| Title: | Kernel Approaches for Nonlinear Genetic Association Regression |
|---|---|
| Description: | Methods to extract information on pathways, genes and various single-nucleotid polymorphisms (SNPs) from online databases. It provides functions for data preparation and evaluation of genetic influence on a binary outcome using the logistic kernel machine test (LKMT). Three different kernel functions are offered to analyze genotype information in this variance component test: A linear kernel, a size-adjusted kernel and a network-based kernel). |
| Authors: | Juliane Manitz [aut, cre], Benjamin Hofner [aut], Stefanie Friedrichs [aut], Patricia Burger [aut], Ngoc Thuy Ha [aut], Saskia Freytag [ctb], Heike Bickeboeller [ctb] |
| Maintainer: | Juliane Manitz <[email protected]> |
| License: | GPL-2 |
| Version: | 1.4.2 |
| Built: | 2024-11-08 05:29:42 UTC |
| Source: | https://github.com/jmanitz/kangar00 |
This package includes methods to extract information on pathways, genes and SNPs from online databases and to evaluate these data using the logistic kernel machine test (LKMT) (Liu et al. 2008).
We defined SNP sets representing genes and whole pathways using knowledge on gene membership and interaction from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Kanehisa et al. 2014). SNPs are mapped to genes via base pair positions of SNPs and transcript start and end points of genes as documented in the Ensemble database (Cunningham et al. 2015).
In the LKMT, we employed the linear kernel (Wu et al. 2010) as well as two more advanced kernels, adjusting for size bias in the number of SNPs and genes in a pathway (size-adjusted kernels), and incorporating the network structure of genes within the pathway (pathway kernels), respectively (Freytag et al. 2012, 2014). P-values are derived in a variance component test using a moment matching method (Schaid, 2010) or Davies' algorithm (Davies, 1980).
| Package: | kangar00 |
| Version: | 1.1 |
| Date: | 2017-08-07 |
| License: | GPL-2 |
Juliane Manitz [aut], Stefanie Friedrichs [aut], Patricia Burger [aut],
Benjamin Hofner [aut], Ngoc Thuy Ha [aut], Saskia Freytag [ctb],
Heike Bickeboeller [ctb]
Maintainer: Juliane Manitz <[email protected]>
Cunningham F, Ridwan Amode M, Barrell D, et al. Ensembl 2015. Nucleic Acids Research 2015 43 Database issue:D662-D669
Davies R: Algorithm as 155: the distribution of a linear combination of chi-2 random variables. J R Stat Soc Ser C 1980, 29:323-333.
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.
Friedrichs S, Manitz J, Burger P, Amos CI, Risch A, Chang-Claude JC, Wichmann HE, Kneib T, Bickeboeller H, Hofner B: Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies. Computational and Mathematical Methods in Medicine. 2017(6742763), 1-17. doi:10.1155/2017/6742763.
Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.; Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199-D205 (2014).
Liu D, Ghosh D, Lin X. Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models. BMC Bioinformatics. 2008 9:292.
Schaid DJ: Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations. Hum Hered 2010, 70:109-131.
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
A dataset containing an annotation example for 4056 SNPs in three different pathways.
data(anno)data(anno)
A data frame with 4056 rows and 5 variables:
includes KEGG identifiers of three example pathways
names of genes in the pathways
specifies the chromosome
includes rs-numbers of example SNPs
gives positions of example SNPs
simulated data
data(anno) head(anno) # create gwas object data(pheno) data(geno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study")data(anno) head(anno) # create gwas object data(pheno) data(geno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study")
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"), ...)## 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.
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)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)
A matrix containing example genotypes for 4056 SNPs of 50 individuals. Column names give the rs-numbers of 4056 example SNPs, row names the identifiers of 50 example individuals.
data(geno)data(geno)
A matrix with 5 rows and 4056 columns:
each entry in the matrix represents a simulated minor allele count for the corresponding SNP and individual.
simulated data
data(geno) head(geno) # create gwas object data(pheno) data(anno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study")data(geno) head(geno) # create gwas object data(pheno) data(anno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study")
A function to create the annotation for a GWASdata object.
It combines a snp_info and a pathway_info
object into an annotation data.frame used for pathway
analysis on GWAS. SNPs are assigned to pathways via gene membership.
