findKeyContributors: Identification of Key Contributors

In esteinig/netview: Mutual k-Nearest-Neighbour Networks for Population Analysis

Description

Identification of key contributors using Horn's Parallel Analysis, Eigenvalue Decomposition and calculation of the Genetic Contribution Score (GCS) as described in Neuditschko et al. (2016).

Usage

findKeyContributors(relMatrix, metaData, paranIterations=100, paranCentile=99, distMatrix=FALSE, verbose=TRUE)

Arguments

relMatrix	Pairwise relationship / similarity matrix between samples (N x N)
metaData	Data frame for NetView containing ID of individuals ordered as in the similarity matrix (N)
paranIterations	Iterations for computing the number of significant components using Horn's Parallel Analysis in Paran [ int, 100 ]
distMatrix	Input matrix is a distance matrix; converted to similarity matrix by subtracting from 1 [ bool, FALSE ]
verbose	Print computation status and results of Horn's Parallel Analysis [ bool, TRUE ]
paran.centile	Significance level (centile used in estimating bias) in Paran [ int, 99 ]

Value

Data frame containing ordered genetic contribution scores (GCS)

esteinig/netview documentation built on Feb. 8, 2022, 7:17 a.m.