Package 'polySegratio'

Title: Simulate and Test Marker Dosage for Dominant Markers in Autopolyploids
Description: Perform classic chi-squared tests and Ripol et al(1999) binomial confidence interval approach for autopolyploid dominant markers. Also, dominant markers may be generated for families of offspring where either one or both of the parents possess the marker. Missing values and misclassified markers may be generated at random.
Authors: Peter Baker [aut, cre]
Maintainer: Peter Baker <[email protected]>
License: GPL-3
Version: 0.2-5
Built: 2025-02-20 02:42:59 UTC
Source: https://github.com/petebaker/polysegratio

Help Index

Segregation Ratios for Autoployploids

Description

These functions provide tools for computing expected segregation ratios (or more correctly segregation proportions) for dominant markers in regular autopolyploids and simulating such marker data as well as conducting standard Chi squared tests and Binomial confidance intervals for assigning marker dosage.

Details

Package: polySegratio
Type: Package
Version: 0.2-5
Date: 2018-03-22
License: GPL-3

Use expected.segRatio to compute expected segregation proportions for regular autopolyploids

Use segregationRatios to compute segregation ratios for a matrix of markers

Use test.segRatio to assignmarker dosage via Chi squared tests or Binomial CIs

Use sim.autoMarkers and sim.autoCross to simulate marker data under various scenarios

Use addMisclass and addMissing make some markers misclassified or missing at random

Author(s)

Peter Baker [email protected]

References

  • J B S Haldane (1930) Theoretical genetics of autopolyploids. Journal of genetics 22 359–372

  • Ripol, M I et al(1999) Statistical aspects of genetic mapping in autopolyploids. Gene 235 31–41

Examples

## expected segregation proportions heterogeneous parents
expected.segRatio(4)
expected.segRatio("Tetraploid")
expected.segRatio("Octa")

## expected segregation proportions homogeneous parents
expected.segRatio("Octa",type="heter")

## generate dominant markers for autotetraploids
a1 <- sim.autoMarkers(4,c(0.8,0.2))
print(a1)
plot(a1)

## generate crosses for different parental types
p2 <- sim.autoCross(4, dose.proportion=list(p01=c(0.7,0.3),
                     p10=c(0.7,0.3),p11=c(0.6,0.2,0.2)))
print(p2)
plot(p2)

## simulate and test some markers, printing out a summary table of
## no.s of correct marker dosages 

a <- sim.autoMarkers(ploidy = 8, c(0.7,0.2,0.09,0.01),
                     type="hetero", n.markers=500,n.individuals=100)
a <- addMissing(a, 0.07)  # make seven percent missing at random
at <- test.segRatio(a$seg.ratios, ploidy=8, type.parents="het",
                      method="bin")
print(addmargins(table(a$true.doses$dosage, at$dosage, exclude=NULL)))

Misclassifies marker data in objects of class autoMarker or autoCross

Description

Marker data are misclassified at a specified rate for objects of class simAutoMarkers or simAutoCross. The rate may be specified either as a proportion of missing at random or a proportion of columns and rows with specified proportions of missings.

Usage

addMisclass(x, misclass = 0, bands.missed=0, parents = FALSE,
parent.cols = c(1, 2), seed)

Arguments

x

object of class simAutoMarkers or simAutoCross, or a matrix with dominant markers scored as 0 or 1
misclass

proportion misclassified specified as for na.proportion (Default: 0)
bands.missed

proportion of bands that are not scored when they are actually present. Note this is applied to correctly specified markers after markers are misclassified (Default: 0)
parents

if TRUE then misclassify parental alleles, otherwise misclassify offspring marker alleles
parent.cols

for object of simAutoClass the columns containg parental markers
seed

random number generator (RNG) state for random number which will be set at start to reproduce results

Value

returns object of class simAutoMarkers or simAutoCross, or a matrix with dominant markers scored as 0 or 1 with extra components

misclass.info

list with components

  • proportionnumeric proportion misclassified

  • indexindicates which markers were set as misclassified

  • bands.proportionnumeric proportion marker bands missed

  • bands.indexindicates which markers bands were missed

  • callmatches arguments when function called

  • time.generatedtime/date when misclassifieds added

  • seed seed for random number generation

Author(s)