## S4 method for signature 'snp_info,pathway_info' get_anno(object1, object2, ...)## S4 method for signature 'snp_info,pathway_info' get_anno(object1, object2, ...)
object1 |
A |
object2 |
A |
... |
further argdata(hsa04020) |
A data.frame mapping SNPs to genes and genes to
pathways. It includes the columns 'pathway', 'gene', 'chr', 'snp' and
'position'.
Stefanie Friedrichs, Saskia Freytag, Ngoc-Thuy Ha
data(hsa04022_info) # pathway_info('hsa04020') data(rs10243170_info)# snp_info("rs10243170") get_anno(rs10243170_info, hsa04022_info)data(hsa04022_info) # pathway_info('hsa04020') data(rs10243170_info)# snp_info("rs10243170") get_anno(rs10243170_info, hsa04022_info)
matrix for a pathway objectget_network_matrix creates the adjacency matrix representing the gene-gene interaction structure within a particular pathway. Note that a
KEGG kgml file is downloaded and saved in the working directory.
## S4 method for signature 'pathway' get_network_matrix(object, directed = TRUE, method = "auto")## S4 method for signature 'pathway' get_network_matrix(object, directed = TRUE, method = "auto")
object |
A |
directed |
A |
method |
Download method to be used for downloading files, passed to via |
get_network_matrix returns the modified pathway object, where the slots adj and sign are altered according to the downloaded information in the KEGG kgml file.
Stefanie Friedrichs, Patricia Burger, Juliane Manitz
GWASdata object.An object of type GWASdata containing the example files for annotation, phenotypes and genotypes.
data(gwas)data(gwas)
An object of class GWASdata:
contains example genotypes
example annotation for three pathways
exemplary phenotypes for all 'genotyped' individuals
a description of the GWAS study, here 'example study'
simulated data
# create gwas object data(pheno) data(geno) data(anno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study")# create gwas object data(pheno) data(geno) data(anno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study")
S4 class for an object representing a Genome-wide Assocaition Study.
'GWASdata' is a GWASdata object constructor.
show displays basic information on GWASdata object
summary summarizes the content of a GWASdata object
and gives an overview about the information included in a
GWASdata object. Summary statistics for phenotype and genotype
data are calculated.
GeneSNPsize creates a data.frame of pathway
names with numbers of snps and genes in each pathway.
GWASdata(object, ...) ## S4 method for signature 'ANY' GWASdata(geno, anno, pheno = NULL, desc = "") ## S4 method for signature 'GWASdata' show(object) ## S4 method for signature 'GWASdata' summary(object) ## S4 method for signature 'GWASdata' GeneSNPsize(object)GWASdata(object, ...) ## S4 method for signature 'ANY' GWASdata(geno, anno, pheno = NULL, desc = "") ## S4 method for signature 'GWASdata' show(object) ## S4 method for signature 'GWASdata' summary(object) ## S4 method for signature 'GWASdata' GeneSNPsize(object)
object |
A |
... |
Further arguments can be added to the function. |
geno |
An object of any type, including the genotype information. |
anno |
A |
pheno |
A |
desc |
A |
GeneSNPsize(GWASdata): creates a data.frame of pathway names with numbers
of snps and genes in each pathway.
genoAn object of any type, including genotype information. The format
needs to be one line per individual and on colum per SNP in minor-allele
coding (0,1,2). Other values between 0 and 2, as from impute dosages, are
allowed. Missing values must be imputed prior to creation of a GWASdata object.
annoA data.frame mapping SNPs to genes and genes to
pathways. Needs to include the columns 'pathway' (pathway ID, e.g. hsa
number from KEGG database), 'gene' (gene name (hgnc_symbol)), 'chr'
(chromosome), 'snp' (rsnumber) and 'position' (base pair position of SNP).
phenoA data.frame specifying individual IDs, phenotypes and
covariates to be included in the regression model e.g. ID, pheno, sex,
pack.years. Note: IDs have to be in the first column!
descA character giving the GWAS description, e.g. name of study.
