Package 'isoRelate' reference manual

Title:	Identity-by-Descent Inference of Haploid Recombining Organisms
Description:	Pairwise identity by descent inference of haploid species using single nucleotide polymorphism data. isoRelate can detect IBD in the presence of multi-clonal infections and also provides a function for identifying loci under recent positive selection.
Authors:	Lyndal Henden [aut, cre]
Maintainer:	Lyndal Henden <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2024-06-27 04:17:07 UTC
Source:	https://github.com/bahlolab/isoRelate

The matrix A in the equation Ax=b for 2 diploid chromosomes

Description

The matrix A in the equation Ax=b for 2 diploid chromosomes

Usage

AmatrixDD(pop_allele_freqs, genotypes)
AmatrixDD(pop_allele_freqs, genotypes)

Arguments

`pop_allele_freqs`	A numeric vector of population allele frequencies for each SNP
`genotypes`	An integer matrix of genotype calls for a pair of isolates. Each coloumn represents and isolate and each row represents a SNP.

The matrix A in the equation Ax=b for 1 haploid and 1 diploid chromosome

Description

The matrix A in the equation Ax=b for 1 haploid and 1 diploid chromosome

Usage

AmatrixHD(pop_allele_freqs, genotypes)
AmatrixHD(pop_allele_freqs, genotypes)

Arguments

`pop_allele_freqs`	A numeric vector of population allele frequencies for each SNP
`genotypes`	An integer matrix of genotype calls for a pair of isolates. Each coloumn represents and isolate and each row represents a SNP.

The matrix A in the equation Ax=b for 2 haploid chromosomes

Description

The matrix A in the equation Ax=b for 2 haploid chromosomes

Usage

AmatrixHH(pop_allele_freqs, genotypes)
AmatrixHH(pop_allele_freqs, genotypes)

Arguments

`pop_allele_freqs`	A numeric vector of population allele frequencies for each SNP
`genotypes`	An integer matrix of genotype calls for a pair of isolates. Each coloumn represents and isolate and each row represents a SNP.

Plasmodium Falciparum Gene Annotation Dataset

Description

Gene annotations for the reference genome 3D7 were downloaded from http://www.plasmodb.org/common/downloads/Current_Release/Pfalciparum3D7/gff/data/, release PlasmoDB-28, last modified 23/03/2016.

Usage

annotation_genes
annotation_genes

Format

A data frame with 6 columns of information

chr: Chromosomes
start: Base-pair positions of start of genes
end: Base-pair positions of end of genes
strand: The positive or negaitve gene strand
name: Gene name, commonly abbreviated
gene_id: Gene id

Internal function

Description

areColors() checks if colour names are valid

Usage

areColors(x)
areColors(x)

Arguments

`x`	vector of length 1 or higher containing numeric or character values for colours

The vector b in the equation Ax=b for 2 diploid chromosomes

Description

The vector b in the equation Ax=b for 2 diploid chromosomes

Usage

bVectorDD(genotypes)
bVectorDD(genotypes)

Arguments

genotypes

An integer matrix of genotype calls for a pair of isolates. Each coloumn represents and isolate and each row represents a SNP.

The vector b in the equation Ax=b for 1 haploid chromosome and 1 diploid chromosome

Description

The vector b in the equation Ax=b for 1 haploid chromosome and 1 diploid chromosome

Usage

bVectorHD(genotypes)
bVectorHD(genotypes)

Arguments

genotypes

An integer matrix of genotype calls for a pair of isolates. Each coloumn represents and isolate and each row represents a SNP.

The vector b in the equation Ax=b for 2 haploid chromosomes

Description

The vector b in the equation Ax=b for 2 haploid chromosomes

Usage

bVectorHH(genotypes)
bVectorHH(genotypes)

Arguments

genotypes

An integer matrix of genotype calls for a pair of isolates. Each coloumn represents and isolate and each row represents a SNP.