Peter Baker [email protected]

See Also

addMissing add missing markers at random, sim.autoMarkers simulate autopolyploid markers, sim.autoCross simulate autopolyploid markers for a cross

Examples

## simulate autopolyploid markers
p1 <- sim.autoCross(4, dose.proportion=c(0.7,0.3), n.markers=20, n.indiv=10)
p2 <- sim.autoCross(4, dose.proportion=list(p01=c(0.7,0.3),p10=c(0.7,0.3),p11=c(
0.6,0.2,0.2)))

## add misclassified for a whopping 20% of markers
print(addMisclass(p1, 0.2, parents=TRUE), row=1:20)
addMisclass(p2, 0.1)

Adds missing data to objects of class autoMarker or autoCross

Description

Adds missing data to objects of class simAutoMarkers or simAutoCross as specified either as a proportion of missing at random or a proportion of columns and rows with specified proportions of missings.

Usage

addMissing(x, na.proportion = 0, parent.cols = c(1, 2), seed)

Arguments

x

object of class simAutoMarkers or simAutoCross, or a matrix with dominant markers scored as 0 or 1
na.proportion

proportion missing at random or a list with two components indiv and marker each containing c(prop. markers missing, prop. missing) (Default: 0)
parent.cols

columns containing parental markers (etc) not altered only used if object of class simAutoCross
seed

random number generator (RNG) state for random number which will be set at start to reproduce results

Value

Returns object of class simAutoMarkers or simAutoCross, or a matrix with dominant markers scored as 0 or 1 with extra component na.proportion which has the following elements

na.proportion

proportion missing at random or a list with two components indiv and marker each containing c(prop. markers missing, prop. missing)
time.generated

time/date when data set generated + when missing added
seed

random number generator seed which could be used to reproduce results (I hope)
call

matches arguments when function called

Author(s)

Peter Baker [email protected]

See Also

addMisclass misclassifies markers at random, sim.autoMarkers simulate autopolyploid markers, sim.autoCross simulate autopolyploid markers for a cross

Examples

## simulate autopolyploid markers
p1 <- sim.autoCross(4, dose.proportion=c(0.7,0.3), n.markers=20, n.indiv=10)
p2 <- sim.autoCross(4, dose.proportion=list(p01=c(0.7,0.3),p10=c(0.7,0.3),p11=c(
0.6,0.2,0.2)))

## add missings
addMissing(p2, 0.1)

Automatically fill out blanks of a vector with the preceding label

Description

autoFill is commonly used to generate labels from columns of a spreadsheet when many cells are left blank in order to save a lot of typing. For instance, c("a","","","b","") becomes c("a","a","a","b","b")

Usage

autoFill(x, squash = FALSE)

Arguments

x

a vector of character strings
squash

If set to TRUE then leading and trailing spaces are removed which is useful if spaces are inadvertantly typed because these may be hard to track down. Default: FALSE

Value

x

a vector of character strings with blank strings replaced by preceding non–blank strings

Note

While this function may be called directly, it is more often called by makeLabel

Author(s)

Peter Baker [email protected]

See Also

makeLabel uses autoFill to create labels from two columns of marker names

Examples

## description: fill out blanks of a vector with preceeding label
  label.1 <- c("a","","","b","")
  print(autoFill(label.1))

  label.2 <- c("agc","","","","gct5","","ccc","","")
  print(autoFill(label.2))

Divide markers by parental type

Description

Given markers (or more correctly dominant 1,0) marker data and return list object of containing markers data split according to parental alleles, namely 1,0 for each parent and 1,1 for both parents

Usage

divide.autoMarkers(markers, description = paste("Markers split for",
deparse(substitute(markers))), parent.cols = c(1, 2),
extra.cols = NULL, cols.drop = c(parent.cols, extra.cols))

Arguments

markers

matrix of 1, 0, NA indicating marker alleles where rownames are markernames, column names are progeny names
description

text containing a description for printing
parent.cols

column(s) for parental markers (default: 1,2)
extra.cols

extra column(s) to be subsetted (default: NULL)
cols.drop

columns to be dropped from markers before splitting data which can be set to NULL if no columns are to be dropped (Default: c(parent.cols,extra.cols))