Juliane Manitz, Stefanie Friedrichs
# create gwas data object data(pheno) data(geno) data(anno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study") # show and summary methods gwas summary(gwas) # SNPs and genes in pathway GeneSNPsize(gwas)# create gwas data object data(pheno) data(geno) data(anno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study") # show and summary methods gwas summary(gwas) # SNPs and genes in pathway GeneSNPsize(gwas)
pathway object for pathway hsa04020.An object of class pathway for the pathway with KEGG
identifier hsa04020.
data(hsa04020)data(hsa04020)
A pathway object including 180 genes.
KEGG identifier of the example pathways
gives the quadratic adjacency matrix for the pathway and with
that the network topology. Matrix dimensions equal the number of genes in the
pathway
includes a vector of signs to distinguish activations and
inhibitions in the adjacency matrix
simulated data and Ensembl extract
pathway_info object for pathway hsa04022.An object of class pathway_info for the pathway
with KEGG identifier hsa04020.
data(hsa04022_info)data(hsa04022_info)
A pathway_info object including information on 163 genes.
a data frame including information on genes included in
pathway. Has columns 'pathway', 'gene_start', 'gene_end', 'chr', and 'gene'
Ensembl extract
## Not run: pathway_info('hsa04020') ## End(Not run)## Not run: pathway_info('hsa04020') ## End(Not run)
An S4 class representing a kernel matrix calculated for a pathway
show displays the kernel object briefly
summary generates a kernel object summary including the number of
individuals and genes for the pathway
plot creates an image plot of a kernel object
## S4 method for signature 'kernel' show(object) ## S4 method for signature 'kernel' summary(object) ## S4 method for signature 'kernel,missing' plot(x, y = NA, hclust = FALSE, ...)## S4 method for signature 'kernel' show(object) ## S4 method for signature 'kernel' summary(object) ## S4 method for signature 'kernel,missing' plot(x, y = NA, hclust = FALSE, ...)
object |
An object of class |
x |
the |
y |
missing (placeholder). |
hclust |
|
... |
further arguments to be passed to the function. |
typeA character representing the kernel type: Use
'lin' for linear kernel, 'sia' for the size-adjusted
or 'net' for the network-based kernel.
kernelA kernel matrix of dimension equal to the number of individuals
pathwayA pathway object
Juliane Manitz
data(gwas) data(hsa04020) net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') show(net_kernel) summary(net_kernel) plot(net_kernel, hclust=TRUE)data(gwas) data(hsa04020) net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') show(net_kernel) summary(net_kernel) plot(net_kernel, hclust=TRUE)
For parameter 'satt' a pathway's influence on the probability of
beeing a case is evaluated in the logistic kernel machine test and p-values
are determined using a Sattherthwaite approximation as described by Dan Schaid.
For parameter 'davies' a pathways influence on the probability
of beeing a case is evaluated using the p-value calculation method described
by Davies. Here the function davies from package
CompQuadForm is used.
lkmt_test(formula, kernel, GWASdata, method = c("satt", "davies"), ...) ## S4 method for signature 'matrix' score_test(x1, x2) ## S4 method for signature 'matrix' davies_test(x1, x2)lkmt_test(formula, kernel, GWASdata, method = c("satt", "davies"), ...) ## S4 method for signature 'matrix' score_test(x1, x2) ## S4 method for signature 'matrix' davies_test(x1, x2)
formula |
The formula to be used for the regression nullmodel. |
kernel |
An object of class |
GWASdata |
A |
method |
A |
... |
Further arguments can be given to the function. |
x1 |
A |
x2 |
An |
An lkmt object including the following test results
The formula of the regression nullmodel used in the variance component test.
An object of class kernel including the similarity matrix of the individuals based on which the pathways influence is evaluated.
An object of class GWASdata stating the data on which the test was conducted.
statistic A vector giving the value of the variance component test statistic.
df A vector giving the number of degrees of freedom.
p.value A vector giving the p-value calculated for the pathway in the variance component test.
Stefanie Friedrichs, Juliane Manitz
For details on the variance component test
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
Liu D, Lin X, Ghosh D: Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics 2007, 63(4):1079-88.
