CentralCNS: Gene expression and clinical data used to predict tumors of...

In lsplsGlm: Classification using LS-PLS for Logistic Regression

Description

Gene expression (60x7129) and clinical data (60x4) are used to predict the response of childhood malignant embryonal tumors of Central Nervous System (CNS) to therapy.

Usage

data(CentralCNS)

Details

The dataset is composed of 60 patients samples, 21 patients died and 39 survived within 24 months. There are two matrices: genetic data X of size 60x7129 and clinical data D of size 60x4. There is also a response variable Y of size 60x1.

Value

X	a data gene matrix (60x7129) giving the expression levels of 7129 genes for the 60 patients. Each row corresponds to a patient, each column to a gene.
D	a data matrix (60x4) of clinical data. Each row corresponds to a patient and each column to a clinical variable.
Y	a numeric vector of length 60 giving the condition of the patient (1 dead, 0 alive).

Source

S. L. Pomeroy, P. Tamayo, and M. Gaasenbeek. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 415:436-442, 2002.

Examples

# load dataset
data(CentralCNS)

# how many patients and how many genes ?
dim(CentralCNS$X)

# how many patients of class 0 and 1 respectively ?
sum(CentralCNS$Y==0)
sum(CentralCNS$Y==1)

Example output

[1]   60 7129
[1] 39
[1] 21

lsplsGlm documentation built on May 2, 2019, 12:36 p.m.