Value

Returns S3 class divideAutoMarkers containing

p10, p01, p11

lists for where the first, second components are heterozygous for parents 1, 2 and both resp. Each list contains

  • descriptiontext containing a description for printing

  • parentlabel for parent

  • markersmarkers for specified parental type (including parents etc)

  • extrasextra columns subsetted (if specified)

  • seg.ratiossegregation ratios as class segRatio

Author(s)

Peter Baker [email protected]

See Also

segRatio, sim.autoCross

Examples

p2 <- sim.autoCross(4,
dose.proportion=list(p01=c(0.7,0.3),p10=c(0.7,0.3),
                     p11=c(0.6,0.2,0.2)))
print(p2)

ss <- divide.autoMarkers(p2$markers)

print(ss)

S3 class divideAutoMarkers

Description

An S3 class which contains marker data and segregation proportions split into three groups corresponding to parents with ‘01’, ‘10’ and ‘11’ markers

Value

p10, p01, p11

lists for where the first, second components are heterozygous for parents 1, 2 and both resp. Each list contains

  • descriptiontext containing a description for printing

  • parentlabel for parent

  • markersmarkers for specified parental type (including parents etc)

  • extrasextra columns subsetted (if specified)

  • seg.ratiossegregation ratios as class segRatio

Author(s)

Peter Baker [email protected]

See Also

segRatio, sim.autoCross

Examples

p2 <- sim.autoCross(4,
dose.proportion=list(p01=c(0.7,0.3),p10=c(0.7,0.3),
                     p11=c(0.6,0.2,0.2)))
print(p2)

ss <- divide.autoMarkers(p2$markers)

print(ss)

Compute theoretical segregation proportions for regular autopolyploids

Description

Expected segregation proportions for various dosages of dominant markers for regular autopolyploids are calculated using the formula of Ripol et al (1999) based on Haldane (1930) for single dose and multiple dose parents cross nulliplex ("homozygous") and an unpublished formula where both parents possess at least single dose markers ("heterogeneous")

Usage

expected.segRatio(ploidy.level = stop("No ploidy level set"),
type.parents = c("heterogeneous", "homozygous"))

Arguments

ploidy.level

the number of homologous chromosomes, either as numeric or as a character string
type.parents

"heterogeneous" if parental markers are 0,1 or "homogeneous" if parental markers are both 1

Value

ratio

vector of proportions for each dosage
ploidy.level

numeric value of ploidy level 2,4,6,8, ...
ploidy.name

name of ploidy

Warning

While results will be returned if the ploidy level is set as an odd number, the formula used are only for even numbers.

Author(s)

Peter Baker [email protected]

References

  • J B S Haldane (1930) Theoretical genetics of autopolyploids. Journal of genetics 22 359–372

  • Ripol, M I et al(1999) Statistical aspects of genetic mapping in autopolyploids. Gene 235 31–41

See Also

segRatio, test.segRatio

Examples

## heterogeneous parents

expected.segRatio(2)
expected.segRatio("Tetraploid")
expected.segRatio("tEtR")
expected.segRatio("octo")
expected.segRatio("Octa")
expected.segRatio(14)
## warning
expected.segRatio(9)

## errors - not run
## expected.segRatio("abcd")
## expected.segRatio(-1)

## homogeneous parents

expected.segRatio("Octa", type.parents="heter")
expected.segRatio("Octa", type.parents="homo")
expected.segRatio("tetra", type.parents="homo")
expected.segRatio(6, type.parents="homo")
expected.segRatio(9, type.parents="homo")

Generate labels from two columns where blanks in first column are replaced by preceding non–blank label

Description

Primarily used to generate marker labels from two columns where the first column is a nucleotide sequence which is mainly blank in that it is the same as the previous one while the second column is increasing numbers (fragment size) for each nucleotide combination

Usage

makeLabel(x, columns = c(1, 2), squash = TRUE, sep = "")

Arguments

x

data frame of markers including labels
columns

the column numbers containing labels (default: c(1,2))
squash

remove trailing/leading blanks in 1st column (default:TRUE)

sep

separator when combining two label columns (default: "")

Value

returns vector of marker names

Author(s)

Peter Baker [email protected]

See Also

autoFill is used to replace blanks in first column

Examples

## imaginary data frame representing ceq marker names read in from
  ## spreadsheet
  x <- data.frame( col1 = c("agc","","","","gct5","","ccc","",""),
                   col2 = c(1,3,4,5,1,2,2,4,6))
  print(x)
  print(makeLabel(x))
  print(cbind(x,lab=makeLabel(x, sep=".")))

