robCompositions: biomarker – R documentation

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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

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

Compositional Data Analysis

v2.3.0

GPL (>= 2)

Authors

Matthias Templ [aut, cre] (<https://orcid.org/0000-0002-8638-5276>), Karel Hron [aut] (<https://orcid.org/0000-0002-1847-6598>), Peter Filzmoser [aut] (<https://orcid.org/0000-0002-8014-4682>), Kamila Facevicova [ctb], Petra Kynclova [ctb], Jan Walach [ctb], Veronika Pintar [ctb], Jiajia Chen [ctb], Dominika Miksova [ctb], Bernhard Meindl [ctb], Alessandra Menafoglio [ctb] (<https://orcid.org/0000-0003-0682-6412>), Alessia Di Blasi [ctb], Federico Pavone [ctb], Gianluca Zeni [ctb]

Initial release

2020-11-18