nndist-methods: Nearest neighbour distances
In pRoloc: A unifying bioinformatics framework for spatial proteomics

Methods computing the nearest neighbour indices and distances for matrix and MSnSet instances.

signature(object = "matrix", k = "numeric", dist = "character", ...): Calculates indices and distances to the k (default is 3) nearest neighbours of each feature (row) in the input matrix object. The distance dist can be either of "euclidean" or "mahalanobis". Additional parameters can be passed to the internal function FNN::get.knn. Output is a matrix with 2 * k columns and nrow(object) rows.
signature(object = "MSnSet", k = "numeric", dist = "character", ...): As above, but for an MSnSet input. The indices and distances to the k nearest neighbours are added to the object's feature metadata.
signature(object = "matrix", query = "matrix", k = "numeric", ...): If two matrix instances are provided as input, the k (default is 3) indices and distances of the nearest neighbours of query in object are returned as a matrix of dimensions 2 * k by nrow(query). Additional parameters are passed to FNN::get.knnx. Only euclidean distance is available.

library("pRolocdata")
data(dunkley2006)

## Using a matrix as input
m <- exprs(dunkley2006)
m[1:4, 1:3]
head(nndist(m, k = 5))
tail(nndist(m[1:100, ], k = 2, dist = "mahalanobis"))

## Same as above for MSnSet
d <- nndist(dunkley2006, k = 5)
head(fData(d))

d <- nndist(dunkley2006[1:100, ], k = 2, dist = "mahalanobis")
tail(fData(d))

## Using a query
nndist(m[1:100, ], m[101:110, ], k = 2)