| Title: | Gene Set Analysis with Fisher Combined Method |
|---|---|
| Description: | Provides the p-value for a joint test of association between a phenotype and a set of genetic variants (SNPs) by combining marginal p-values using the Fisher method. See Fisher, R.A. (1925,ISBN:0-05-002170-2) Statistical Methods for Research Workers. |
| Authors: | Carlos Garcia Prieto |
| Maintainer: | Carlos Garcia Prieto <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0 |
| Built: | 2024-11-05 06:41:23 UTC |
| Source: | CRAN |
Provides the p-value for a joint test of association between a phenotype and a set of genetic variants (SNPs) by combining marginal p-values using the Fisher method. See Fisher, R.A. (1925,ISBN:0-05-002170-2) Statistical Methods for Research Workers.
Package: GSAfisherCombined Type: Package Version: 1.0 Date: 2018-02-10 License: GPL (>= 2)
Carlos Garcia Prieto
Maintainer: Carlos Garcia Prieto <[email protected]>
Fisher, R.A. (1925). Statistical Methods for Research Workers. ISBN 0-05-002170-2.
It is a generic function that dispatches different methods. It provides the p-value for a joint test of association between a phenotype and a set of genetic variants (SNPs) by combining marginal p-values using the Fisher method.
GSAfisher(x,...)GSAfisher(x,...)
x |
Set of marginal p-values. |
... |
Further arguments passed to other methods. |
Use methods("GSAfisher") to get all the methods for the GSAfisher generic.
UseMethod("GSAfisher")
Carlos Garcia Prieto
Fisher, R.A. (1925). Statistical Methods for Research Workers. ISBN 0-05-002170-2.
Default method of GSAfisher function that computes the p-value for a joint test of association between a phenotype and a set of genetic variants (SNPs) by combining marginal p-values using the Fisher method.
## Default S3 method: GSAfisher(x,...)## Default S3 method: GSAfisher(x,...)
x |
Numeric set of marginal p-values (one vector or a sinlge p-value). |
... |
Further arguments passed to or from other methods. |
p |
Fisher method combined p-value. |
Carlos Garcia Prieto
Fisher, R.A. (1925). Statistical Methods for Research Workers. ISBN 0-05-002170-2.
#Generate 1000 random values from an Uniform distribution U (0, 0.25). x<-runif(1000, 0, 0.25) #Apply the GSAfisher.default function to this data vector. GSAfisher(x)#Generate 1000 random values from an Uniform distribution U (0, 0.25). x<-runif(1000, 0, 0.25) #Apply the GSAfisher.default function to this data vector. GSAfisher(x)
Multiple method of GSAfisher function that computes the p-value for a joint test of association between a phenotype and multiple gene-sets of genetic variants (SNPs) by combining multiple marginal p-values data vectors using the Fisher method.
## S3 method for class 'multiple' GSAfisher(...)## S3 method for class 'multiple' GSAfisher(...)
... |
Set of multiple marginal p-values data vectors or matrix. |
p.multiple |
Set of multiple Fisher method combined p-values for each data vector. |
Carlos Garcia Prieto
#Generate 3 vectors with 1000 random values from an Uniform distribution U (0, 0.25). data.vector1<-runif(1000, 0, 0.25) data.vector2<-runif(1000, 0, 0.25) data.vector3<-runif(1000, 0, 0.25) #Set data class to "multiple". class(data.vector1)<-"multiple" #Apply the GSAfisher.multiple function to these data vectors. GSAfisher(data.vector1, data.vector2, data.vector3) #You can also try: #Generate 5000 vectors with 1000 random values from an Uniform distribution U(0,0.25). data.vectors<-sapply(1:5000, function (x) runif(1000, 0, 0.25)) #Set data class to "multiple". class(data.vectors)<-"multiple" #Apply the GSAfisher.multiple function to these data vectors. GSAfisher(data.vectors)#Generate 3 vectors with 1000 random values from an Uniform distribution U (0, 0.25). data.vector1<-runif(1000, 0, 0.25) data.vector2<-runif(1000, 0, 0.25) data.vector3<-runif(1000, 0, 0.25) #Set data class to "multiple". class(data.vector1)<-"multiple" #Apply the GSAfisher.multiple function to these data vectors. GSAfisher(data.vector1, data.vector2, data.vector3) #You can also try: #Generate 5000 vectors with 1000 random values from an Uniform distribution U(0,0.25). data.vectors<-sapply(1:5000, function (x) runif(1000, 0, 0.25)) #Set data class to "multiple". class(data.vectors)<-"multiple" #Apply the GSAfisher.multiple function to these data vectors. GSAfisher(data.vectors)
Plot method of GSAfisher function that plots the p-values for a joint test of association between a phenotype and multiple gene-sets of genetic variants (SNPs) by combining multiple marginal p-values data vectors using the Fisher method.
