MAR.est: Missing At Random (MAR) proportion estimation based on...

In GMSimpute: Generalized Mass Spectrum Missing Peaks Abundance Imputation

Description

MAR.est estimates the proportion of missing peaks at random (MAR) caused by preprocessing tools with exactly two technical replicates per sample.

Usage

MAR.est(abundance, sample, log.scale = TRUE, violin.plot = FALSE)

Arguments

abundance	The full abundance matrix without missing value, with features in rows and samples in columns.
sample	A vector of characters or integers. It is the sample name for each pair of replicates.
log.scale	A scalar or vector of proportions. It is the total percentage of missing peaks throughout the full matrix.
violin.plot	Logical, whether to generate violin and box plots to visualize abundance distribution of missing and nonmissing peaks.

Value

MAR.Proportion	Estimated MAR proportion
plot	Violin and box plots generated by ggplot2

Examples

data('replicates')
# replicates contains mass specturm log abundance of 85 peptides
# with missing values for 4 pairs of technical replicates.


MAR=MAR.est(replicates,sample=rep(1:4,each=2),log.scale=FALSE,violin.plot=TRUE)
# Estimates the MAR proportion in the 4 pairs of replicates and output violin/box plots object.

print(MAR$plot)
# Print violin/box plots

GMSimpute documentation built on May 1, 2019, 10:13 p.m.