This metric quantifies how much the factorization and alignment distorts the
geometry of the original datasets. The greater the agreement, the less
distortion of geometry there is. This is calculated by performing
dimensionality reduction on the original and quantile aligned (or just
factorized) datasets, and measuring similarity between the k nearest
neighbors for each cell in original and aligned datasets. The Jaccard index
is used to quantify similarity, and is the final metric averages across all
cells.
Note that for most datasets, the greater the chosen nNeighbor
, the
greater the agreement in general. Although agreement can theoretically
approach 1, in practice it is usually no higher than 0.2-0.3.