Package 'episcan' reference manual

Title:	Scan Pairwise Epistasis
Description:	Searching genomic interactions with linear/logistic regression in a high-dimensional dataset is a time-consuming task. This package provides some efficient ways to scan epistasis in genome-wide interaction studies (GWIS). Both case-control status (binary outcome) and quantitative phenotype (continuous outcome) are supported (the main references: 1. Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. (2011). <doi:10.1038/ejhg.2010.196>. 2. Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. (2011). <doi:10.1093/bioinformatics/btr218>.)
Authors:	Beibei Jiang <[email protected]> and Benno Pütz <[email protected]>
Maintainer:	Beibei Jiang <[email protected]>
License:	GPL (>= 2)
Version:	0.0.1
Built:	2024-12-06 06:48:02 UTC
Source:	CRAN

Check chunk size

Description

Check the chunk size whether it is over the given number of variables(vaiants) in genotype data. If yes, reset the chunk size equal to the number of variables(vaiants).

Usage

checkchunksize(c, m, n = NULL, ...)
checkchunksize(c, m, n = NULL, ...)

Arguments

`c`	an integer indicating the set chunk size.
`m`	an integer indicating the number of variables(vaiants) in `geno1` if there is only one genotype input.
`n`	an integer indicating the number of variables(vaiants) in `geno2` if there are two genotype inputs. The default is NULL.
`...`	not used.

Value

an integer indicating the chunk size

Examples

set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), 
ncol = 10)
geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), 
ncol = 20)

# if chunk size is smaller, there is no problem
chunksize <- 10
checkchunksize(chunksize, ncol(geno1))

# if chunk size is bigger than the number of columns in genotype input, 
# set chunk size equal to the number of columns in genotype input
chuksize <- 12
checkchunksize(chunksize, ncol(geno1))

# if chunk size is bigger than the number of columns of geno1 and geno2, 
# set chunk size equal to the minima nunumber of columns of geno1 and geno2
chunksize <- 50
checkchunksize(chunksize, ncol(geno1), ncol(geno2))
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), 
ncol = 10)
geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), 
ncol = 20)

# if chunk size is smaller, there is no problem
chunksize <- 10
checkchunksize(chunksize, ncol(geno1))

# if chunk size is bigger than the number of columns in genotype input, 
# set chunk size equal to the number of columns in genotype input
chuksize <- 12
checkchunksize(chunksize, ncol(geno1))

# if chunk size is bigger than the number of columns of geno1 and geno2, 
# set chunk size equal to the minima nunumber of columns of geno1 and geno2
chunksize <- 50
checkchunksize(chunksize, ncol(geno1), ncol(geno2))

Parallelized calculation of the difference of correlation coefficients and compute $Z$ test with one genotype input

Description

Calculate the difference of correlation coefficents between cases and controls, conduct $Z$ test for the differences (values) and choose variant pairs with the significance below the given threshold for output.

Usage

epiblaster1geno(geno, pheno, chunk = 1000, zpthres = 1e-05,
  outfile = "NONE", suffix = ".txt", ...)
epiblaster1geno(geno, pheno, chunk = 1000, zpthres = 1e-05,
  outfile = "NONE", suffix = ".txt", ...)

Arguments

`geno`	is the normalized genotype data. It can be a matrix or a dataframe, or a big.matrix object (from bigmemory. The columns contain the information of variables and the rows contain the information of samples.
`pheno`	a vector containing the binary phenotype information (case/control). The values are either 0 (control) or 1 (case).
`chunk`	is the number of variants in each chunk. Default: 1000.
`zpthres`	is the significance threshold to select variant pairs for output. Default is 1e-6.
`outfile`	is the base of out filename. Default: 'NONE'.
`suffix`	is the suffix of out filename. Default: '.txt'.
`...`	not used.

Value

null

Examples

# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
p1 <- c(rep(0, 60), rep(1, 40))

# normalized data
geno1 <- scale(geno1)

# one genotype with case-control phenotype
epiblaster1geno(geno = geno1, 
pheno = p1,
outfile = "episcan_1geno_cc", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_1geno_cc.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)
# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
p1 <- c(rep(0, 60), rep(1, 40))

# normalized data
geno1 <- scale(geno1)

# one genotype with case-control phenotype
epiblaster1geno(geno = geno1, 
pheno = p1,
outfile = "episcan_1geno_cc", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_1geno_cc.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)

Parallelized calculattion of the difference of correlation coefficients and compute Z test with two genotype inputs

Description

Calculate the difference of correlation coeficents between cases and controls, conduct Z test for the differences (values) and choose variant pairs with the significance below the given threshold for output.