Plot segregation ratios for either observed or simulated marker data

Description

Plots an object of S3 class segRatio

Usage

## S3 method for class 'segRatio'
plot(x, main =
deparse(substitute(x)), xlab="", xlab.segRatio = "Segregation ratio",
xlab.nobs = "Number of dominant markers",
xlab.miss = "Number of missing markers per individual",
NCLASS = 100, type = c("seg.ratio", "all","no","missing"), ...)

## S3 method for class 'simAutoMarkers'
plot(x, main = deparse(substitute(x)), xlab = "Segregation ratio",...)

## S3 method for class 'simAutoCross'
plot(x, main = deparse(substitute(x)), xlab = "Segregation ratio",
...)

Arguments

x

An object of class segRatio

xlab

label for x axis: not usually set
main

Title for plot

xlab.segRatio

x–axis label when plotting segregation proportions

xlab.nobs

x axis label when plotting no. of 1's

xlab.miss

x axis label when plotting number of missing individuals per marker
NCLASS

number of classes for histograms (Default: 100)
type

type of plot may be set to

  • seg.ratioHistogram of segregation proportions (Default)

  • noHistogram of the number of 1s

  • missingHistogram of the numbers of missing values per marker

  • allProduce all plots on one page

...

other parameters passed to plot function

Details

By default the histograms are produced of the segregation proportions. Other histograms that may be produced are numbers of observed dominant markers (recorded as a 1) and the number of individuals missing a particular marker.

Value

Used for its side-effects

Author(s)

Peter Baker [email protected]

See Also

segRatio, segregationRatios, sim.autoMarkers, sim.autoCross

Examples

## generate some autooctoploid data
a <- sim.autoMarkers(8,c(0.7,0.2,0.09,0.01))

## print markers and plot segratios
print(a)
plot(a$seg.ratios)  # plot the segregation ratios directly
plot(a)             # plot the simAutoMarkers object

## add some missing values and plot all histograms
plot(addMissing(a,0.2)$seg.ratios, type="all")

Print segregation ratios

Description

Prints an object of S3 class segRatio

Usage

## S3 method for class 'segRatio'
print(x, digits=3, ..., index = c(1:min(10,length(x$r))) )

Arguments

x

object of class segRatio

digits

minimal number of significant digits, see print.default
index

which rows of the marker matrix and segregation proportions to print. (Default: c(1:10))

...

extra parameters passed on to print function

Value

None.

Note

Objects of class segRatio may be produced from a matrix of markers by employing the function segregationRatios

Author(s)

Peter Baker [email protected]

See Also

segRatio, segregationRatios, print,print.default

Examples

## generate autopolyploid markers
 a1 <- sim.autoMarkers(4,c(0.8,0.2),n.markers=20,n.individuals=10)

 print(class(a1$seg.ratios))
 print(a1$seg.ratios)

Print objects of class simAutoMarkers

Description

Prints an object of S3 class simAutoMarkers

Usage

## S3 method for class 'simAutoMarkers'
print(x, ..., row.index = c(1:min(10,
nrow(x$markers))), col.index = c(1:min(10, ncol(x$markers))) )

## S3 method for class 'simAutoCross'
print(x, ..., row.index = c(1:min(10,
nrow(x$markers))), col.index = c(1:min(10, ncol(x$markers))))

## S3 method for class 'divideAutoMarkers'
print(x, ..., row.index = c(1:10),
  col.index = c(1:10), tabulate.extras = FALSE )

Arguments

x

object of class simAutoMarkers

row.index

which rows to print (Default: first 10)
col.index

which columns to print (Default: first 10)
tabulate.extras

If TRUE then cross–tabulate any axtra columns (Default: FALSE)
...

extra options for printing

Value

None.

