sample.missingness: estimate sample missingness

In MRCIEU/metaboprep: Metabolomics data preparation and processing pipeline

estimate sample missingness

Description

This function estimates sample missingness in a matrix of data and provides an option to exclude certain columns or features from the analysis, such as xenobiotics (with high missingness rates) in metabolomics data sets.

Usage

sample.missingness(wdata, excludethesefeatures = NA)

Arguments

wdata	a numeric matrix with samples in row and features in columns
excludethesefeatures	a vector of feature names (i.e. column names) to exclude from missingness estimates

Value

A data frame of missingness estimates for each sample. If a vector of feature names was also passed to the function a second column of missingness estimates will also be returned providing missingness estimates for each sample to the exclusion of those features provided.

Examples

## simulate some data
set.seed(1110)
ex_data = sapply(1:5, function(x){ rnorm(10, 40, 5) })
rownames(ex_data) = paste0("ind", 1:nrow(ex_data))
colnames(ex_data) = paste0("var", 1:ncol(ex_data))
## add some missingness to the data
ex_data[ sample(1:50, 10) ] = NA
## estimate missingness
mis_est = sample.missingness(ex_data)
mis_est_v2 = sample.missingness(ex_data, excludethesefeatures = "var5")

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