Calculate alpha

Description

Calculate alpha

Usage

calculateAlpha(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, error, gender_1, gender_2)
calculateAlpha(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, error, gender_1, gender_2)

Arguments

`number_states`	Integer. The number of IBD states in the model
`initial_prob`	A numeric vector containing the initial state probabilities
`meiosis`	Integer. The number of meiosis separating the two isolates
`number_snps`	Integer. The number of SNPs
`genotypes`	A integer martix containing the genotype calls for a pair of isolates
`pop_allele_freqs`	A numeric vector of population allele frequencies
`positions_cM`	A numeric vector of SNP genetic map positions in cM
`error`	Numeric. The genotype error rate
`gender_1`	Integer. The MOI estimate of isolate 1
`gender_2`	Integer. The MOI estimate of isolate 2

Calculate beta

Description

Calculate beta

Usage

calculateBeta(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, scale, error, gender_1, gender_2)
calculateBeta(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, scale, error, gender_1, gender_2)

Arguments

`number_states`	Integer. The number of IBD states in the model
`initial_prob`	A numeric vector containing the initial state probabilities
`meiosis`	Integer. The number of meiosis separating the two isolates
`number_snps`	Integer. The number of SNPs
`genotypes`	A integer martix containing the genotype calls for a pair of isolates
`pop_allele_freqs`	A numeric vector of population allele frequencies
`positions_cM`	A numeric vector of SNP genetic map positions in cM
`scale`	A numeric vector containing the scaling values used to scale alpha
`error`	Numeric. The genotype error rate
`gender_1`	Integer. The MOI estimate of isolate 1
`gender_2`	Integer. The MOI estimate of isolate 2

Calculate gamma

Description

Calculate gamma

Usage

calculateGamma(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, error, gender_1, gender_2)
calculateGamma(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, error, gender_1, gender_2)

Arguments

`number_states`	Integer. The number of IBD states in the model
`initial_prob`	A numeric vector containing the initial state probabilities
`meiosis`	Integer. The number of meiosis separating the two isolates
`number_snps`	Integer. The number of SNPs
`genotypes`	A integer martix containing the genotype calls for a pair of isolates
`pop_allele_freqs`	A numeric vector of population allele frequencies
`positions_cM`	A numeric vector of SNP genetic map positions in cM
`error`	Numeric. The genotype error rate
`gender_1`	Integer. The MOI estimate of isolate 1
`gender_2`	Integer. The MOI estimate of isolate 2

Calculate the log-likelihood of the data

Description

Calculate the log-likelihood of the data

Usage

calculateLogLikelihood(number_states, initial_prob, meiosis, number_snps,
  genotypes, pop_allele_freqs, positions_cM, error, gender_1, gender_2)
calculateLogLikelihood(number_states, initial_prob, meiosis, number_snps,
  genotypes, pop_allele_freqs, positions_cM, error, gender_1, gender_2)

Arguments

`number_states`	Integer. The number of IBD states in the model
`initial_prob`	A numeric vector containing the initial state probabilities
`meiosis`	Integer. The number of meiosis separating the two isolates
`number_snps`	Integer. The number of SNPs
`genotypes`	A integer martix containing the genotype calls for a pair of isolates
`pop_allele_freqs`	A numeric vector of population allele frequencies
`positions_cM`	A numeric vector of SNP genetic map positions in cM
`error`	Numeric. The genotype error rate
`gender_1`	Integer. The MOI estimate of isolate 1
`gender_2`	Integer. The MOI estimate of isolate 2

Estimation of Meiosis

Description

calculateMeiosis() estimates the number of meiosis separating a pair of isolates given the global IBD pop_allele_freqs estimates. This method is described in Purcell et al (2007).

Usage

calculateMeiosis(omega.0, omega.1, omega.2)
calculateMeiosis(omega.0, omega.1, omega.2)

Arguments

`omega.0`	A numeric value between 0 and 1 representing the pop_allele_freqs of sharing 0 alleles IBD. The sum of omega.0, omega.1 and omega.2 should equal 1.
`omega.1`	A numeric value between 0 and 1 representing the pop_allele_freqs of sharing 1 alleles IBD.
`omega.2`	A numeric value between 0 and 1 representing the pop_allele_freqs of sharing 2 alleles IBD.

Value

The number of meiosis separating the pair of isoaltes.

Calculate Missingness Proportions

Description

Calculates the proportion of missing data for each SNPs or each isolate where missing values are denoted by -1. Missing values are calculated for each column of genotypes (where columns are isolates and rows are SNPs), however genotypes can be transposed to calculate missingness proportions for SNPs.

