bardet: Simplified gene expression data from Scheetz et al. (2006)

In gglasso: Group Lasso Penalized Learning Using a Unified BMD Algorithm

Description

Gene expression data (20 genes for 120 samples) from the microarray experiments of mammalian eye tissue samples of Scheetz et al. (2006).

Usage

bardet

Format

An object of class list of length 2.

Details

This data set contains 120 samples with 100 predictors (expanded from 20 genes using 5 basis B-splines, as described in Yang, Y. and Zou, H. (2015)).

Value

A list with the following elements:

x	a [120 x 100] matrix (expanded from a [120 x 20] matrix) giving the expression levels of 20 filtered genes for the 120 samples. Each row corresponds to a subject, each 5 consecutive columns to a grouped gene.
y	a numeric vector of length 120 giving expression level of gene TRIM32, which causes Bardet-Biedl syndrome.

References

Scheetz, T., Kim, K., Swiderski, R., Philp, A., Braun, T., Knudtson, K., Dorrance, A., DiBona, G., Huang, J., Casavant, T. et al. (2006), “Regulation of gene expression in the mammalian eye and its relevance to eye disease”, Proceedings of the National Academy of Sciences 103(39), 14429-14434.

Huang, J., S. Ma, and C.-H. Zhang (2008). “Adaptive Lasso for sparse high-dimensional regression models”. Statistica Sinica 18, 1603-1618.

Yang, Y. and Zou, H. (2015), “A Fast Unified Algorithm for Computing Group-Lasso Penalized Learning Problems,” Statistics and Computing. 25(6), 1129-1141.
BugReport: https://github.com/emeryyi/gglasso

Examples

# load gglasso library
library(gglasso)

# load data set
data(bardet)

# how many samples and how many predictors ?
dim(bardet$x)

# repsonse y
bardet$y

gglasso documentation built on March 18, 2020, 9:07 a.m.