomics. In a CISI experiment, we generate a series of composite images -- corresponding to linear combinations of genes, and made by simultaneous probing for multiple targets -- which are decompressed based on the mathematics of compressed sensing.
Run a demo of the autoencoding-based decompression on composite data from our study.
- Follow the training steps to analyze snRNA-Seq data. This will generate a gene module dictionary and use simulation to select the best measurement compositions.
(Not included in this repository: one will then order and mix probes according to the selected composite designs, and generate a series of composite images, potentially in multiple tissue sections.)
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Preprocess the imaging data to generate background subtracted, smoothed, stitched images, and cell segmentation masks.
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Decompress the data using either the autoencoding-based method, or the method based on segmentation.