For details on the p-value calculation see
Schaid DJ: Genomic Similarity and Kernel Methods I: Advancements by Building on Mathematical and Statistical Foundations. Hum Hered 2010, 70:109-31
Davies R: Algorithm as 155: the distribution of a linear combination of chi-2 random variables. J R Stat Soc Ser C 1980, 29:323-333.
data(hsa04020) data(gwas) net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') lkmt_test(pheno ~ sex + age, net_kernel, gwas, method='satt')data(hsa04020) data(gwas) net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') lkmt_test(pheno ~ sex + age, net_kernel, gwas, method='satt')
An S4 class to represent the variance component test.
show displays basic information on lkmt object
summary generates a lkmt object summary including the used kernel, pathway and the test result
## S4 method for signature 'lkmt' show(object) ## S4 method for signature 'lkmt' summary(object)## S4 method for signature 'lkmt' show(object) ## S4 method for signature 'lkmt' summary(object)
object |
An object of class |
show Basic information on lkmt object.
summary Summarized information on lkmt object.
formulaA formula stating the regression nullmodel that will be used in the variance component test.
kernelAn object of class kernel representing the similarity
matrix of the individuals based on which the pathways influence is evaluated.
GWASdataAn object of class GWASdata including the data
on which the test is conducted.
statisticA vector giving the value of the variance component
test statistic.
dfA vector containing the number of degrees of freedom.
p.valueA vector giving the p-value calculated for the pathway object considered in the variance component test.
For details on the variance component test see the references.
Juliane Manitz, Stefanie Friedrichs
Liu D, Lin X, Ghosh D: Semiparametric regression of multidimensional genetic pathway data: least-squares kernel machines and linear mixed models. Biometrics 2007, 63(4):1079-88.
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
data(hsa04020) data(gwas) # compute kernel net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') # perform LKMT res <- lkmt_test(pheno ~ sex + age, net_kernel, gwas, method='satt') # show and summary methods show(res) summary(res) # summary method summary(lkmt.net.kernel.hsa04020)data(hsa04020) data(gwas) # compute kernel net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') # perform LKMT res <- lkmt_test(pheno ~ sex + age, net_kernel, gwas, method='satt') # show and summary methods show(res) summary(res) # summary method summary(lkmt.net.kernel.hsa04020)
kernel for
pathway hsa04020.An object of class lkmt containing exemplary test results for an
application of the logistic kernel machine test, derived from the example data.
data(lkmt.net.kernel.hsa04020)data(lkmt.net.kernel.hsa04020)
An object of class lkmt for the network-based
kernel and the pathway hsa04020.
gives a formular defining the nullmodel used in the logistic kernel machine test
gives the GWASdata object including the study data
considered in testing
gives the value of the test statistic
specifies the degrees of freedom
includes teh p-value resulting from the test
simulated data and Ensembl extract
data(hsa04020) data(gwas) net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') lkmt_test(pheno ~ sex + age, net_kernel, gwas, method='satt')data(hsa04020) data(gwas) net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') lkmt_test(pheno ~ sex + age, net_kernel, gwas, method='satt')
An S4 class to represent a low-rank kernel for a SNPset at specified knots
This kernel is used for predictions. If observations and knots are
equal, better construct a full-rank kernel of class kernel.
typecharacter, kernel type: Use 'lin' for the linear kernel,
'sia' for the size-adjusted or 'net' for the network-based kernel.
kernelkernel matrix of dimension equal to individuals
pathwaypathway object
Juliane Manitz
data(gwas) data(hsa04020) square <- calc_kernel(gwas, hsa04020, knots=gwas, type='lin', calculation='cpu') dim(square@kernel) gwas2 <- new('GWASdata', pheno=pheno[1:10,], geno=geno[1:10,], anno=anno, desc="study 2") low_rank <- calc_kernel(gwas, hsa04020, knots = gwas2, type='net', calculation='cpu') dim(low_rank@kernel)data(gwas) data(hsa04020) square <- calc_kernel(gwas, hsa04020, knots=gwas, type='lin', calculation='cpu') dim(square@kernel) gwas2 <- new('GWASdata', pheno=pheno[1:10,], geno=geno[1:10,], anno=anno, desc="study 2") low_rank <- calc_kernel(gwas, hsa04020, knots = gwas2, type='net', calculation='cpu') dim(low_rank@kernel)
matrix to be positive semi-definiteAdjust network matrix to be positive semi-definite
## S4 method for signature 'matrix' make_psd(x, eps = sqrt(.Machine$double.eps))## S4 method for signature 'matrix' make_psd(x, eps = sqrt(.Machine$double.eps))
x |
A |
eps |
A |
For a matrix N, the closest positive semi-definite matrix is
calculated as N* = rho*N + (1+rho)*I, where I is the identity matrix
and rho = 1/(1 - lambda) with lambda the smallest eigenvalue of N.
