I have two sets, let's call them C and T, of unpaired points, which could for example represent two types of cells. Hence, both points are drawn from the same underlying distribution, but the points in T underwent an additional process/function.
Although biologically debatable, I want to find the most similar points in the T set for each point in the C set. It does not need to be a 1:1 mapping, 1:n is also possible, because the sample sizes might be different.
How can I find such a mapping? Do you know any algorithms (or have other ideas) to achieve this? First, I thought it makes sense to span a graph in both sets by drawing edges between points which are below a certain distance. The distance for each set has to be such that the number of vertices with n edges are approximately the same in both sets. Afterwards I could match all points with k neighbors in set C, with all points with k neighbors in set T. Although I have not implemented this yet, I am unsure how efficient this will be.