Package 'rqt' reference manual

Title:	rqt: utilities for gene-level meta-analysis
Description:	Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, <doi:10.1093/bioinformatics/btw429>.
Authors:	Ilya Zhbannikov [aut, cre], Konstantin Arbeev [aut], Anatoliy Yashin [aut]
Maintainer:	Ilya Zhbannikov <[email protected]>
License:	GPL
Version:	1.33.0
Built:	2024-11-06 06:15:07 UTC
Source:	https://github.com/bioc/rqt

Applies linear of logistic regregression to the data.

Description

Applies linear of logistic regregression to the data.

Usage

build.null.model(y, x, reg.family = "binomial", verbose = FALSE)
build.null.model(y, x, reg.family = "binomial", verbose = FALSE)

Arguments

`y`	A vector with values of dependent variable (outcome).
`x`	A data.frame of covariates.
`reg.family`	A regression family. Can be either "binomial" or "gaussian."
`verbose`	Indicates verbosing output. Default: FALSE.

Value

A list of two: "S" - a dataframe with predictors and "fit" - an object returned by "glm" function.

This function performs an access to covariates

Description

An accessor to covariates

Usage

covariates(obj)

## S4 method for signature 'rqt'
covariates(obj)
covariates(obj)

## S4 method for signature 'rqt'
covariates(obj)

Arguments

obj

An object of rqt class.

Value

covariates returns the covariates

Examples

data <- data.matrix(read.table(system.file("extdata/test.bin1.dat", 
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
covariates(obj)
data <- data.matrix(read.table(system.file("extdata/test.bin1.dat", 
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
covariates(obj)

This function performs a gene-level test based on combined effect sizes.

Description

This function performs a gene-level test based on combined effect sizes.

geneTest This function performs a gene-level test based on combined effect sizes.

Usage

geneTest(obj, ...)

## S4 method for signature 'rqt'
geneTest(
  obj,
  perm = 0,
  STT = 0.2,
  weight = FALSE,
  cumvar.threshold = 75,
  out.type = "D",
  method = "pca",
  scaleData = FALSE,
  asym.pval = FALSE,
  penalty = 0.001,
  verbose = FALSE
)
geneTest(obj, ...)

## S4 method for signature 'rqt'
geneTest(
  obj,
  perm = 0,
  STT = 0.2,
  weight = FALSE,
  cumvar.threshold = 75,
  out.type = "D",
  method = "pca",
  scaleData = FALSE,
  asym.pval = FALSE,
  penalty = 0.001,
  verbose = FALSE
)

Arguments

`obj`	Object of class `rqt`
`...`	Additional parameters to pass to the function
`perm`	Integer indicating the number of permutations to compute p-values. Default: 0.
`STT`	Numeric indicating soft truncation threshold (STT) to convert to gamma parameter (must be <= 0.4). Needed for an optimal parameter a in Gamma-distribution. Default: 0.2. See, for example, Fridley, et al 2013: "Soft truncation thresholding for gene set analysis of RNA-seq data: Application to a vaccine study".
`weight`	Logical value. Indicates using weights (see Lee et al 2016). Default: FALSE.
`cumvar.threshold`	Numeric value indicating the explained variance threshold for PCA-like methods. Default: 75
`out.type`	Character, indicating a type of phenotype. Possible values: `D` (dichotomous or binary), `C` (continous or quantitative).
`method`	Method used to reduce multicollinerity and account for LD. Default: `pca`. Another methods available: `lasso`, `ridge`, `pls`.
`scaleData`	A logic parameter (TRUE/FALSE) indicating scaling of the genotype dataset.
`asym.pval`	Indicates Monte Carlo approximation for p-values. Default: FALSE.
`penalty`	A value of `penalty` parameter for LASSO/ridge regression. Default: 0.001
`verbose`	Indicates verbosing output. Default: FALSE.

Value

Updated rqt object with result slot

Object of class rqt

Examples

data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
res <- geneTest(obj, method="pca", out.type = "D")
print(res)
data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
res <- geneTest(obj, method="pca", out.type = "D")
print(res)

This function performs a gene-level meta-analysis based on combined effect sizes.

Description

This function performs a gene-level meta-analysis based on combined effect sizes.

Usage

geneTestMeta(objects, ...)

## S4 method for signature 'list'
geneTestMeta(
  objects,
  perm = 0,
  STT = 0.2,
  weight = FALSE,
  cumvar.threshold = 75,
  out.type = "D",
  method = "pca",
  scaleData = FALSE,
  asym.pval = FALSE,
  comb.test = "wilkinson",
  penalty = 0.001,
  verbose = FALSE
)
geneTestMeta(objects, ...)