Usage

epiblaster2genos(geno1, geno2, pheno, chunk = 1000, zpthres = 1e-05,
  outfile = "NONE", suffix = ".txt", ...)
epiblaster2genos(geno1, geno2, pheno, chunk = 1000, zpthres = 1e-05,
  outfile = "NONE", suffix = ".txt", ...)

Arguments

`geno1`	is the first normalized genotype data. It can be a matrix or a dataframe, or a big.matrix object from bigmemory. The columns contain the information of variables and the rows contain the information of samples.
`geno2`	is the second normalized genotype data. It can be a matrix or a dataframe, or a big.matrix object from bigmemory. The columns contain the information of variables and the rows contain the information of samples.
`pheno`	a vector containing the binary phenotype information (case/control). The values are either 0 (control) or 1 (case).
`chunk`	is the number of variants in each chunk.
`zpthres`	is the significance threshold to select variant pairs for output. Default is 1e-6.
`outfile`	is the prefix of out filename.
`suffix`	is the suffix of out filename.
`...`	not used.

Value

null

Examples

# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), ncol = 20)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), col = paste0("exm", 1:ncol(geno2)))
p1 <- c(rep(0, 60), rep(1, 40))

# normalized data
geno1 <- scale(geno1)
geno2 <- scale(geno2)

# two genotypes with quantitative phenotype
epiblaster2genos(geno1 = geno1, 
geno2 = geno2, 
pheno = p1, outfile = "episcan_2geno_cc", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_2geno_cc.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)
# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), ncol = 20)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), col = paste0("exm", 1:ncol(geno2)))
p1 <- c(rep(0, 60), rep(1, 40))

# normalized data
geno1 <- scale(geno1)
geno2 <- scale(geno2)

# two genotypes with quantitative phenotype
epiblaster2genos(geno1 = geno1, 
geno2 = geno2, 
pheno = p1, outfile = "episcan_2geno_cc", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_2geno_cc.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)

Calculate HSIC values

Description

Calculate HSIC values

Usage

epiHSIC(A = NULL, B = NULL, P = NULL, ...)
epiHSIC(A = NULL, B = NULL, P = NULL, ...)

Arguments

`A`	is one matrix.
`B`	is one matrix.
`P`	is "phenoype", a vector.
`...`	not used.

Value

a matrix

Author(s)

Beibei Jiang [email protected]

Examples

 
# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), ncol = 20)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), col = paste0("exm", 1:ncol(geno2)))

epiHSIC(A = scale(geno1),
B = scale(geno2),
P = rnorm(100))
# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), ncol = 20)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), col = paste0("exm", 1:ncol(geno2)))

epiHSIC(A = scale(geno1),
B = scale(geno2),
P = rnorm(100))

Calculate epistasis using HSIC with one genotype input

Description

Calculate the significance of epistasis according the definition of HSIC, conduct $Z$ test for HSIC values and choose variant pairs with the significance below the given threshold for output.

Usage

epiHSIC1geno(geno = NULL, pheno, chunk = 1000, zpthres = 1e-05,
  outfile = "NONE", suffix = ".txt", ...)
epiHSIC1geno(geno = NULL, pheno, chunk = 1000, zpthres = 1e-05,
  outfile = "NONE", suffix = ".txt", ...)

Arguments

`geno`	is the normalized genotype data. It can be a matrix or a dataframe, or a big.matrix object from bigmemory. The columns contain the information of variables and the rows contain the information of samples.
`pheno`	is a vector containing the normalized phenotype information.
`chunk`	is the number of variants in each chunk.
`zpthres`	is is the significance threshold to select variant pairs for output. Default is 1e-6.
`outfile`	is the basename of out filename.
`suffix`	is the suffix of out filename.
`...`	not used.

