dataCervical: Contains three R-objects, including the data and the binary...

In gren: Adaptive Group-Regularized Logistic Elastic Net Regression

Description

The three objects are: mirCerv: 772 sequenced microRNAs for 56 samples; respCerv: binary response coded as healthy and pre-cursor lesions for cervical cancer; and mirCons: conservation status of the microRNAs in three levels coded as only found in humans, found in most mammals, and found in most vertebrates

Usage

data(dataCervical)

Format

The formats are:

mirCerv

matrix [1:56, 1:772]

respCerv

Factor w/ 2 levels "CIN3","Normal"

mirCons

Factor w/ 3 levels "NotCons", "Mammals", "Broadly"

Details

This data is used for illustration in the vignette of the gren package.

Source

Novianti, P.W., Snoek, B.C., Wilting, S.M., and van de Wiel, M.A. (2017). Better diagnostic signatures from RNAseq data through use of auxiliary co-data. Bioinformatics, 33, 1572–1574.

References

Münch, M.M., Peeters, C.F.W., van der Vaart, A.W., and van de Wiel, M.A. (2018). Adaptive group-regularized logistic elastic net regression. arXiv:1805.00389v1 [stat.ME].

Examples

data(dataCervical)
str(mirCerv)
str(respCerv)
str(mirCons)

gren documentation built on May 1, 2019, 7:31 p.m.