Usage

calculateMissingness(genotypes)
calculateMissingness(genotypes)

Arguments

genotypes

An integer matrix of genotype data of the form -1, 0, 1 and 2 representing missing genotypes, homozygous reference, heterozygous and homozygous alternative respectively.

Value

A vector of length n where n is the number of columns in genotypes.

Calculate Allele Frequencies for SNPs from Genotype Data

Description

calculatePopAlleleFreq calculates reference allele frequencies for each SNP given genotype data.

Usage

calculatePopAlleleFreq(genotypes, moi)
calculatePopAlleleFreq(genotypes, moi)

Arguments

`genotypes`	An integer matrix of genotype data of the form -1, 0, 1 and 2 representing missing genotypes, homozygous reference, heterozygous and homozygous alternative respectively. Each column of `genotypes` represents a unique isolate and each row of `genotypes` represents a unique SNP.
`moi`	An integer vector of multiplicity of infection (MOI) estimates for each isoalte. Isolate MOI estimates should be ordered such that value `n` of `moi` corresponds to column `n` of `genotypes`.

Calculate scale

Description

Calculate scale

Usage

calculateScale(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, error, gender_1, gender_2)
calculateScale(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, error, gender_1, gender_2)

Arguments

`number_states`	Integer. The number of IBD states in the model
`initial_prob`	A numeric vector containing the initial state probabilities
`meiosis`	Integer. The number of meiosis separating the two isolates
`number_snps`	Integer. The number of SNPs
`genotypes`	A integer martix containing the genotype calls for a pair of isolates
`pop_allele_freqs`	A numeric vector of population allele frequencies
`positions_cM`	A numeric vector of SNP genetic map positions in cM
`error`	Numeric. The genotype error rate
`gender_1`	Integer. The MOI estimate of isolate 1
`gender_2`	Integer. The MOI estimate of isolate 2

Calculate the Viterbi sequence

Description

Calculate the Viterbi sequence

Usage

calculateViterbi(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, error, gender_1, gender_2)
calculateViterbi(number_states, initial_prob, meiosis, number_snps, genotypes,
  pop_allele_freqs, positions_cM, error, gender_1, gender_2)

Arguments

`number_states`	Integer. The number of IBD states in the model
`initial_prob`	A numeric vector containing the initial state probabilities
`meiosis`	Integer. The number of meiosis separating the two isolates
`number_snps`	Integer. The number of SNPs
`genotypes`	A integer martix containing the genotype calls for a pair of isolates
`pop_allele_freqs`	A numeric vector of population allele frequencies
`positions_cM`	A numeric vector of SNP genetic map positions in cM
`error`	Numeric. The genotype error rate
`gender_1`	Integer. The MOI estimate of isolate 1
`gender_2`	Integer. The MOI estimate of isolate 2

A function that prints summary information of clusters

Description

A function that prints summary information of clusters

Usage

clusterSummary(cluster.list)
clusterSummary(cluster.list)

Arguments

cluster.list

a list of isolate groups where unique elements correspond to isolates in unique clusters

The emission probabilities for 2 diploid chromosomes

Description

The emission probabilities for 2 diploid chromosomes

Usage

emissionProbDD(pop_allele_freq, genotype_1, genotype_2, ibd)
emissionProbDD(pop_allele_freq, genotype_1, genotype_2, ibd)

Arguments

`pop_allele_freq`	The population allele frequency for SNP i. This corresponds to the reference allele
`genotype_1`	The genotype for isolate 1 from the pair for SNP i
`genotype_2`	The genotype for isolate 2 from the pair for SNP i
`ibd`	The IBD state

The emission probabilities for 1 haploid chromosome and 1 diploid chromosome

Description

The emission probabilities for 1 haploid chromosome and 1 diploid chromosome

Usage

emissionProbHD(pop_allele_freq, genotype_1, genotype_2, ibd, male_column,
  female_column)
emissionProbHD(pop_allele_freq, genotype_1, genotype_2, ibd, male_column,
  female_column)

Arguments

`pop_allele_freq`	The population allele frequency for SNP i. This corresponds to the reference allele
`genotype_1`	The genotype for isolate 1 from the pair for SNP i
`genotype_2`	The genotype for isolate 2 from the pair for SNP i
`ibd`	The IBD state
`male_column`	The haploid isolate from the pair. Either 1 or 2
`female_column`	The diploid isolate from the pair. Either 1 or 2

