findMutualNN: Find mutual nearest neighbors
In batchelor: Single-Cell Batch Correction Methods

Description Usage Arguments Details Value Author(s) See Also Examples

Find mutual nearest neighbors (MNN) across two data sets.

findMutualNN(
  data1,
  data2,
  k1,
  k2 = k1,
  BNPARAM = KmknnParam(),
  BPPARAM = SerialParam()
)

`data1`	A numeric matrix containing samples (e.g., cells) in the rows and variables/dimensions in the columns.
`data2`	A numeric matrix like `data1` for another data set with the same variables/dimensions.
`k1`	Integer scalar specifying the number of neighbors to search for in `data1`.
`k2`	Integer scalar specifying the number of neighbors to search for in `data2`.
`BNPARAM`	A BiocNeighborParam object specifying the neighbour search algorithm to use.
`BPPARAM`	A BiocParallelParam object specifying how parallelization should be performed.

The concept of a MNN pair can be explained by considering cells in each of two data sets. For each cell in data set 1, the set of k2 nearest cells in data set 2 is identified, based on the Euclidean distance in expression space. For each cell in data set 2, the set of k1 nearest cells in data set 1 is similarly identified. Two cells in different batches are considered to be MNNs if each cell is in the other's set.

A list containing the integer vectors first and second. Corresponding entries in first and second specify a MNN pair of cells from data1 and data2, respectively.

Aaron Lun

queryKNN for the underlying neighbor search code.

fastMNN and mnnCorrect, which call this function to identify MNNs.

B1 <- matrix(rnorm(10000), ncol=50) # Batch 1 
B2 <- matrix(rnorm(10000), ncol=50) # Batch 2
out <- findMutualNN(B1, B2, k1=20)
head(out$first)
head(out$second)