queryKmknn {BiocNeighbors}R Documentation

Query nearest neighbors

Description

Use the KMKNN algorithm to query a dataset for nearest neighbors of points in another dataset.

Usage

queryKmknn(X, query, k, get.index=TRUE, get.distance=TRUE, BPPARAM=SerialParam(),
    precomputed=NULL, transposed=FALSE, subset=NULL, raw.index=FALSE, ...)

Arguments

X

A numeric matrix where rows correspond to data points and columns correspond to variables (i.e., dimensions).

query

A numeric matrix of query points, containing different data points in the rows but the same number and ordering of dimensions in the columns.

k

A positive integer scalar specifying the number of nearest neighbors to retrieve.

get.index

A logical scalar indicating whether the indices of the nearest neighbors should be recorded.

get.distance

A logical scalar indicating whether distances to the nearest neighbors should be recorded.

BPPARAM

A BiocParallelParam object indicating how the search should be parallelized.

precomputed

A KmknnIndex object from running buildKmknn on X.

transposed

A logical scalar indicating whether the query is transposed, in which case query is assumed to contain dimensions in the rows and data points in the columns.

subset

A vector indicating the rows of query (or columns, if transposed=TRUE) for which the nearest neighbors should be identified.

raw.index

A logical scalar indicating whether column indices to the reordered data in precomputed should be directly returned.

...

Further arguments to pass to buildKmknn if precomputed=NULL.

Details

This function uses the same algorithm described in findKmknn to identify points in X that are nearest neighbors of each point in query. This requires both X and query to have the same number of dimensions. Moreover, the upper bound for k is set at the number of points in X.

By default, nearest neighbors are identified for all data points within query. If subset is specified, nearest neighbors are only detected for the query points in the subset. This yields the same result as (but is more efficient than) subsetting the output matrices after running queryKmknn on the full query (i.e., with subset=NULL).

If transposed=TRUE, this function assumes that query is already transposed, which saves a bit of time by avoiding an unnecessary transposition. Turning off get.index or get.distance may also provide a slight speed boost when these returned values are not of interest. Using BPPARAM will also split the search by query points across multiple processes.

If multiple queries are to be performed to the same X, it may be beneficial to use buildKmknn directly to precompute the clustering. Note that when precomputed is supplied, the value of X is ignored. Advanced users can also set raw.index=TRUE, which returns indices of neighbors in the reordered data set in precomputed. This may be useful when dealing with multiple queries to a common precomputed object.

See comments in ?findKmknn regarding the warnings when tied distances are observed.

Value

A list is returned containing:

If subset is not NULL, each row of the above matrices refers to a point in the subset, in the same order as supplied in subset.

If raw.index=TRUE, the values in index refer to columns of KmknnIndex_clustered_data(precomputed).

Author(s)

Aaron Lun

See Also

buildKmknn, findKmknn

Examples

Y <- matrix(rnorm(100000), ncol=20)
Z <- matrix(rnorm(20000), ncol=20)
out <- queryKmknn(Y, query=Z, k=25)
head(out$index)
head(out$distance)

[Package BiocNeighbors version 1.0.0 Index]