For more details check the references.
The matrix x, if it is positive definite and the closest
positive semi-definite matrix if x is not positive semi-definite.
Juliane Manitz, Saskia Freytag, Stefanie Friedrichs
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.
set.seed(2345) m <- matrix(data=sample(size=25, c(0,0,1), replace=TRUE),5,5) m <- m + t(m) min(eigen(m, only.values = TRUE, symmetric = TRUE)$values) round(make_psd(m),2)set.seed(2345) m <- matrix(data=sample(size=25, c(0,0,1), replace=TRUE),5,5) m <- m + t(m) min(eigen(m, only.values = TRUE, symmetric = TRUE)$values) round(make_psd(m),2)
An example of a kernel object.
data(net.kernel.hsa04020)data(net.kernel.hsa04020)
An object of class kernel and type 'network' for the pathway
hsa04020.
specifies which kernel function was used to calculate the kernel
includes the kernel matrix calculated for the pathway
includes the pathway object of the pathway, for which
the kernel matrix was calculated
simulated data and Ensembl extract
data(net.kernel.hsa04020) # derivation data(gwas) data(hsa04020) net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') # are the value differences smaller than machine epsilon? all(abs(net.kernel.hsa04020@kernel - net_kernel@kernel) < sqrt(.Machine$double.eps))data(net.kernel.hsa04020) # derivation data(gwas) data(hsa04020) net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu') # are the value differences smaller than machine epsilon? all(abs(net.kernel.hsa04020@kernel - net_kernel@kernel) < sqrt(.Machine$double.eps))
An S4 class to represent a gene-gene interaction network
pathway is the pathway object constructor.
show displays the pathway object briefly
summary generates a pathway object summary including basic network properties.
pathway2igraph converts a pathway object into an
igraph object with edge attribute sign
analyze pathway network properties
get_genes is a helper function that extracts the gene names in a
pathway and returns a vector containing character
elements of gene names
plot visualizes the pathway as igraph object
sample_genes randomly selects effect gene in a
pathway according the betweenness centrality and (no -1) neighors
pathway(object, ...) ## S4 method for signature 'ANY' pathway(id, adj = matrix(0), sign = NULL) ## S4 method for signature 'pathway' show(object) ## S4 method for signature 'pathway' summary(object) ## S4 method for signature 'pathway' pathway2igraph(object) ## S4 method for signature 'pathway' analyze(object, ...) ## S4 method for signature 'pathway' get_genes(object) ## S4 method for signature 'pathway,missing' plot( x, y = NA, highlight.genes = NULL, gene.names = c(NULL, "legend", "nodes"), main = NULL, asp = 0.95, vertex.size = 11, vertex.color = "khaki1", vertex.label.cex = 0.8, edge.width = 2, edge.color = "olivedrab4", ... ) ## S4 method for signature 'pathway' sample_genes(object, no = 3)pathway(object, ...) ## S4 method for signature 'ANY' pathway(id, adj = matrix(0), sign = NULL) ## S4 method for signature 'pathway' show(object) ## S4 method for signature 'pathway' summary(object) ## S4 method for signature 'pathway' pathway2igraph(object) ## S4 method for signature 'pathway' analyze(object, ...) ## S4 method for signature 'pathway' get_genes(object) ## S4 method for signature 'pathway,missing' plot( x, y = NA, highlight.genes = NULL, gene.names = c(NULL, "legend", "nodes"), main = NULL, asp = 0.95, vertex.size = 11, vertex.color = "khaki1", vertex.label.cex = 0.8, edge.width = 2, edge.color = "olivedrab4", ... ) ## S4 method for signature 'pathway' sample_genes(object, no = 3)
object |
An object of class |
... |
Further arguments can be added to the function. |
id |
A |
adj |
A |
sign |
A |
x |
|
y |
missing (placeholder) |
highlight.genes |
vector of gene names or node id's, which should be highlighted in a different color, default is |
gene.names |
character indicating whether the genes names should appear in a legend ( |
main |
optional overall main title, default is |
asp |
a |
vertex.size |
a |
vertex.color |
a |
vertex.label.cex |
a |
edge.width |
a |
edge.color |
a |
no |
a |
pathway2igraph returns an unweighted igraph object with edge attribute sign
analyze returns a data.frame consisting of
pathway id,
number of genes,
number of links,
number of inhibition links,
network density,
average degree,
average degree of inhibition links,
network diamter,
transitivity, and
signed transitivity (Kunegis et al., 2009).