## S4 method for signature 'list'
geneTestMeta(
  objects,
  perm = 0,
  STT = 0.2,
  weight = FALSE,
  cumvar.threshold = 75,
  out.type = "D",
  method = "pca",
  scaleData = FALSE,
  asym.pval = FALSE,
  comb.test = "wilkinson",
  penalty = 0.001,
  verbose = FALSE
)

Arguments

`objects`	List of objects of class rqt
`...`	Additional parameters to pass to the function
`perm`	Integer indicating the number of permutations to compute p-values. Default: 0.
`STT`	Numeric indicating soft truncation threshold (STT) to convert to gamma parameter (must be <= 0.4). Needed for an optimal parameter a in Gamma-distribution. Default: 0.2. See, for example, Fridley, et al 2013: "Soft truncation thresholding for gene set analysis of RNA-seq data: Application to a vaccine study".
`weight`	Logical value. Indicates using weights (see Lee et al 2016). Default: FALSE.
`cumvar.threshold`	Numeric value indicating the explained variance threshold for PCA-like methods. Default: 75
`out.type`	Character, indicating a type of phenotype. Possible values: `D` (dichotomous or binary), `C` (continous or quantitative).
`method`	Method used to reduce multicollinerity and account for LD. Default: `pca`. Another methods available: `lasso`, `ridge`, `pls`.
`scaleData`	A logic parameter (TRUE/FALSE) indicating scaling of the genotype dataset.
`asym.pval`	Indicates Monte Carlo approximation for p-values. Default: FALSE.
`comb.test`	Statistical test for combining p-values.
`penalty`	Value of `penalty` parameter for LASSO/ridge regression. Default: 0.001
`verbose`	Indicates verbosing output. Default: FALSE.

Value

A list of two: (i) final.pvalue - a final p-value across all studies; (ii) pvalueList - p-values for each study;

Examples

data1 <- data.matrix(read.table(system.file("extdata/phengen2.dat",
                                           package="rqt"), skip=1))
pheno <- data1[,1]
geno <- data1[, 2:dim(data1)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj1 <- rqt(phenotype=pheno, genotype=geno.obj)

data2 <- data.matrix(read.table(system.file("extdata/phengen3.dat",
                                           package="rqt"), skip=1))
pheno <- data2[,1]
geno <- data2[, 2:dim(data2)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj2 <- rqt(phenotype=pheno, genotype=geno.obj)

data3 <- data.matrix(read.table(system.file("extdata/phengen.dat",
                                           package="rqt"), skip=1))
pheno <- data3[,1]
geno <- data3[, 2:dim(data3)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj3 <- rqt(phenotype=pheno, genotype=geno.obj)

res.meta <- geneTestMeta(list(obj1, obj2, obj3))
print(res.meta)
data1 <- data.matrix(read.table(system.file("extdata/phengen2.dat",
                                           package="rqt"), skip=1))
pheno <- data1[,1]
geno <- data1[, 2:dim(data1)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj1 <- rqt(phenotype=pheno, genotype=geno.obj)

data2 <- data.matrix(read.table(system.file("extdata/phengen3.dat",
                                           package="rqt"), skip=1))
pheno <- data2[,1]
geno <- data2[, 2:dim(data2)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj2 <- rqt(phenotype=pheno, genotype=geno.obj)

data3 <- data.matrix(read.table(system.file("extdata/phengen.dat",
                                           package="rqt"), skip=1))
pheno <- data3[,1]
geno <- data3[, 2:dim(data3)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj3 <- rqt(phenotype=pheno, genotype=geno.obj)

res.meta <- geneTestMeta(list(obj1, obj2, obj3))
print(res.meta)

get.reg.family

Description

get.reg.family

Usage

geneTestOne(
  phenotype,
  genotype,
  covariates,
  STT = 0.2,
  weight = FALSE,
  cumvar.threshold = 75,
  method = "pca",
  out.type = "D",
  scaleData = FALSE,
  penalty = 0.001,
  verbose = FALSE
)
geneTestOne(
  phenotype,
  genotype,
  covariates,
  STT = 0.2,
  weight = FALSE,
  cumvar.threshold = 75,
  method = "pca",
  out.type = "D",
  scaleData = FALSE,
  penalty = 0.001,
  verbose = FALSE
)

Arguments

`phenotype`	phenotype
`genotype`	genotype
`covariates`	covariates
`STT`	STT
`weight`	weight
`cumvar.threshold`	cumvar.threshold
`method`	method
`out.type`	out.type
`scaleData`	scaleData
`penalty`	penalty
`verbose`	verbose

Value

rslt

This function performs an access to genotype.

Description

A genotype accessor

Usage

genotype(obj)

## S4 method for signature 'rqt'
genotype(obj)
genotype(obj)

## S4 method for signature 'rqt'
genotype(obj)

Arguments

obj

An object of rqt class.

