Rcppreqtl Manual

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Package ‘rcppreqtl’
September 12, 2018
Type Package
Title eqtl optimization using 'RcppEigen'
Version 0.99.0
Date 2016-01-19 The 'rcppreqtl' package uses 'RcppEigen' and C++11
numeric solver for eqtl optimization.
Author Vasyl Zhabotynsky [aut, cre], Wei Sun [aut]
Maintainer Vasyl Zhabotynsky 
Description
Analysis of combined total and allele specific reads from the human trio experiment using RNAseq data.
License GPL (>= 2) | file LICENSE
Depends R (>= 2.15.1),MASS,VGAM
LazyLoad yes
LinkingTo RcppEigen, Rcpp
Imports Matrix (>= 1.1-0), RcppEigen (>= 0.3.2.0), Rcpp (>= 0.11.0),
stats, utils
NeedsCompilation yes
Archs i386, x64

R topics documented:
fit . . . . .
fitsh . . . .
makeXmatr
readCounts
simu2 . . .
simu4 . . .

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2
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1

2

fit

fit

Optimization wrapper, maximizing the joint model of total (TReC) and
allele specific (ASE) counts for autosomes

Description
Performs optimization of joint TReC and ASE for autosome and tests with lrt test for two hypotheses hypotheses: additive and parent of origin.
Usage
fit(subset=NULL, data, traceit=FALSE)
Arguments
subset

a subset of entries to be tested, but default is set to NULL which leads to fitting
all the genes in the table

data

an object of class readCounts including read counts and necessary supporting
information

traceit

include more debug output, by default set to FALSE

Value
a list of following matrices:
full

matrix with columns: log(phiNB) and -log(phiBB), coefficients, -log(likelihood)
for the full model fit as well as appended two p-value tests for additive and parent
of origin effect

testadd

matrix with columns: log(phiNB) and -log(phiBB), coefficients, -log(likelihood)
for the restricted to b0=0 model fit

testpoo

matrix with columns: log(phiNB) and -log(phiBB), coefficients, -log(likelihood)
for the restricted to b1=0 model fit

Author(s)
Vasyl Zhabotynsky vasyl@unc.edu
See Also
fitsh, data, simu4, simu2, readCounts.
Examples
## Not run:
# fitting autosome data for a full model with allele-specific counts collected on gene level:
percase = 0.1
dblcnt = 0.2
mn = 100
b0 = 0
b1 = 0
phiNB = .5;
phiBB=phiNB/4

fitsh

3
niter = 10
betas = c(3,.2,.05,.5)
ss=2

dep = makeXmatr(ss)
dat = simu4(num=niter, Xmatr=dep$Xmatr, haplotype=dep$thp, totmean=mn, percase=percase, dblcnt=dblcnt, phiNB=p
fit(subset=NULL, data=dat, traceit=FALSE)
## End(Not run)

fitsh

Optimization wrapper, maximizing the joint model of total (TReC) and
allele specific (ASE) counts for autosomes

Description
Performs optimization of joint TReC and ASE for autosome and tests with lrt test for two hypotheses hypotheses: additive and parent of origin. This function modification assumes that phiNB and
phiBB are common for the gene of interest. Otherwise the model is exactly the same.
Usage
fitsh(subset=NULL, data, traceit=FALSE)
Arguments
subset

a subset of entries to be tested, but default is set to NULL which leads to fitting
all the genes in the table

data

an object of class readCounts including read counts and necessary supporting
information

traceit

include more debug output, by default set to FALSE

Value
a list of following matrices:
full

matrix with columns: log(phiNB) and -log(phiBB), coefficients, -log(likelihood)
for the full model fit as well as appended two p-value tests for additive and parent
of origin effect

testadd

matrix with columns: log(phiNB) and -log(phiBB), coefficients, -log(likelihood)
for the restricted to b0=0 model fit

testpoo

matrix with columns: log(phiNB) and -log(phiBB), coefficients, -log(likelihood)
for the restricted to b1=0 model fit

Author(s)
Vasyl Zhabotynsky vasyl@unc.edu
See Also
fitsh, data, simu2, simu4, readCounts.