Note

Objects of class simAutoMarkers may be produced from by employing the function sim.autoMarkers and the same for sim.autoCross and divide.autoMarkers

Author(s)

Peter Baker [email protected]

See Also

segRatio, segregationRatios, sim.autoCross, sim.autoMarkers, divide.autoMarkers, print

Examples

## generate data sets
a1 <- sim.autoMarkers(4,c(0.8,0.2))
a2 <- sim.autoMarkers(8,c(0.7,0.2,0.09,0.01),type="homo",n.markers=20,n.individuals=10)

print(a1)
print(a2)

## datasets from crosses
p1 <- sim.autoCross(4, dose.proportion=c(0.7,0.3), n.markers=20, n.indiv=10)
print(p1)
p2 <- sim.autoCross(4, dose.proportion=list(p01=c(0.7,0.3),p10=c(0.7,0.3),p11=c(0.6,0.2,0.2)))
print(p2)

## divide up data from crosses
ss <- divide.autoMarkers(p2$markers)
print(ss)

Print objects of class testSegRatio

Description

Prints an object of S3 class testSegRatio

Usage

## S3 method for class 'testSegRatio'
print(x, ..., last = 10)

Arguments

x

object of class testSegRatio
last

prints from 1 to last segregation ratio tests (Default: 10)

...

extra printing options

Value

None

Author(s)

Peter Baker [email protected]

See Also

segRatio, segregationRatios, test.segRatio

Examples

## simulated data
  a <- sim.autoMarkers(ploidy = 8, c(0.7,0.2,0.09,0.01))
  ac <- test.segRatio(a$seg.ratios, ploidy=8, method="chi.squared")
  print(ac)

S3 class segRatio

Description

An S3 class which contains the segregation ratios for dominant markers and other information such as the number of dominant markers per individual

Value

r

no. of 1's for each individual
n

total no. of markers present for each individual
seg.ratio

segregation proportion for each individual
n.individuals

total number of individuals

Author(s)

Peter Baker [email protected]

See Also

segregationRatios: computing segregation ratios, testSegRatio: chi squared χ2\chi^2 and tests and Binomial confidence intervals for assigning marker dosage, expected.segRatio: compute expected segregation proportions for various dosages for dominant markers in regular autopolyploids

Compute observed segregation proportions for dominant markers in autopolyploids

Description

Computes segregation ratios for a matrix of markers where the rows are markers and the columns are individuals and the markers are recorded as 0's and 1's

Usage

segregationRatios(x, drop.cols = NULL)

Arguments

x

matrix of 0's, 1,s and NA's representing scores of dominant markers where the rows are markers and the columns are individuals

drop.cols

numeric columns to drop when calculating segregation ratios

Value

Returns an object of class segRatio containing

r

no. of 1's for each individual
n

total no. of markers present for each individual
seg.ratio

segregation proportion for each individual
n.individuals

total number of individuals

Author(s)

Peter Baker [email protected]

See Also

testSegRatio: chi squared χ2\chi^2 and tests and Binomial confidence intervals for assigning marker dosage, expected.segRatio: compute expected segregation proportions for various dosages for dominant markers in regular autopolyploids

Examples

## simulate small autotetraplid data set
a1 <- sim.autoMarkers(4,c(0.8,0.2),n.markers=20,n.individuals=10)
print(a1)

print(segregationRatios(a1$markers))

Simulate dominant markers for an autopolyploid cross for all parental types

Description

Simulates dominant markers from an autopolyploid cross given the ploidy level and/or expected segregation ratios and the proportions in each dosage marker class. This is a wrapper to sim.autoMarkers to generate markers for ‘10’, ‘01’ and ‘11’ parents

Usage

sim.autoCross(ploidy.level, prop.par.type = structure(c(0.4, 0.4, 0.2),
names = c("p10", "p01", "p11")), n.markers = 500, n.individuals = 200,
dose.proportion, true.seg.ratios, no.dosage.classes,
marker.names = paste("M", 1:n.markers, sep = "."),
individual.names = paste("X", 1:n.individuals, sep = "."),
parent.names = c("P.1", "P.2"), seed)

