ibskm: Kernel Matrix Using Identity-by-state Kernel

In RKUM: Robust Kernel Unsupervised Methods

Kernel Matrix Using Identity-by-state Kernel

Description

For GWASs, a kernel captures the pairwise similarity across a number of SNPs in each gene. Kernel projects the genotype data from original high dimensional space to a feature space. One of the more popular kernels used for genomics similarity is the identity-by-state (IBS) kernel (non- parametric function of the genotypes)

Usage

ibskm(Z)

Arguments

Z	a data matrix

Details

For genome-wide association study, a kernel captures the pairwise similarity across a number of SNPs in each gene. Kernel projects the genotype data from original high dimensional space to a feature space. One popular kernel used for genomics similarity is the identity-by-state (IBS) kernel, The IBS kernel does not need any assumption on the type of genetic interactions.

Value

K	a Gram/ kernel matrix

Author(s)

Md Ashad Alam <[e-mail]>

References

Md. Ashad Alam, Hui-Yi Lin, HOng-Wen Deng, Vince Calhour Yu-Ping Wang (2018), A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia, Journal of Neuroscience Methods, Vol. 309, 161-174.

Md Ashad Alam, Kenji Fukumizu and Yu-Ping Wang (2018), Influence Function and Robust Variant of Kernel Canonical Correlation Analysis, Neurocomputing, Vol. 304 (2018) 12-29.

M. Romanazzi (1992), Influence in canonical correlation analysis, Psychometrika vol 57(2) (1992) 237-259.

See Also

See also as gkm, lkm

Examples

##Dummy data:
X <- matrix(rnorm(200),50)
ibskm(X)

RKUM documentation built on June 22, 2022, 9:06 a.m.