Value

null

Author(s)

Beibei Jiang [email protected]

Examples

# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
p2 <- rnorm(100, mean = 5, sd = 10)

# normalized data
geno1 <- scale(geno1)
p2 <- as.vector(unlist(scale(p2)))

# one genotypes with quantitative phenotype
epiHSIC1geno(geno = geno1,
pheno = p2, 
outfile = "episcan_1geno_quant", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_1geno_quant.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)
# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = 10)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
p2 <- rnorm(100, mean = 5, sd = 10)

# normalized data
geno1 <- scale(geno1)
p2 <- as.vector(unlist(scale(p2)))

# one genotypes with quantitative phenotype
epiHSIC1geno(geno = geno1,
pheno = p2, 
outfile = "episcan_1geno_quant", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_1geno_quant.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)

Calculate epistasis using HSIC with two genotype inputs

Description

Calculate the significance of epistasis according the definition of HSIC, conduct Z test for HSIC values and choose variant pairs with the significance below the given threshold for output.

Usage

epiHSIC2genos(geno1 = NULL, geno2 = NULL, pheno = NULL,
  chunk = 1000, zpthres = 1e-05, outfile = "NONE", suffix = ".txt",
  ...)
epiHSIC2genos(geno1 = NULL, geno2 = NULL, pheno = NULL,
  chunk = 1000, zpthres = 1e-05, outfile = "NONE", suffix = ".txt",
  ...)

Arguments

`geno1`	is the first normalized genotype data. It can be a matrix or a dataframe, or a big.matrix object from bigmemory. The columns contain the information of variables and the rows contain the information of samples.
`geno2`	is the second normalized genotype data. It can be a matrix or a dataframe, or a big.matrix object from bigmemory. The columns contain the information of variables and the rows contain the information of samples.
`pheno`	is a vector containing the normalized phenotype information.
`chunk`	is the number of variants in each chunk.
`zpthres`	is the significance threshold for cut-off output of the variant pairs.
`outfile`	is the basename of out filename.
`suffix`	is the suffix of out filename.
`...`	not used

Value

null

Examples

# simulate some data
set.seed(123)
n1 <- 10; n2 <- 15; rows <- 10
geno1 <- matrix(sample(0:2, size = n1*rows, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = n1)
geno2 <- matrix(sample(0:2, size = n2*rows, replace = TRUE, prob = c(0.4, 0.3, 0.3)), ncol = n2)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), col = paste0("exm", 1:ncol(geno2)))
p2 <- rnorm(rows, mean = 5, sd = 10)

# normalized data
geno1 <- scale(geno1)
geno2 <- scale(geno2)
p2 <- as.vector(unlist(scale(p2)))

# two genotypes with quantitative phenotype
epiHSIC2genos(geno1 = geno1, 
geno2 = geno2, 
pheno = p2, 
outfile = "episcan_2geno_quant", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_2geno_quant.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)
# simulate some data
set.seed(123)
n1 <- 10; n2 <- 15; rows <- 10
geno1 <- matrix(sample(0:2, size = n1*rows, replace = TRUE, prob = c(0.5, 0.3, 0.2)), ncol = n1)
geno2 <- matrix(sample(0:2, size = n2*rows, replace = TRUE, prob = c(0.4, 0.3, 0.3)), ncol = n2)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), col = paste0("rs", 1:ncol(geno1)))
dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), col = paste0("exm", 1:ncol(geno2)))
p2 <- rnorm(rows, mean = 5, sd = 10)

# normalized data
geno1 <- scale(geno1)
geno2 <- scale(geno2)
p2 <- as.vector(unlist(scale(p2)))

# two genotypes with quantitative phenotype
epiHSIC2genos(geno1 = geno1, 
geno2 = geno2, 
pheno = p2, 
outfile = "episcan_2geno_quant", 
suffix = ".txt", 
zpthres = 0.9, 
chunk = 10)

# take a look at the result
res <- read.table("episcan_2geno_quant.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)

Scan pairwise epistasis

Description

Genomic interaction analysis with EPIBLASTER or epistasis-oriented Hilbert–Schmidt Independence Criterion (HSIC).

Usage

episcan(geno1, geno2 = NULL, pheno = NULL,
  phetype = c("case-control", "quantitative"), outfile = "episcan",
  suffix = ".txt", zpthres = 1e-06, chunksize = 1000, scale = TRUE,
  ...)
episcan(geno1, geno2 = NULL, pheno = NULL,
  phetype = c("case-control", "quantitative"), outfile = "episcan",
  suffix = ".txt", zpthres = 1e-06, chunksize = 1000, scale = TRUE,
  ...)