get_genes returns a character vector of gene names extracted from adjacency matrix rownames.
sample_genes returns a vector of length no with
vertex id's of sampled genes
analyze(pathway):
get_genes(pathway):
sample_genes(pathway):
idA character repesenting the pathway id,
e.g. hsa00100 as used in the KEGG database.
adjA matrix respresenting the network adjacency matrix of dimension
equaling the number of genes (1 interaction, 0 otherwise)
signA numeric vector indicating the interaction type for
each link (1 activation, -1 inhibition) in the interaction network for the
pathway.
Juliane Manitz, Stefanie Friedrichs, Patricia Burger
Details to the computation and interpretation can be found in:
Kolaczyk, E. D. (2009). Statistical analysis of network data: methods and models. Springer series in statistics. Springer.
Kunegis, J., A. Lommatzsch, and C. Bauckhage (2009). The slashdot zoo: Mining a social network with negative egdes. In Proceedings of the 18th international conference on World wide web, pp. 741-750. ACM Press.
# pathway object constructor pathway(id="hsa04022") # convert to igraph object data(hsa04020) str(hsa04020) g <- pathway2igraph(hsa04020) str(g) # analyze pathway network properties data(hsa04020) summary(hsa04020) analyze(hsa04020) # extract gene names from pathway object get_genes(hsa04020) # plot pathway as igraph object plot(hsa04020) sample3 <- sample_genes(hsa04020, no = 3) plot(hsa04020, highlight.genes = sample3) # sample effect genes sample3 <- sample_genes(hsa04020, no = 3) plot(hsa04020, highlight.genes = sample3) sample5 <- sample_genes(hsa04020, no = 5) plot(hsa04020, highlight.genes = sample5)# pathway object constructor pathway(id="hsa04022") # convert to igraph object data(hsa04020) str(hsa04020) g <- pathway2igraph(hsa04020) str(g) # analyze pathway network properties data(hsa04020) summary(hsa04020) analyze(hsa04020) # extract gene names from pathway object get_genes(hsa04020) # plot pathway as igraph object plot(hsa04020) sample3 <- sample_genes(hsa04020, no = 3) plot(hsa04020, highlight.genes = sample3) # sample effect genes sample3 <- sample_genes(hsa04020, no = 3) plot(hsa04020, highlight.genes = sample3) sample5 <- sample_genes(hsa04020, no = 5) plot(hsa04020, highlight.genes = sample5)
This function lists all genes formig a particular pathway. Start and end
positions of these genes are extracted from the Ensemble database. The
database is accessed via the R-package biomaRt.
pathway_info(x) ## S4 method for signature 'character' pathway_info(x) ## S4 method for signature 'pathway_info' show(object) ## S4 method for signature 'pathway_info' summary(object)pathway_info(x) ## S4 method for signature 'character' pathway_info(x) ## S4 method for signature 'pathway_info' show(object) ## S4 method for signature 'pathway_info' summary(object)
x |
A |
object |
An object of class |
A data.frame including as many rows as genes appear in the
pathway. for each gene its name, the start and end point and the chromosome
it lies on are given.
show Basic information on pathway_info object.
summary Summarized information on pathway_info object.
infoA data.frame including information on genes contained in
pathways with columns 'pathway', 'gene_start', 'gene_end', 'chr' and 'gene'.