The emission probabilities for 2 haploid chromosomes

Description

The emission probabilities for 2 haploid chromosomes

Usage

emissionProbHH(pop_allele_freq, genotype_1, genotype_2, ibd)
emissionProbHH(pop_allele_freq, genotype_1, genotype_2, ibd)

Arguments

`pop_allele_freq`	The population allele frequency for SNP i. This corresponds to the reference allele
`genotype_1`	The genotype for isolate 1 from the pair for SNP i
`genotype_2`	The genotype for isolate 2 from the pair for SNP i
`ibd`	The IBD state

Calculating the emission probability sumation when missing genotype calls present

Description

Calculating the emission probability sumation when missing genotype calls present

Usage

emissionProbMissingGeno(pop_allele_freq, genotype_1, genotype_2, error,
  gender_1, gender_2, ibd_j)
emissionProbMissingGeno(pop_allele_freq, genotype_1, genotype_2, error,
  gender_1, gender_2, ibd_j)

Arguments

`pop_allele_freq`	The population allele frequency of SNP t
`genotype_1`	The genotype of isolate 1 at SNP t
`genotype_2`	The genotype of isolate 2 at SNP t
`error`	The genotype error rate
`gender_1`	The MOI estimate of isolate 1
`gender_2`	The MOI estimate of isolate 2
`ibd_j`	The IBD state

The genotyping error probability for 1 diploid chromosome

Description

The genotyping error probability for 1 diploid chromosome

Usage

genotypeErrorD(truth, observed, error)
genotypeErrorD(truth, observed, error)

Arguments

`truth`	The true genotype
`observed`	The observed genotype
`error`	The genotype error rate

The genotyping error probability for 1 haploid chromosome

Description

The genotyping error probability for 1 haploid chromosome

Usage

genotypeErrorH(truth, observed, error)
genotypeErrorH(truth, observed, error)

Arguments

`truth`	The true genotype
`observed`	The observed genotype
`error`	The genotype error rate

IsoRelate Colour Palette for groups

Description

getColourPaletteMajor() generates a spectrum colour palette with a specified number of colours.

Usage

getColourPaletteMajor(number.colours)
getColourPaletteMajor(number.colours)

Arguments

number.colours

numeric. The number of colours to return.

Value

A character vector of length=number.colours containing a colour specturm.

`getColourPaletteMinor()` generates a specified number of shades of a given colour.

Description

getColourPaletteMinor() generates a specified number of shades of a given colour.

Usage

getColourPaletteMinor(major.colour, number.colours)
getColourPaletteMinor(major.colour, number.colours)

Arguments

`major.colour`	character. The colour name or code.
`number.colours`	numeric. The number of colours to return.

Value

A character vector of length=number.colours containing colour shades, excluding white.

Pre-Analysis Data Processing

Description

getGenotypes() performs pre-analysis data processing of PLINK formatted unphased genotype data, including removal of SNPs and isolates with high proportions of missing data and SNPs with low minor allele frequencies. It also calculates SNP allele frequencies from either the input dataset or a specified reference dataset.

Usage

getGenotypes(ped.map, reference.ped.map = NULL, maf = 0.01,
  isolate.max.missing = 0.1, snp.max.missing = 0.1, chromosomes = NULL,
  input.map.distance = "cM", reference.map.distance = "cM")
getGenotypes(ped.map, reference.ped.map = NULL, maf = 0.01,
  isolate.max.missing = 0.1, snp.max.missing = 0.1, chromosomes = NULL,
  input.map.distance = "cM", reference.map.distance = "cM")