Arguments

`geno1`	a data.frame or matrix of the first genotype data. `big.matrix` object from bigmemory also works. The columns contain the information of variables and the rows contain the information of samples.
`geno2`	optional. A data.frame or matrix of the second genotype data. `big.matrix` object from bigmemory also works. The columns contain the information of variables and the rows contain the information of samples.
`pheno`	a vector (named or not). If not provided, the value of `geno2` will be used if it is a vector. The values is either case-control phenotype (0, 1) or quantitative phenotype.
`phetype`	character string. Either "case-control" or "quantitative".
`outfile`	output file name. Default is "episcan".
`suffix`	suffix for output file. Default is ".txt". The final result will be stored in `outfilesuffix`.
`zpthres`	is the significance threshold to select variant pairs for output. Default is 1e-6.
`chunksize`	the number of variants in each chunk.
`scale`	a logical value to define wheter the input data needs to be normalized. Default is TRUE which means, by default, all the genotype data will be normalized and if the phetype is "quantitative", the phenotype will also be normalized.
`...`	not used.

Value

null

Author(s)

Beibei Jiang [email protected]

References

Kam-Thong, T., D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, et al. 2011. "EPIBLASTER-Fast Exhaustive Two-Locus Epistasis Detection Strategy Using Graphical Processing Units." Journal Article. European Journal of Human Genetics 19 (4): 465–71. https://doi.org/10.1038/ejhg.2010.196.

Kam-Thong, T., B. Pütz, N. Karbalai, B. Müller-Myhsok, and K. Borgwardt. 2011. "Epistasis Detection on Quantitative Phenotypes by Exhaustive Enumeration Using GPUs." Journal Article. Bioinformatics 27 (13): i214–21. https://doi.org/10.1093/bioinformatics/btr218.

Examples

# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), 
ncol = 10)
geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), 
ncol = 20)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), 
col = paste0("rs", 1:ncol(geno1)))
dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), 
col = paste0("exm", 1:ncol(geno2)))
p1 <- c(rep(0, 60), rep(1, 40))
p2 <- rnorm(100)

# one genotype with case-control phenotype
episcan(geno1 = geno1, 
geno2 = NULL, 
pheno = p1, 
phetype = "case-control",
outfile = "episcan_1geno_cc", 
suffix = ".txt", 
zpthres = 0.9, 
chunksize = 10, 
scale = TRUE)

# take a look at the result
res <- read.table("episcan_1geno_cc.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)

# two genotypes with quantitative phenotype
episcan(geno1 = geno1, 
geno2 = geno2, 
pheno = p2, 
phetype = "quantitative",
outfile = "episcan_2geno_quant", 
suffix = ".txt", 
zpthres = 0.9, 
chunksize = 10, 
scale = TRUE)
# simulate some data
set.seed(123)
geno1 <- matrix(sample(0:2, size = 1000, replace = TRUE, prob = c(0.5, 0.3, 0.2)), 
ncol = 10)
geno2 <- matrix(sample(0:2, size = 2000, replace = TRUE, prob = c(0.4, 0.3, 0.3)), 
ncol = 20)
dimnames(geno1) <- list(row = paste0("IND", 1:nrow(geno1)), 
col = paste0("rs", 1:ncol(geno1)))
dimnames(geno2) <- list(row = paste0("IND", 1:nrow(geno2)), 
col = paste0("exm", 1:ncol(geno2)))
p1 <- c(rep(0, 60), rep(1, 40))
p2 <- rnorm(100)

# one genotype with case-control phenotype
episcan(geno1 = geno1, 
geno2 = NULL, 
pheno = p1, 
phetype = "case-control",
outfile = "episcan_1geno_cc", 
suffix = ".txt", 
zpthres = 0.9, 
chunksize = 10, 
scale = TRUE)

# take a look at the result
res <- read.table("episcan_1geno_cc.txt", 
header = TRUE, 
stringsAsFactors = FALSE)
head(res)

# two genotypes with quantitative phenotype
episcan(geno1 = geno1, 
geno2 = geno2, 
pheno = p2, 
phetype = "quantitative",
outfile = "episcan_2geno_quant", 
suffix = ".txt", 
zpthres = 0.9, 
chunksize = 10, 
scale = TRUE)

Get correlation matrix

Description

Fast calculation of correlation matrix on CPU (the idea is from WGCNA fast function for pearson correlations)

Usage

getcor(A = NULL, B = NULL, method = "pearson", ...)
getcor(A = NULL, B = NULL, method = "pearson", ...)

