feature.tree.independence: identify independent features

In MRCIEU/metaboprep: Metabolomics data preparation and processing pipeline

identify independent features

Description

This function identifies independent features using Spearman's Rho, and a dendrogram tree cut step. The feature returned as 'independent' within is k-cluster is the feature with the least missingness or chosen at random in case of missingness ties.

Usage

feature.tree.independence(wdata)

Arguments

wdata

the metabolite data matrix. samples in row, metabolites in columns

Value

a data frame of 'k' cluster or group ids, and a 0/1 binary identifying if a feature was identified as and independent ('1') feature or not ('0').

Examples

cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.9, 0.9, 0.8)
cmat[2,] = c(0.9, 1, 0.7, 0.6)
cmat[3,] = c(0.9, 0.7, 1, 0.8)
cmat[4,] = c(0.8, 0.6, 0.8,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
ex_data = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## define the data set
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## run the function
df = feature.tree.independence(ex_data)

MRCIEU/metaboprep documentation built on Jan. 28, 2023, 7:29 p.m.