Value

genotype returns the genotype

Examples

data <- data.matrix(read.table(system.file("extdata/test.bin1.dat", 
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
genotype(obj)
data <- data.matrix(read.table(system.file("extdata/test.bin1.dat", 
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
genotype(obj)

Get a given STT

Description

Get a given STT

Usage

get.a(L, STT = 0.2)
get.a(L, STT = 0.2)

Arguments

`L`	TODO
`STT`	Numeric indicating soft truncation threshold (STT) to convert to gamma parameter (must be <= 0.4).

Value

a number from gamma distribution

get.reg.family

Description

get.reg.family

Usage

get.reg.family(out.type = "D")
get.reg.family(out.type = "D")

Arguments

out.type

out.type

Value

reg.family

This function performs an access to phenotype

Description

A phenotype accessor

Usage

phenotype(obj)

## S4 method for signature 'rqt'
phenotype(obj)
phenotype(obj)

## S4 method for signature 'rqt'
phenotype(obj)

Arguments

obj

An object of rqt class.

Value

phenotype returns the phenotype

Examples

data <- data.matrix(read.table(system.file("extdata/test.bin1.dat", 
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
phenotype(obj)
data <- data.matrix(read.table(system.file("extdata/test.bin1.dat", 
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
phenotype(obj)

Preprocess input data with Principal Component Analysis method (PCA)

Description

Preprocess input data with Principal Component Analysis method (PCA)

Usage

preprocess(
  data,
  pheno = NULL,
  method = "pca",
  reg.family = "binomial",
  scaleData = FALSE,
  cumvar.threshold = 75,
  out.type = "D",
  penalty = 0.001,
  verbose = FALSE
)
preprocess(
  data,
  pheno = NULL,
  method = "pca",
  reg.family = "binomial",
  scaleData = FALSE,
  cumvar.threshold = 75,
  out.type = "D",
  penalty = 0.001,
  verbose = FALSE
)

Arguments

`data`	An input matrix with values of independent variables (predictors).
`pheno`	A phenotype - column-vector, needed for LASSO/ridge and `NULL` by default.
`method`	A dimensionality reduction method. Default: `pca`.
`reg.family`	A regression family. Default: `"binomial"`.
`scaleData`	A logical variable, indicates wheither or not scaling should be performed. Default: `FALSE`.
`cumvar.threshold`	A threshold value for explained variance. Default: `75`
`out.type`	An output (phenotype) type. Default: `"D"`
`penalty`	Value of penalty parameter for LASSO/ridge regression. Default: `0.001`
`verbose`	Indicates verbosing output. Default: FALSE.

Value

A list of one: "S" - a data frame of predictor values.

preprocessLASSO

Description

preprocessLASSO

Usage

preprocessLASSO(data, pheno, reg.family, penalty = 0.001)
preprocessLASSO(data, pheno, reg.family, penalty = 0.001)

Arguments

`data`	data
`pheno`	pheno data
`reg.family`	reg.family
`penalty`	penalty Default: FALSE.

Value

list(S, fit, model)

preprocessPCA

Description

preprocessPCA

Usage

preprocessPCA(data, scaleData, cumvar.threshold, verbose)
preprocessPCA(data, scaleData, cumvar.threshold, verbose)

Arguments

`data`	data
`scaleData`	scaled data
`cumvar.threshold`	cumvar.threshold
`verbose`	Indicates verbosing output Default: FALSE.

Value

list(S, indices, model).

preprocessPLS

Description

preprocessPLS

Usage

preprocessPLS(data, pheno, scaleData, cumvar.threshold, out.type)
preprocessPLS(data, pheno, scaleData, cumvar.threshold, out.type)

Arguments

`data`	data
`pheno`	pheno data
`scaleData`	scaleData
`cumvar.threshold`	cumvar.threshold
`out.type`	out.type Default: FALSE.

Value

list(S, Y, model)

preprocessLASSO

Description

preprocessLASSO

Usage

preprocessRidge(data, pheno, reg.family, penalty = 0.001)
preprocessRidge(data, pheno, reg.family, penalty = 0.001)

Arguments

`data`	data
`pheno`	pheno data
`reg.family`	reg.family
`penalty`	penalty Default: FALSE.

Value

list(S, fit, model)

This function performs an access to covariates

Description

An accessor to results

Usage

results(obj)

## S4 method for signature 'rqt'
results(obj)
results(obj)

## S4 method for signature 'rqt'
results(obj)

Arguments

obj

An object of rqt class.

