biomarker: biomarker

In robCompositions: Compositional Data Analysis

biomarker

Description

The function for identification of biomakers and outlier diagnostics as described in paper "Robust biomarker identification in a two-class problem based on pairwise log-ratios"

Usage

biomarker(
  x,
  cut = qnorm(0.975, 0, 1),
  g1,
  g2,
  type = "tau",
  diag = TRUE,
  plot = FALSE,
  diag.plot = FALSE
)

## S3 method for class 'biomarker'
plot(x, cut = qnorm(0.975, 0, 1), type = "Vstar", ...)

## S3 method for class 'biomarker'
print(x, ...)

## S3 method for class 'biomarker'
summary(object, ...)

Arguments

x	data frame
cut	cut-off value, initialy set as 0.975 quantile of standard normal distribution
g1	vector with locations of observations of group 1
g2	vector with locations of observations of group 2
type	type of estimation of the variation matrix. Possible values are "sd", "mad" and "tau", representing Standard deviation, Median absolute deviation and Tau estimator of scale
diag	logical, indicating wheter outlier diagnostic should be computed
plot	logical, indicating wheter Vstar values should be plotted
diag.plot	logical, indicating wheter outlier diagnostic plot should be made
...	further arguments can be passed through
object	object of class biomarker

Details

Robust biomarker identification and outlier diagnostics

The method computes variation matrices separately with observations from both groups and also together with all observations. Then, V statistics is then computed and normalized. The variables, for which according V* values are bigger that the cut-off value are considered as biomarkers.

Value

The function returns object of type "biomarker". Functions print, plot and summary are available.

biom.ident	List of V, Vstar, biomarkers
V	Values of V statistics
Vstar	Normalizes values of V statistics (V^* values))
biomarkers	Logical value, indicating if certain variable was identified as biomarker
diag	Outlier diagnostics (returned only if diag=TRUE)

Author(s)

Jan Walach

See Also

plot.biomarker

Examples

# Data simulation
set.seed(4523)
n <- 40; p <- 50
r <- runif(p, min = 1, max = 10)
conc <- runif(p, min = 0, max = 1)*5+matrix(1,p,1)*5
a <- conc*r
S <- rnorm(n,0,0.3) %*% t(rep(1,p))
B <- matrix(rnorm(n*p,0,0.8),n,p)
R <- rep(1,n) %*% t(r)
M <- matrix(rnorm(n*p,0,0.021),n,p)
# Fifth observation is an outlier
M[5,] <- M[5,]*3 + sample(c(0.5,-0.5),replace=TRUE,p)
C <- rep(1,n) %*% t(conc)
C[1:20,c(2,15,28,40)] <- C[1:20,c(2,15,28,40)]+matrix(1,20,4)*1.8
X <- (1-S)*(C*R+B)*exp(M)
# Biomarker identification
b <- biomarker(X, g1 = 1:20, g2 = 21:40, type = "tau")

robCompositions documentation built on Aug. 25, 2023, 5:13 p.m.