BARcode DEmixing through Non-negative Spatial Regression (BarDensr)
Fig 1
BarDensr uses non-negative regression to demix and deconvolve the observed image stack, yielding a sparse intensity image for each barcode.
The key task of spatial transcriptomics data analysis is to take a stack of images (right) and use it to infer the locations of rolonies in the tissue. To solve this problem, BarDensr posits an ‘observation model’: a description of the physical process by which rolonies in the tissue give rise to the brightnesses we observe at each voxel. In particular, BarDensr assumes there is an unobserved ‘rolony density’ for each gene at each voxel (left), and the observation model mathematically describes how this rolony density transforms into the image stack we can see (right). Once this observation model is formulated, we can use sparse regression to solve the inverse problem: starting from an image stack, the regression gives us the value of the (unobserved) rolony density.