feature.selection {GSelection}

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.

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.

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.

Author(s)

Sayanti Guha Majumdar <[e-mail]>, Anil Rai, Dwijesh Chandra Mishra

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

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)