Arguments

`ped.map`	A list with 2 objects: A data frame which contains the PLINK PED information. The first six columns of this data frame are: Family ID (type `"character"`, `"numeric"` or `"integer"`) Isolate ID (type `"character"`, `"numeric"` or `"integer"`) Paternal ID (type `"character"`, `"numeric"` or `"integer"`) Maternal ID (type `"character"`, `"numeric"` or `"integer"`) Multiplicity of infection (MOI) (1 = single infection or haploid, 2 = multiple infections or diploid) Phenotype (type `"character"`, `"numeric"` or `"integer"`) where each row describes a single isolate. The IDs are alphanumeric: the combination of family and isolate ID should uniquely identify a sample. The paternal, maternal and phenotype columns are not used by isoRelate, however they are required for completeness of a standard pedigree and are typically filled with the numeric value zero. Columns 7 onwards are the isolate genotypes where the A and B alleles are coded as 1 and 2 respectively and missing data is coded as 0. All SNPs must have two alleles specified and each allele should be in a separate column. For example, the alleles in columns 7 and 8 correspond to the unphased genotypes of SNP 1 in the map file. For single infections, genotypes should be specified as homozygous. Either both alleles should be missing (i.e. 0) or neither. Column names are not required. A data frame which contains the PLINK MAP information. This data frame contains exactly four columns of information: Chromosome (type `"character"`, `"numeric"` or `"integer"`) SNP identifier (type `"character"`) Genetic map distance (centi morgans, cM, or morgans, M) (type `"numeric"`) Base-pair position (type `"numeric"` or `"integer"`) where each row describes a single SNP Genetic map distance and base-pair positions are expected to be positive values. The MAP file must be ordered by increasing genetic map distance. SNP identifiers can contain any characters expect spaces or tabs; also you should avoid * symbols in the names. The MAP file must contain as many markers as are in the PED file. Column names are not required.
`reference.ped.map`	An optional list containing reference data used to calculate SNP allele frequencies. The list has 2 objects in the same format as `ped.map`. The default value is `reference.ped.map=NULL` and isoRelate will calculate the SNP allele frequencies from the input data. This is not recommended for small datasets or datasets of mixed populations.
`maf`	A numeric value denoting the smallest minor allele frequency allowed in the analysis. The default value is 0.01.
`isolate.max.missing`	A numeric value denoting the maximum proportion of missing data allowed for each isolate. The default value is 0.1.
`snp.max.missing`	A numeric value denoting the maximum proportion of missing data allowed for each SNP. The default value is 0.1.
`chromosomes`	A vector containing a subset of chromosomes to perform formatting on. The default value is `chromosomes=NULL` which will reformat all genotypes for all chromosomes in the MAP data frame.
`input.map.distance`	A character string of either "M" or "cM" denoting whether the genetic map distances in the input MAP data frame are in Morgans (M) or centi-Morgans (cM). The default is cM.
`reference.map.distance`	A character string of either "M" or "cM" denoting whether the genetic map distances in the reference MAP data frame are in Morgans (M) or centi-Morgans (cM). The default is cM.

Value

A list of two objects named pedigree and genotypes:

A pedigree containing the isolates that remain after filtering. The pedigree is the first six columns of the PED file and these columns are headed fid, iid, pid, mid, moi and aff respectively.
A data frame with the first five columns:
1. Chromosome (type "character", "numeric" or "integer")
2. SNP identifiers (type "character")
3. Genetic map distance (Morgans, M) (type "numeric")
4. Base-pair position (type "integer")
5. Population allele frequency (type "integer")
where each row describes a single SNP. These columns are headed chr, snp_id, pos_M, pos_bp and freq respectively. Columns 6 onwards contain the genotype data for each isolate, where a single column corresponds to a single isolate. These columns are labeled with merged family IDs and isolate IDs separated by a slash symbol (/).

Examples

# take a look at the data
str(png_pedmap)

# reformat and filter to call genotypes
my_genotypes <- getGenotypes(ped.map = png_pedmap,
                             reference.ped.map = NULL,
                             maf = 0.01,
                             isolate.max.missing = 0.1,
                             snp.max.missing = 0.1,
                             chromosomes = NULL,
                             input.map.distance = "cM",
                             reference.map.distance = "cM")
# take a look at the data
str(png_pedmap)

# reformat and filter to call genotypes
my_genotypes <- getGenotypes(ped.map = png_pedmap,
                             reference.ped.map = NULL,
                             maf = 0.01,
                             isolate.max.missing = 0.1,
                             snp.max.missing = 0.1,
                             chromosomes = NULL,
                             input.map.distance = "cM",
                             reference.map.distance = "cM")

Interval Cluster Networks

Description

getIBDiclusters() produces a network of clusters of isolates that have been inferred IBD over a specified interval. Isolates that are not IBD over the interval are not included in the network or output. The networks are created using the R package igraph.

