New computationalmethod builds detailed maps of human tissues WeillCornell naturemethods
in tremendous detail, down to which genes each cell is expressing, so they're focused on the cells instead of focusing on the tissue structure," he said.
Manually combining single cell data with maps of tissue structure is slow and tedious, though. Machine learning algorithms have shown some potential for automating the process, but they're limited by the data used to train them. To address that, Kim and his colleagues developed an unsupervised computational strategy, using a combination of single-cell gene expression profiles and cells' locations to define structural regions within a tissue.
Putting all of those data into one matrix, and the buildings' locations into another, one could then combine the two matrices and look for patterns.
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