Arguments

ploidy.level

the number of homologous chromosomes, either as numeric (single value) or as a character string containing type tetraploid, hexaploid, octoploid, ...
prop.par.type

the proportion of markers generated from each parental type '10', '01' and '11'. Note that the exact number will be randomly generated from the multinomial distribution (Default: c(0.4,0.4,0.2))
n.markers

number of markers (Default: 500)
n.individuals

number of individuals in the cross (Default: 200)
dose.proportion

the proportion of markers to be simulated in each dosage class. Note that the exact number will be randomly generated from the multinomial distribution NB: If a vector is supplied the dose.proportion is same for each parental type otherwise as list with components ‘p01’, ‘p10’ and ‘p11’
true.seg.ratios

numeric vector containing segregation proportion to be supplied if you wish to overide automatic calculations using ploidy.level
no.dosage.classes

numeric vector containing the number of dosage classes
marker.names

labels for markers (Default: M.1 ... M.n.markers)
individual.names

labels for offspring (Default: ... X.j ... )
parent.names

numeric vector of length 2 containing columns of marker matrix containing parental markers (Default: first 2 columns)

seed

integer used to set seed for random number generator (RNG) which (if set) may be used to reproduce results

Value

Returns an object of class simAutoCross containing

markers

matrix of 0,1 dominant markers with individuals as cols and rows as markers
true.dosage

true doses for each marker
name.true.dose

names of true doses for each marker
p10

object of class simAutoMarkers for parental type ‘10’

p01

object of class simAutoMarkers for parental type ‘01’

p11

object of class simAutoMarkers for parental type ‘11’

ploidy.level

the number of homologous chromosomes as numeric (single value)
prop.par.type

proportion of markers for each parental type ‘p01’, ‘p10’ and ‘p11’
n.markers

number of markers (Default: 500)
n.individuals

number of individuals in the cross (Default: 200)
dose.proportion

proportion in each dose – if numeric vector is the same for ‘p01’, ‘p10’ and ‘p11’ else a list with components ‘p01’, ‘p10’ and ‘p11’
no.dosage.classes

number in each dosage class
no.parType

number in each parental type
time.generated

time/date when data set generated
seed

seed for random number generator seed which could be used to reproduce results (I hope)
call

matches arguments when function called

Note

All parameters except the proportions of marker dosage types can be left at the default. If only one value is set, then individual list components will be assumed to be equal. The marker matrix is prepended with parental marker alleles. An alternative is to simply create each group using sim.automarkers and cbind them.

Author(s)

Peter Baker [email protected]

See Also

simAutoCross,simAutoMarkers, sim.autoMarkers

Examples

p1 <- sim.autoCross(4, dose.proportion=c(0.7,0.3), n.markers=20, n.indiv=10)
print(p1)

p2 <- sim.autoCross(4, dose.proportion=list(p01=c(0.7,0.3),p10=c(0.7,0.3),p11=c(0.6,0.2,0.2)))
print(p2)

Simulates dominant markers from an autopolyploid cross

Description

Dominant markers are simulated from an autopolyploid cross given the ploidy level, expected segregation ratios and the proportions in each dosage marker class. This may be chosen from tetraploid to heccaidecaploid and the segregation ratios may be specified explicitly or generated automatically.

Usage

sim.autoMarkers(ploidy.level, dose.proportion, n.markers = 500,
n.individuals = 200, seg.ratios, no.dosage.classes,
type.parents = c("heterogeneous", "homozygous"),
marker.names = paste("M", 1:n.markers, sep = "."),
individual.names = paste("X", 1:n.individuals, sep = "."),
overdispersion=FALSE, shape1=50, seed)