## S3 method for class 'plot' GSAfisher(...)## S3 method for class 'plot' GSAfisher(...)
... |
Set of multiple marginal p-values data vectors or matrix. |
plot.p.multiple |
Plot of a set of multiple Fisher method combined p-values for each data vector. |
Carlos Garcia Prieto
#Generate 3 vectors with 1000 random values from an Uniform distribution U(0, 0.25). data.vector1<-runif(1000, 0, 0.25) data.vector2<-runif(1000, 0, 0.25) data.vector3<-runif(1000, 0, 0.25) #Set data class to "plot". class(data.vector1)<-"plot" #Apply the GSAfisher.plot function to these data vectors. GSAfisher(data.vector1, data.vector2, data.vector3) #You can also try: #Generate 5000 vectors with 1000 random values from an Uniform distribution U(0,0.25). data.vectors<-sapply(1:5000, function (x) runif(1000, 0, 0.25)) #Set data class to "plot". class(data.vectors)<-"plot" #Apply the GSAfisher.plot function to these data vectors. GSAfisher(data.vectors)#Generate 3 vectors with 1000 random values from an Uniform distribution U(0, 0.25). data.vector1<-runif(1000, 0, 0.25) data.vector2<-runif(1000, 0, 0.25) data.vector3<-runif(1000, 0, 0.25) #Set data class to "plot". class(data.vector1)<-"plot" #Apply the GSAfisher.plot function to these data vectors. GSAfisher(data.vector1, data.vector2, data.vector3) #You can also try: #Generate 5000 vectors with 1000 random values from an Uniform distribution U(0,0.25). data.vectors<-sapply(1:5000, function (x) runif(1000, 0, 0.25)) #Set data class to "plot". class(data.vectors)<-"plot" #Apply the GSAfisher.plot function to these data vectors. GSAfisher(data.vectors)
Print method of GSAfisher function that computes the p-value for a joint test of association between a phenotype and a set of genetic variants (SNPs) by combining marginal p-values using the Fisher method and prints the result.
## S3 method for class 'print' GSAfisher(x,...)## S3 method for class 'print' GSAfisher(x,...)
x |
Numeric set of marginal p-values (one vector or a single p-value). |
... |
Further arguments passed to or from other methods. |
print.p |
Displays Fisher method combined pvalue. |
Carlos Garcia Prieto
#Generate 1000 random values from an Uniform distribution U(0, 0.25). x<-runif(1000, 0, 0.25) #Set data class to "print". class(x)<-"print" #Apply the GSAfisher.print function to this data vector. GSAfisher(x)#Generate 1000 random values from an Uniform distribution U(0, 0.25). x<-runif(1000, 0, 0.25) #Set data class to "print". class(x)<-"print" #Apply the GSAfisher.print function to this data vector. GSAfisher(x)
Summary method of GSAfisher function that computes a numerical summary of the marginal p-values.
## S3 method for class 'summary' GSAfisher(x,...)## S3 method for class 'summary' GSAfisher(x,...)
x |
Numeric set of marginal p-values (one vector or a sinlge p-value). |
... |
Further arguments passed to or from other methods. |
summary.p |
Summary statistics (min, 1st quantile, median, 3rd quantile, max) of a numerical set of marginal p-values. |
Carlos Garcia Prieto
#Generate 1000 random values from an Uniform distribution U (0, 0.25). x<-runif(1000, 0, 0.25) #Set data class to "summary" class(x)<-"summary" #Apply the GSAfisher.summary function to this data vector. GSAfisher(x)#Generate 1000 random values from an Uniform distribution U (0, 0.25). x<-runif(1000, 0, 0.25) #Set data class to "summary" class(x)<-"summary" #Apply the GSAfisher.summary function to this data vector. GSAfisher(x)