calc_kernel | R Documentation |
Uses individuals' genotypes to create a kernel
object including
the calculated kernel matrix
for a specific pathway
.
Each numeric value within this matrix
is calculated
from two individuals' genotypevectors of the SNPs within
the pathway
by a kernel function. It can be interpreted as the genetic
similiarity of the individuals. Association between the pathway
and a
binary phenotype (case-control status) can be evaluated
in the logistic kernel machine test, based on the kernel
object.
Three kernel functions are available.
## S4 method for signature 'GWASdata'
calc_kernel(
object,
pathway,
knots = NULL,
type = c("lin", "sia", "net"),
calculation = c("cpu", "gpu"),
...
)
## S4 method for signature 'GWASdata'
lin_kernel(object, pathway, knots = NULL, calculation = c("cpu", "gpu"), ...)
## S4 method for signature 'GWASdata'
sia_kernel(object, pathway, knots = NULL, calculation = c("cpu", "gpu"), ...)
## S4 method for signature 'GWASdata'
net_kernel(object, pathway, knots = NULL, calculation = c("cpu", "gpu"), ...)
object |
|
pathway |
object of the class |
knots |
|
type |
|
calculation |
|
... |
further arguments to be passed to |
Different types of kernels can be constructed:
type='lin'
creates the linear kernel assuming additive SNP
effects to be evaluated in the logistic kernel machine test.
type='sia'
calculates the size-adjusted kernel which takes
into consideration the numbers of SNPs and genes in a pathway
to correct for size bias.
type='net'
calculates the network-based kernel. Here not only information on gene membership and gene/pathway size in number of SNPs is incorporated, but also the interaction structure of genes in the pathway
.
For more details, check the references.
Returns an object of class kernel
, including the similarity
matrix
of the pathway
for the considered individuals.
If knots
are specified low-rank kernel of class a lowrank_kernel
will be returned, which is not necessarily quadratic and symmetric.
lin_kernel(GWASdata)
:
sia_kernel(GWASdata)
:
net_kernel(GWASdata)
:
Stefanie Friedrichs, Juliane Manitz
Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X Powerful SNP-Set Analysis for Case-Control Genome-Wide Association Studies. Am J Hum Genet 2010, 86:929-42
Freytag S, Bickeboeller H, Amos CI, Kneib T, Schlather M: A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis. Hum Hered. 2012, 74(2):97-108.
Freytag S, Manitz J, Schlather M, Kneib T, Amos CI, Risch A, Chang-Claude J, Heinrich J, Bickeboeller H: A network-based kernel machine test for the identification of risk pathways in genome-wide association studies. Hum Hered. 2013, 76(2):64-75.
kernel-class
,pathway
data(gwas)
data(hsa04020)
lin_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='lin', calculation='cpu')
summary(lin_kernel)
net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu')
summary(net_kernel)
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