Arguments

ploidy.level

the number of homologous chromosomes, either as numeric (single value) or as a character string containing type tetraploid, hexaploid, octoploid, ...
dose.proportion

the proportion of markers to be simulated in each dosage class. Note that the exact number will be randomly generated from the multinomial distribution
n.markers

number of markers (Default: 500)
n.individuals

number of individuals in the cross (Default: 200)
seg.ratios

numeric vector containing segregation proportion to be supplied if you wish to override automatic calculations using ploidy.level
no.dosage.classes

only generate markers for the first no.dosage.classes classes (if set)
type.parents

heterogeneous for (1,0) or (0,1) homozygous for (1,1) (default: heterogeneous)
marker.names

labels for markers (Default: M.1 ... M.n.markers)
individual.names

labels for offspring (Default: ... X.j ... )
overdispersion

logical indicating overdispersion (Default: FALSE)
shape1

shape1 parameter(s) for the beta distribution used to generate the Binomial probability p, either of length 1 or no.dosage.classes. Default: 50 which implies very little overdispersion. NB: 'shape2' is calculated from shape 1 and expected segregation ratios
seed

integer used to set seed for random number generator (RNG) which (if set) may be used to reproduce results

Value

Returns an object of class simAutoMarkers containing

markers

matrix of 0,1 dominant markers with individuals as cols and rows as markers
E.segRatio

expected segregation proportions, list with components

  • ratiosegregation proportions,

  • ploidy.levellevel of ploidy 4,6,8, ...

  • ploidy.nametetraploid, ... , unknown

type.parents

heterogeneous for (1,0) or (0,1) homozygous for (1,1)
dose.proportion

proportions of markers set for each dosage class
n.markers

number of markers (Default: 500)
n.individuals

number of individuals in the cross (Default: 200)
true.doses

list containing

  • dosage doses generated for each marker for simulation

  • table.dosagessummary of no.s in each dosage

  • namesnames for each dosage such as SD (single dose), DD (double dose), SDxSD etc

seg.ratios

object of class segRatio containing segregation ratios
time.generated

time/date when data set generated
seed

seed for random number generator seed which could be used to reproduce results (I hope)
overdispersion

either a list with components 'overdispersion': logical for whether overdispersion is set or not and if TRUE then two extra components 'shape1' and 'shape2' contain parameters for the beta distribution employed to generate Binomial probabilities
call

matches arguments when function called

Note

For use in simulation studies, other parameters such as the true dosage of each marker are also returned. Also, if extra binomial variation or overdispersion is requested then a beta-binomial distribution is employed to simulate marker data.Note that as the 'shape1' parameter becomes larger, the resulting marker data are less overdispersed.

Author(s)

Peter Baker [email protected]

See Also

simAutoMarkers, print.simAutoMarkers, plot.simAutoMarkers, segRatio

Examples

## generate autopolyploid markers
a1 <- sim.autoMarkers(4,c(0.8,0.2),n.markers=20,n.individuals=10)
print(a1)

a2 <-
sim.autoMarkers(8,c(0.7,0.2,0.09,0.01),type.parents="homo",n.markers=20,n.individuals=10)
print(a2)

S3 class simAutoCross

Description

An S3 class which contains simulated dominant marker data for autopolyploids and other data of interest such as segregation proportions as well as parameters set for the generating given parents with ‘01’, ‘10’ and ‘11’ markers

Value

markers

matrix of 0,1 dominant markers with individuals as cols and rows as markers
true.dosage

true doses for each marker
name.true.dose

names of true doses for each marker
p10

object of class simAutoMarkers for parental type ‘10’

p01

object of class simAutoMarkers for parental type ‘01’

p11

object of class simAutoMarkers for parental type ‘11’

ploidy.level

the number of homologous chromosomes as numeric (single value)
prop.par.type

proportion of markers for each parental type ‘p01’, ‘p10’ and ‘p11’
n.markers

number of markers (Default: 500)
n.individuals

number of individuals in the cross (Default: 200)
dose.proportion

proportion in each dose – if numeric vector is the same for ‘p01’, ‘p10’ and ‘p11’ else a list with components sQuotep01, ‘p10’ and ‘p11’
no.dosage.classes

number in each dosage class
no.parType

number in each parental type
time.generated

time/date when data set generated
seed

seed for random number generator seed which could be used to reproduce results (I hope)
call

matches arguments when function called

Author(s)

Peter Baker [email protected]

See Also

sim.autoCross,simAutoMarkers, sim.autoMarkers

S3 class simAutoMarkers

Description

An S3 class which contains the simulated dominant marker data for autopolyploids and other data of interest such as segregation proportions as well as parameters set for the generating

