queryNeighbors {BiocNeighbors} | R Documentation

## Query neighbors

### Description

Find all neighboring data points within a certain distance of a query point with the KMKNN algorithm.

### Usage

    queryNeighbors(X, query, threshold, 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.
`threshold` | A positive numeric scalar specifying the maximum distance at which a point is considered a neighbor.
`get.index` | A logical scalar indicating whether the indices of the neighbors should be recorded.
`get.distance` | A logical scalar indicating whether distances to the 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 neighbors should be identified.
`raw.index` | A logial scalar indicating whether raw column indices to `precomputed$data` should be 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 neighbors (i.e., within a distance `threshold`) of each point in `query`. This requires both `X` and `query` to have the same number of dimensions.

By default, neighbors are identified for all data points within `query`. If `subset` is specified, 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 `queryNeighbors` 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. 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.

### Value

A list is returned containing:

- `index`, if `get.index=TRUE`. This is a list of integer vectors where each entry corresponds to a point (denoted here as i) in `query`. The vector for i contains the set of row indices of all points in `X` that lie within `threshold` of point i. Points in each vector are not ordered, and i will always be included in its own set.

- `distance`, if `get.distance=TRUE`. This is a list of numeric vectors where each entry corresponds to a point (as above) and contains the distances of the neighbors from i. Elements of each vector in `distance` match to elements of the corresponding vector in `index`.

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` to build an index ahead of time.

### Examples

    Y <- matrix(rnorm(100000), ncol=20)
    Z <- matrix(rnorm(20000), ncol=20)
    out <- queryNeighbors(Y, query=Z, threshold=1)
    head(out$index)
    head(out$distance)

[Package BiocNeighbors version 1.0.0 Index]