Stefanie Friedrichs, Juliane Manitz
data(hsa04022_info) # pathway_info('hsa04020') show(hsa04022_info) summary(hsa04022_info)data(hsa04022_info) # pathway_info('hsa04020') show(hsa04022_info) summary(hsa04022_info)
A dataset containing simulated example phenotypes for 50 individuals row names include the identifiers of 50 example individuals.
data(pheno)data(pheno)
A data frame with 50 rows and 3 variables:
includes the case-control status for each individual, coded as 1(case) or 0 (control)
includes gender information for the 50 individuals, coded as 1 (male) or 0 (female)
numerical value giving the persons age
simulated data
data(pheno) head(pheno) # create gwas object data(geno) data(anno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study")data(pheno) head(pheno) # create gwas object data(geno) data(anno) gwas <- new('GWASdata', pheno=pheno, geno=geno, anno=anno, desc="some study")
GWASdata.read genotype data from file to one of several available objects, which
can be passed to a GWASdata object GWASdata.
## S4 method for signature 'character' read_geno( file.path, save.path = NULL, sep = " ", header = TRUE, use.fread = TRUE, use.big = FALSE, row.names = FALSE, ... )## S4 method for signature 'character' read_geno( file.path, save.path = NULL, sep = " ", header = TRUE, use.fread = TRUE, use.big = FALSE, row.names = FALSE, ... )
file.path |
|
save.path |
|
sep |
|
header |
|
use.fread |
|
use.big |
|
row.names |
|
... |
further arguments to be passed to |
If the data set contains rownames specified, set option has.row.names = TRUE.
## Not run: path <- system.file("extdata", "geno.txt", package = "kangar00") geno <- read_geno(path, save.path = getwd(), sep = " ", use.fread = FALSE, row.names = FALSE) ## End(Not run)## Not run: path <- system.file("extdata", "geno.txt", package = "kangar00") geno <- read_geno(path, save.path = getwd(), sep = " ", use.fread = FALSE, row.names = FALSE) ## End(Not run)
pathway, which go through a gene not
represented by any SNPs in the considered GWASdata dataset.Rewires interactions in a pathway, which go through a gene not
represented by any SNPs in the considered GWASdata dataset.
## S4 method for signature 'pathway' rewire_network(object, x)## S4 method for signature 'pathway' rewire_network(object, x)
object |
|
x |
A |
A pathway object including the rewired network matrix
Juliane Manitz, Stefanie Friedrichs
## Not run: data(hsa04020) summary(hsa04020) hsa04020_rewired <- rewire_network(hsa04020, x=c('ADCY3', 'CALML3','GNAQ')) summary(hsa04020_rewired) ## End(Not run)## Not run: data(hsa04020) summary(hsa04020) hsa04020_rewired <- rewire_network(hsa04020, x=c('ADCY3', 'CALML3','GNAQ')) summary(hsa04020_rewired) ## End(Not run)
snp_info object for SNP rs10243170.An object of class snp_info for rs10243170.
data(rs10243170_info)data(rs10243170_info)
A snp_info object including information on the SNP as
extracted from the Ensembl database.
a data frame including the extracted information on the
SNP. Columns given are 'chr', 'position', and 'rsnumber'
Ensembl extract
## Not run: snp_info("rs10243170") ## End(Not run)## Not run: snp_info("rs10243170") ## End(Not run)
An S4 class for an object assigning SNP positions to rs-numbers (for internal use)
This function gives for a vector of SNP identifiers the position of each SNP
as extracted from the Ensemble database. The database is accessed via the
R-package biomaRt.
show Shows basic information on snp_info object
summary Summarizes information on snp_info object
snp_info(x, ...) ## S4 method for signature 'character' snp_info(x) ## S4 method for signature 'snp_info' show(object) ## S4 method for signature 'snp_info' summary(object)snp_info(x, ...) ## S4 method for signature 'character' snp_info(x) ## S4 method for signature 'snp_info' show(object) ## S4 method for signature 'snp_info' summary(object)
x |
A |
... |
further arguments can be added. |
object |
An |
A data.frame including the SNP positions with columns
'chromosome', 'position' and 'snp'. SNPs not found in the Ensemble database
will not be listed in the returned snp_info object, SNPs with multiple
positions will appear several times.
show Basic information on snp_info object.
summary Summarized information on snp_info object.
infoA data.frame including information on SNP positions
Stefanie Friedrichs
data(rs10243170_info) # snp_info("rs10243170") rs10243170_info summary(rs10243170_info)data(rs10243170_info) # snp_info("rs10243170") rs10243170_info summary(rs10243170_info)