feature.sum.stats: feature summary statistics

In MRCIEU/metaboprep: Metabolomics data preparation and processing pipeline

feature summary statistics

Description

This function estimates feature statistics for samples in a matrix of metabolite features.

Usage

feature.sum.stats(
  wdata,
  sammis = NA,
  tree_cut_height = 0.5,
  outlier_udist = 5,
  feature_names_2_exclude = NA
)

Arguments

wdata	the metabolite data matrix. samples in row, metabolites in columns
sammis	a vector of sample missingness estimates, that is ordered to match the samples in the rows of your data matrix.
tree_cut_height	tree cut height is the height at which to cut the feature|metabolite dendrogram to identify "independent" features. tree_cut_height is 1-absolute(Spearman's Rho) for intra-cluster correlations.
outlier_udist	the interquartile range unit distance from the median to call a sample an outlier at a feature.
feature_names_2_exclude	A vector of feature|metabolite names to exclude from the tree building, independent feature identification process.

Value

a list object of length two, with (1) a data frame of summary statistics and (2) a hclust object

Examples

## define a covariance matrix
cmat = matrix(1, 4, 4 )
cmat[1,] = c(1, 0.8, 0.6, 0.2)
cmat[2,] = c(0.8, 1, 0.7, 0.5)
cmat[3,] = c(0.6, 0.7, 1, 0.6)
cmat[4,] = c(0.2, 0.5, 0.6,1)
## simulate some correlated data (multivariable random normal)
set.seed(1110)
d1 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
set.seed(1010)
d2 = MASS::mvrnorm(n = 250, mu = c(5, 45, 25, 15), Sigma = cmat )
## simulate some random data
d3 = sapply(1:20, function(x){ rnorm(250, 40, 5) })
## define the data set
ex_data = cbind(d1,d2,d3)
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add in some missingness
ex_data[sample(1:length(ex_data), 450)] = NA
## add in some technical error to two samples
m = apply(ex_data, 2, function(x){ mean(x, na.rm = TRUE) })
ex_data[c(1,10), ] = ex_data[1, ] + (m*0.00001) 
## run the function
fss = feature.sum.stats(ex_data)
## feature summary table
fss$table[1:5, ]
## plot the dendrogram
plot(fss$tree, hang = -1)

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