Value

results returns the results

Examples

data <- data.matrix(read.table(system.file("extdata/test.bin1.dat", 
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
res <- geneTest(obj, method="pca", out.type = "D")
results(res)
data <- data.matrix(read.table(system.file("extdata/test.bin1.dat", 
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
res <- geneTest(obj, method="pca", out.type = "D")
results(res)

Importing required packages and functions

Description

Importing required packages and functions

Usage

ridge_se(xs, y, yhat, my_mod, verbose = FALSE)
ridge_se(xs, y, yhat, my_mod, verbose = FALSE)

Arguments

`xs`	Genotype matrix
`y`	Phenotype
`yhat`	Ridge/LASSO regression object
`my_mod`	Ridge/LASSO regression object
`verbose`	Indicates verbosing output, Default: FALSE.

Value

list(vcov, se). vcov: variance-covariance matrix; se: standard deviation

The rqt class constructor

Description

This function generates rqt class objects

Usage

rqt(phenotype = NULL, genotype = NULL, covariates = NULL, results = NULL)
rqt(phenotype = NULL, genotype = NULL, covariates = NULL, results = NULL)

Arguments

`phenotype`	Phenotype (a vector of length `N`, where `N` - number of individuals).
`genotype`	Genotype - an object of class `SummarizedExperiment`. Should contain one assay (matrix, `N` by `M` where `N` - number of individuals, `M` - number of genetic variants).
`covariates`	Covariates, a data frame `N` by `K` where `N` - number of individuals, `K` - number of covariates
`results`	A list of two: test statistics: (`Q1`, `Q2`, `Q3`), p-values: (`p1.Q1`, `p2.Q2`, `p3.Q3`)

Value

Object of class rqt

Examples

data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
print(obj)
data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
package="rqt"), header=TRUE))
pheno <- data[,1]
geno <- data[, 2:dim(data)[2]]
colnames(geno) <- paste(seq(1, dim(geno)[2]))
geno.obj <- SummarizedExperiment(geno)
obj <- rqt(phenotype=pheno, genotype=geno.obj)
print(obj)

The rqt class

Description

This class stores parameters and results of the rtq algorithms

Value

None

Slots

phenotype:: Phenotype (a vector of length N, where N - number of individuals).
genotype:: Genotype - an object of class SummarizedExperiment. Should contain one assay (matrix, N by M where N - number of individuals, M - number of genetic variants).
covariates:: data frame N by K where N - number of individuals, K - number of covariates)
results:: A list of two: test statistics (Q1, Q2, Q3), p-values (p1.Q1, p2.Q2, p3.Q3)

General functions of `rqt` such as accessors and printing.

Description

Common methods for class rqt. This document lists a series of basic methods for the class rqt

Details

Common methods for class rqt

Value

None

Applies linear of logistic regregression to the data.

Description

Applies linear of logistic regregression to the data.

Usage

simple.multvar.reg(null.model, Z, verbose = FALSE)
simple.multvar.reg(null.model, Z, verbose = FALSE)

Arguments

`null.model`	A fitted null model
`Z`	A genotype matrix
`verbose`	Indicates verbosing output. Default: FALSE.

Value

A list of two: "S" - a dataframe with predictors and "fit" - an object returned by "glm" function.

vcov_ridge: returns variance-covariance matrix and standard deviation for ridge/LASSO regression object

Description

vcov_ridge: returns variance-covariance matrix and standard deviation for ridge/LASSO regression object

Usage

vcov_ridge(x, y, rmod, verbose = FALSE)
vcov_ridge(x, y, rmod, verbose = FALSE)

Arguments

`x`	Genotype matrix
`y`	Phenotype
`rmod`	Ridge/LASSO regression object
`verbose`	Indicates verbosing output, Default: FALSE.

Value

list(vcov, se). vcov: variance-covariance matrix; se: standard deviation

Package 'rqt'

Help Index

Applies linear of logistic regregression to the data.

Description

Usage

Arguments

Value

This function performs an access to covariates

Description

Usage

Arguments

Value

Examples

This function performs a gene-level test based on combined effect sizes.

Description

Usage

Arguments

Value

Examples

This function performs a gene-level meta-analysis based on combined effect sizes.

Description

Usage

Arguments

Value

Examples

get.reg.family

Description

Usage

Arguments

Value

This function performs an access to genotype.

Description

Usage

Arguments

Value

Examples

Get a given STT

Description

Usage

Arguments

Value

get.reg.family

Description

Usage

Arguments

Value

This function performs an access to phenotype

Description

Usage

Arguments

Value

Examples

Preprocess input data with Principal Component Analysis method (PCA)

Description

Usage

Arguments

Value

preprocessLASSO

Description

Usage

Arguments

Value

preprocessPCA

Description

Usage

Arguments

Value

preprocessPLS

Description

Usage

Arguments

Value

preprocessLASSO

Description

Usage

Arguments

Value

This function performs an access to covariates

Description

Usage

General functions of `rqt` such as accessors and printing.