GSelection (version 0.1.0)

feature.selection: Genomic Feature Selection

Description

Feature (marker) selection in case of genomic prediction with integrated model framework using both additive (Sparse Additive Models) and non-additive (HSIC LASSO) statistical models.

Usage

feature.selection(x,y,d)

Arguments

x

a matrix of markers or explanatory variables, each column contains one marker and each row represents an individual.

y

a column vector of response variable.

d

number of variables to be selected from x.

Value

Returns a LIST containing

spam_selected_feature_index

returns index of selected markers from x using Sparse Additive Model

coefficient.spam

returns coefficient values of selected markers using Sparse Additive Model.

hsic_selected_feature_index

returns index of selected markers from x using HSIC LASSO.

coefficient.hsic

returns coefficient values of selected markers using HSIC LASSO.

integrated_selected_feature_index

returns index of selected markers from x using integrated model framework.

Details

Integrated model framework was developed by combining one additive model (Sparse Additive Model) and one non-additive model (HSIC LASSO) for selection of important markers from whole genome marker data.

References

Guha Majumdar, S., Rai, A. and Mishra, D. C. (2019). Integrated framework for selection of additive and non-additive genetic markers for genomic selection. Journal of Computational Biology. doi:10.1089/cmb.2019.0223 Ravikumar, P., Lafferty, J., Liu, H. and Wasserman, L. (2009). Sparse additive models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(5), 1009-1030. doi:10.1111/j.1467-9868.2009.00718.x Yamada, M., Jitkrittum, W., Sigal, L., Xing, E. P. and Sugiyama, M. (2014). High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. Neural Computation, 26(1):185-207. doi:10.1162/NECO_a_00537

Examples

# NOT RUN {
library(GSelection)
data(GS)
x_trn <- GS[1:40,1:110]
y_trn <- GS[1:40,111]
x_tst <- GS[41:60,1:110]
y_tst <- GS[41:60,111]
fit <- feature.selection(x_trn,y_trn,d=10)
# }