Arguments

`A`	is a matrix or data.frame.
`B`	is a matrix or data.frame.
`method`	a character string indicating which correlation coefficient is to be computed. Current version only supports "pearson" correlation.
`...`	not used.

Value

correlation matrix

Author(s)

Beibei Jiang [email protected]

Examples

set.seed(123)
A <- matrix(rnorm(100, mean = 5, sd = 10), ncol = 10)
B <- matrix(rnorm(200, mean = 10, sd = 100), ncol = 20)
C <- getcor(A, B)
set.seed(123)
A <- matrix(rnorm(100, mean = 5, sd = 10), ncol = 10)
B <- matrix(rnorm(200, mean = 10, sd = 100), ncol = 20)
C <- getcor(A, B)

index set for idx-th chunk of size chunk for n elements

Description

For proper use of this function it will return the set of variant indices corresponding to the idx-th chunk of size chunk in n variants, taking care of the case that the last chunk might have less than n elements. If used with an idx-value outside the possible chunks (i.e., negative or larger than ceiling(n/chunk)) an empty vector (numeric(0)) is returned.

Usage

ithChunk(idx, n, chunk = 1000)
ithChunk(idx, n, chunk = 1000)

Arguments

`idx`	chunk index (which chunk, first is 1)
`n`	total number of variants
`chunk`	desired chunksize

Value

index range into variants for chunk idx (see details)

Write out epistasis result (normal matrix)

Description

Write out the result of epistasis analysis. Z score matrix is not a symmetric matrix.

Usage

WriteSnpPairs(Zmatrix, indexArr, outfile = "NONE", ...)
WriteSnpPairs(Zmatrix, indexArr, outfile = "NONE", ...)

Arguments

`Zmatrix`	is the Z score matrix (non-symmetric matrix).
`indexArr`	is the index of Zmarix whose z score is over the given `zpthres`.
`outfile`	is the SNP pairs file for the second stage.
`...`	not used.

Value

null

Author(s)

Beibei Jiang [email protected]

Write out epistasis result (symmetric matrix)

Description

Write out the result of epistasis analysis. Z score matrix is a symmetric matrix.

Usage

WriteSnpPairs_sym(Zmatrix, indexArr, outfile = "NONE", ...)
WriteSnpPairs_sym(Zmatrix, indexArr, outfile = "NONE", ...)

Arguments

`Zmatrix`	is the Z score matrix (symmetric matrix).
`indexArr`	is the index of Zmarix whose z score is over the given `zpthres`.
`outfile`	is the SNP pairs file for the second stage.
`...`	not used.

Value

null

Author(s)

Beibei Jiang [email protected]

Convert Z-score to correponding p-value

Description

Convert Z score to correponding p-values

Usage

ZtoP(z.score, ...)
ZtoP(z.score, ...)

Arguments

`z.score`	Z-score(s) (either scalar or vector).
`...`	not used.

Value

corresponding $p$ -value(s).

Note

Due to the IEEE number limits of representing doubles, any $Z$ score over 37.51929999999999765 will be converted to a $p$ -value of 1e-309.

Author(s)

Beibei Jiang [email protected] and Benno Pütz [email protected]

Package 'episcan'

Help Index

Check chunk size

Description

Usage

Arguments

Value

Examples

Parallelized calculation of the difference of correlation coefficients and compute ZZZ test with one genotype input

Description

Usage

Arguments

Value

Examples

Parallelized calculattion of the difference of correlation coefficients and compute Z test with two genotype inputs

Description

Usage

Arguments

Value

Examples

Calculate HSIC values

Description

Usage

Arguments

Value

Author(s)

Examples

Calculate epistasis using HSIC with one genotype input

Description

Usage

Arguments

Value

Author(s)

Examples

Calculate epistasis using HSIC with two genotype inputs

Description

Usage

Arguments

Value

Examples

Scan pairwise epistasis

Description

Usage

Arguments

Value

Author(s)

References

Examples

Get correlation matrix

Description

Usage

Arguments

Value

Author(s)

Examples

index set for idx-th chunk of size chunk for n elements

Description

Usage

Arguments

Value

Write out epistasis result (normal matrix)

Description

Usage

Arguments

Value

Author(s)

Write out epistasis result (symmetric matrix)

Description

Usage

Arguments

Value

Author(s)

Convert Z-score to correponding p-value

Description

Usage

Arguments

Value

Note

Author(s)

Parallelized calculation of the difference of correlation coefficients and compute $Z$ test with one genotype input