Description Usage Arguments Details Value Author(s) See Also Examples

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"

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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, ...)
``` |

`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 |

`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 |

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.

The function returns object of type "biomarker".
Functions `print`

, `plot`

and `summary`

are available.

`biom.ident` |
List of |

`V` |
Values of |

`Vstar` |
Normalizes values of |

`biomarkers` |
Logical value, indicating if certain variable was identified as biomarker |

`diag` |
Outlier diagnostics (returned only if |

Jan Walach

Jan Walach

plot.biomarker

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# 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")
``` |

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