4

makeXmatr

Examples
## Not run:
# fitting autosome data for a full model with allele-specific counts collected on gene level:
percase = 0.1
dblcnt = 0.2
mn = 100
b0 = 0
b1 = 0
phiNB = .5;
phiBB=phiNB/4
niter = 10
betas = c(3,.2,.05,.5)
ss=2

dep = makeXmatr(ss)
dat = simu4(num=niter, Xmatr=dep$Xmatr, haplotype=dep$thp, totmean=mn, percase=percase, dblcnt=dblcnt, phiNB=p
fitsh(subset=NULL, data=dat, traceit=FALSE)
## End(Not run)

makeXmatr

Create example design matrix for simulations

Description
Produces a design matrix of several sample sizes which can be used to generate simulated dataset
Usage
makeXmatr(ss)
Arguments
ss

Sample size class: ss=1 implies dample size 32, ss=2 implies sample size 64,
etc

Value
a design matrix of 4 variables
Author(s)
Vasyl Zhabotynsky vasyl@unc.edu
See Also
fitsh, data, simu4, simu2, readCounts.

readCounts

5

Examples
## Not run:
# fitting autosome data for a full model with allele-specific counts collected on gene level:
percase = 0.1
dblcnt = 0.2
mn = 100
b0 = 0
b1 = 0
phiNB = .5;
phiBB=phiNB/4
niter = 100
betas = c(3,.2,.05,.5)
ss=2
dep = makeXmatr(ss)
## End(Not run)

readCounts

A list object that should be used as input to optimization fit or fitsh
function.

Description
It should contain at least total read counts (TReC), overall allele-specific counts (ASE), paternal
allele-specific counts and haplotype classification 0 to 4. Also should include X matrix with covariates such as intercept, library depth, principal components.
Value
haplotype

a matrix defining the haplotype status of the individual for each gene - AB=0,
BA=1, AA=2, BB=3 coded as PaternalMaternal haplotypes. Each row - one
gene

trc

matrix of TReC counts. Each row - one gene

asn

matrix of ASE counts for subject (column) for corresponding genes (row).

asnp

matrix of ASE counts belonging to paternal allele, for correponding subjects and
genes as in asn.

haplotypeA

haplotype modification for a setup with allele-specific reads collected for multiple SNPs in a gene. This block by default is NULL, but can be used by simulation function to study scenarios when reads are collected not on gene level, but
on SNP level

asnA

allele-specific count corresponding to haplotypeA

asnpA

paternal allele-specific count corresponding to haplotypeA

X

design matrix for total read counts - a place to include intercept, library depth,
other covariates such as batch effect

params

if data is produced by simulation function parameter values can be stored here
for further comparisons

settings

other important settings for the simulation such as mean of total read counts,
percentage of allele specific counts, percentage of reads double-counted with
neighboring SNPs can be stored here

6

simu2

Author(s)
Vasyl Zhabotynsky vasyl@unc.edu
See Also
fit, fitsh, simu2, simu4.
Examples

## Not run:
# see total read counts (TReC) for first 2 X chromosome genes of a data example:
rc = readCounts(haplotype=haplotypef, trc=trc, asn=asnf, asnp=asnpf, haplotypeA=haplotyped, asnA=asnm, asnpA=a
X=Xmatr, params=c(phiNB, phiBB, b0, b1, betas), settings=c(totmean, percase, dblcnt))
## End(Not run)

simu2

Simulate a dataset in a format acceptable by a fit function

Description
Creates an object of a class readCounts with data simulated according to a provided setup
Usage

simu2(num, Xmatr, haplotype, totmean, percase=0.1, dblcnt=0, phiNB=1, phiBB=0.5, b0=0, b1=0, betas
fullest = fit(subset=NULL, data=dat, traceit=FALSE)
Arguments
num

number of iterations

Xmatr

design matrix for total read counts covariates

haplotype

classes of haplotypes 0 - AA, 1 - AB, 2 - BA, 3 - BB where first letter represents
paternal allele

totmean

average total gene expression

percase

percentage of reads classified as allele-specific, default value 10%

dblcnt

optional output considering a simulation split into 2 SNPs with double-counting.
Default value 0.

