Fnn: Compute knn using the fnn algorithm

In Sconify: A toolkit for performing KNN-based statistics for flow and mass cytometry data

Description

This function is a wrapper around FNN package functionality to speed up the KNN process. It uses KD trees as default, along with k set to 100. Selection of K will vary based on the dataset. See k.selection.R.

Usage

Fnn(cell.df, input.markers, k = 100)

Arguments

cell.df	the cell data frame used as input
input.markers	markers to be used as input for knn
k	the number of nearest neighbors to identify

Value

nn: list of 2, nn.index: index of knn (columns) for each cell (rows) nn.dist: euclidean distance of each k-nearest neighbor

Examples

Fnn(wand.combined, input.markers)

Sconify documentation built on Nov. 8, 2020, 6:45 p.m.