Value

markers

matrix of 0,1 dominant markers with individuals as cols and rows as markers
E.segRatio

expected segregation porportions, list with components ratio: segregation proportions, ploidy.level: level of ploidy 4,6,8, ..., ploidy.name: tetraploid, ... , unknown

ploidy.level

the number of homologous chromosomes, either as numeric (single value) or as a character string containing type tetraploid, hexaploid, octoploid, ...
n.markers

number of markers (Default: 500)
n.individuals

number of individuals in the cross (Default: 200)
dose.proportion

the proportion of markers to be simulated in each dosage class. Note that the exact number will be randomly generated from the multinomial distribution
true.doses

list containing

  • dosagedoses generated for each marker for simulation

  • table.dosagessummary of no.s in each dosage

  • namesnames for each dosage such as (SD) single dose, (DD) double dose, SDxSD etc

seg.ratios

segregation proportions as class segRatio
time.generated

date and time data set generated
call

function call used to generate data set

Author(s)

Peter Baker [email protected]

See Also

expected.segRatio, segRatio, print.simAutoMarkers, plot.simAutoMarkers

Classic tests for assessing marker dosage in autopolyploids

Description

Perform chi–squared tests or binomial CIs to obtain expected marker dosage in autopolyploids

Usage

test.segRatio(seg.ratio, ploidy.level = 4,
  type.parents = c("heterogeneous", "homozygous"),
  method = c("chi.squared", "binomial"), alpha = 0.05, expected.ratio)

Arguments

seg.ratio

object of class segRatio containing segregation proportions
ploidy.level

the number of homologous chromosomes, either as numeric or as a character string
type.parents

"heterogeneous" if parental markers are 0,1 or "homozygous" if parental markers are both 1
method

specify which method ‘chi.squared’ or ‘binomial’

alpha

significance level for tests/CIs
expected.ratio

vector of expected segregation proportions Default: determined by using function expected.segRatio given the ploidy.level

Value

Returns object of class testSegRatio with components

probability

matrix of probabilities under the test for each dosage where columns are doses and rows are markers
dosage

vector of allocated dosages where allocation unique otherwise NA
allocated

matrix of 0's and 1's where 1 indicates dosage allocation where columns are doses and rows are markers
alpha

alpha level for significance test or CI construction
expected.ratios

expected segregation ratios under null hypotheses
call

call to test.segRatio

Author(s)

Peter Baker [email protected]

References

  • K Mather(1951) The measurement of linkage in heredity. Methuen London

  • Ripol, M I et al(1999) Statistical aspects of genetic mapping in autopolyploids. Gene 235 31–41

See Also

segregationRatios for computing segregation ratios and segRatio, expected.segRatio

Examples

## simulated data
  a <- sim.autoMarkers(ploidy = 8, c(0.7,0.2,0.09,0.01))
  print(a)

  ## summarise chi-squared test vs true
  ac <- test.segRatio(a$seg.ratios, ploidy=8, method="chi.squared")
  print(addmargins(table(a$true.doses$dosage, ac$dosage, exclude=NULL)))

  ## summarise binomial CI vs true
  ab <- test.segRatio(a$seg.ratios, ploidy=8, method="bin")
  print(addmargins(table(a$true.doses$dosage, ab$dosage, exclude=NULL)))

S3 class testSegRatio

Description

An S3 class which contains results of classic tests for assessing marker dosage in autopolyploids using chi-squared tests or binomial confidence intervals

Value

Returns object of class testSegRatio with components

probability

matrix of probabilities under the test for each dosage where columns are doses and rows are markers
dosage

vector of allocated dosages where allocation unique otherwise NA
allocated

matrix of 0's and 1's where 1 indicates dosage allocation where columns are doses and rows are markers
alpha

alpha level for significance test or CI construction
expected.ratios

expected segregation ratios under null hypotheses
call

call to test.segRatio

Author(s)

Peter Baker [email protected]

References

  • K Mather(1951) The measurement of linkage in heredity. Methuen London

  • Ripol, M I et al(1999) Statistical aspects of genetic mapping in autopolyploids. Gene 235 31–41

See Also

segRatio, expected.segRatio, test.segRatio