get_distances_from_landmarks: Calculate (and filter) disatances between cluster and...
In ParkerICI/scgraphs: Analyzing single-cell data using graphs

This function calculates the cosine similarity between the cluster and landmark nodes, and then filters out the resulting matrix to include only the top-scoring edges. The result of this function can be used as an adjacency matrix for graph construction

1	get_distances_from_landmarks(m, tab, col.names, q.thresh, min.similarity = 0.5)

`m`	A `data.frame` containing expression values for the cluster nodes
`tab`	A `data.frame` containing expression values for the landmark nodes
`col.names`	A vector of column names to be used for the computation
`q.thresh`	The quantile probability to be used for filtering edges. The algorithm will calculate a weight threshold based on this quantile of the weight distribution
`min.similarity`	The minimum similarity value to be used for thresholding edges. Irrespective of the results of the quantile computation, the actual threshold used will never be less than this value

Returns a matrix with each row corresponding to a cluster node, and each column corresponding to the similarity between the cluster node and the respective landmark node. This matrix can be directly used as an adjacency matrix for graph construction

ParkerICI/scgraphs documentation built on April 30, 2021, 1:10 p.m.