R: biomarker

biomarker {robCompositions} | R Documentation

## 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

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")

[Package robCompositions version 2.1.0 Index]