ovarianCancer: Data on Ovarian Cancer (NCI PBSII Data)

In DET: Representation of DET Curve with Confidence Intervals

Data on Ovarian Cancer (NCI PBSII Data)

Description

The database used correspond to proteomic spectra, generated by mass spectroscopy. This data dates from 6-19-02, and includes 91 controls (Normal) and 162 ovarian cancers. The raw spectral data of each sample contains the relative amplitude of the intensity at each molecular mass/charge (M/Z) identity. There are total 15154 M/Z identities. The intensity values were normalized according to the formula: NV = (V-Min)/(Max-Min) where NV is the normalized value, V the raw value, Min the minimum intensity and Max the maximum intensity. The normalization is done over all the 253 samples for all 15154 M/Z identities. After the normalization, each intensity value falls within the range of 0 to 1.

Usage

data(ovarianCancer)

Format

An object of class "data.frame".

References

E. F. Petricoin, A. M. Ardekani, B. A. Hitt, P. J. Levine, V. A. Fusaro, S. M. Steinberg, G. B. Mills, C. Simone, D. A. Fishman, E. C. Kohn, L. A. Liotta (2002). Use of proteomic patterns in serum to identify ovarian cancer. The Lancet, 359(9306), 572–577. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0140-6736(02)07746-2")}

Examples

library(DET)
data(ovarianCancer)
response = as.factor(ovarianCancer$response)
predictors = matrix(c(as.numeric(ovarianCancer[[2]]),
                      as.numeric(ovarianCancer[[3]])), ncol = 2)
colnames(predictors) = c("Protein 1689", "Protein 1737")
detCurves =
  detc(
    response,
    predictors,
    names = colnames(predictors),
    positive = "Cancer"
  )
plot(detCurves, main = "Proteomic patterns")

DET documentation built on Aug. 29, 2025, 5:17 p.m.