phiNB

over-dispersion for Negative-Binomial distribution, default value 1

phiBB

over-dispersion for Beta-Binomial distribution, default value 0.5

b0

additive eQTL, default value 0

b1

parent of origin effect, default value 0

betas

covariates for design matrix Xmatr

Value
an object of class readCounts
simulated dataset that can be used to fit the model

simu4

7

Author(s)
Vasyl Zhabotynsky vasyl@unc.edu
See Also
fitsh, data, simu4, simu2, readCounts.
Examples
## Not run:
# fitting autosome data for a full model with allele-specific counts collected on gene level:
percase = 0.1
dblcnt = 0.2
mn = 100
b0 = 0
b1 = 0
phiNB = .5;
phiBB=phiNB/4
niter = 100
betas = c(3,.2,.05,.5)
ss=2

dep = makeXmatr(ss)
dat = simu2(num=niter, Xmatr=dep$Xmatr, haplotype=dep$thp, totmean=mn, percase=percase, dblcnt=dblcnt, phiNB=p
## End(Not run)

simu4

Simulate a dataset in a format acceptable by a fit function

Description
Creates an object of a class readCounts with data simulated according to a provided setup
Usage

simu4(num, Xmatr, haplotype, totmean, percase=0.1, dblcnt=0, phiNB=1, phiBB=0.5, b0=0, b1=0, betas
fullest = fit(subset=NULL, data=dat, traceit=FALSE)
Arguments
num

number of iterations

Xmatr

design matrix for total read counts covariates

haplotype

classes of haplotypes 0 - AA, 1 - AB, 2 - BA, 3 - BB where first letter represents
paternal allele

totmean

average total gene expression

percase

percentage of reads classified as allele-specific, default value 10%

dblcnt

optional output considering a simulation split into 4 SNPs with double-counting.
Default value 0.

phiNB

over-dispersion for Negative-Binomial distribution, default value 1

8

simu4
phiBB

over-dispersion for Beta-Binomial distribution, default value 0.5

b0

additive eQTL, default value 0

b1

parent of origin effect, default value 0

betas

covariates for design matrix Xmatr

Value
an object of class readCounts
simulated dataset that can be used to fit the model
Author(s)
Vasyl Zhabotynsky vasyl@unc.edu
See Also
fitsh, data, simu4, simu2, readCounts.
Examples
## Not run:
# fitting autosome data for a full model with allele-specific counts collected on gene level:
percase = 0.1
dblcnt = 0.2
mn = 100
b0 = 0
b1 = 0
phiNB = .5;
phiBB=phiNB/4
niter = 100
betas = c(3,.2,.05,.5)
ss=2

dep = makeXmatr(ss)
dat = simu4(num=niter, Xmatr=dep$Xmatr, haplotype=dep$thp, totmean=mn, percase=percase, dblcnt=dblcnt, phiNB=p
## End(Not run)

Index
∗Topic methods
fit, 2
fitsh, 3
makeXmatr, 4
simu2, 6
simu4, 7
∗Topic utilities
readCounts, 5
data, 2–4, 7, 8
fit, 2, 6
fitsh, 2, 3, 3, 4, 6–8
makeXmatr, 4
readCounts, 2–4, 5, 7, 8
simu2, 2–4, 6, 6, 7, 8
simu4, 2–4, 6, 7, 7, 8

9



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