Usage

getIBDiclusters(ped.genotypes, ibd.segments, interval = NULL, prop = 0,
  hi.clust = FALSE)
getIBDiclusters(ped.genotypes, ibd.segments, interval = NULL, prop = 0,
  hi.clust = FALSE)

Arguments

`ped.genotypes`	A list containing 2 objects. See the `Value` description in `getGenotypes` for more details on this input.
`ibd.segments`	A data frame containing the IBD segments detected by isoRelate. See the `Value` description in `getIBDsegments` for more details on this input.
`interval`	A vector of length 3 containing the region to identify clusters over. This vector should contain the chromosome ID, the start of the interval in base-pairs and the end of the interval in base-pairs; in this order respectively.
`prop`	Numeric value between 0 and 1 (inclusive). The minimum proportion of an interval (in base-pairs) shared IBD between a pair of isolates in order for the pair to be included in the network. For example, if `prop=1` then two isolates will be included if they are IBD over the entire interval, whereas `prop=0.5` will include isolates that are IBD over at least 50% of the interval. The default is `prop=0`, which includes isolates if they have an IBD segment that overlaps the interval in any way.
`hi.clust`	Logical. Whether to perform hierarchical clustering using the `fastgreedy.community` approach in the `igraph` package.

Value

A list of three objects named clusters, i.network and hi.clust:

A list where each object contains the names of isolates that form a disjoint cluster in the network. If hierarchical clustering has been performed then the clusters may not be disjoint.
An igraph network used in the construction of network plots. See http://igraph.org/r/ doe more details.
Logical. Whether or not hierarchical clustering has been performed.

Examples

# generate the isolates who are IBD over the Plasmodium falciparum CRT gene
my_i_clusters <- getIBDiclusters(ped.genotypes = png_genotypes,
                                 ibd.segments = png_ibd,
                                 interval = c("Pf3D7_07_v3", 403222, 406317),
                                 prop=0,
                                 hi.clust = FALSE)

str(my_i_clusters)
# generate the isolates who are IBD over the Plasmodium falciparum CRT gene
my_i_clusters <- getIBDiclusters(ped.genotypes = png_genotypes,
                                 ibd.segments = png_ibd,
                                 interval = c("Pf3D7_07_v3", 403222, 406317),
                                 prop=0,
                                 hi.clust = FALSE)

str(my_i_clusters)

Selection Significance Statistic

Description

getIBDiR() calculates a summary statistic for each SNP that can be used to assess the significance of excess IBD sharing at genomic loci, thus identifying regions under positive selection. First relatedness between isolates and SNP allele frequencies are accounted for, then normalization procedures are applied where we assume our transformed summary statistic follows a chi-squared distribution with 1 degree of freedom. This allows the calculation of -log10 (P-values) which we denote as the iR statistic. SNPs with iR values greater than some threshold (i.e. -log10 (P-values) > -log10 (0.05)) provide evidence of positive selection. getIBDiR can return NA iR statistics for a number of reasons, including trying to generate iR statistics when there are no IBD pairs or when all pairs are IBD, or when only several isolates are analyzed.

Usage

getIBDiR(ped.genotypes, ibd.matrix, groups = NULL)
getIBDiR(ped.genotypes, ibd.matrix, groups = NULL)

Arguments

ped.genotypes

A list containing 2 objects. See the Value description in getGenotypes for more details on this input.

ibd.matrix

A data frame containing the binary IBD information for each SNP and each pair. See the returned Value in getIBDmatrix for more details.

groups

A data frame with 3 columns of information:

Family ID
Isolate ID
Group ID

where IBD proportions are calculated for

all pairs of isolates within the same group
all pairwise-group comparisons where isolates belong to different groups

Group ID, for example, can be the geographic regions where the isolates were collected. The default is groups=NULL and IBD proportions will be calculated over all pairs.

Value

A data frame the following 7 columns:

Chromosome (type "character", "numeric" or "integer")
SNP identifiers (type "character")
Genetic map distance (centi morgans, cM) (type "numeric")
Base-pair position (type "integer")
Population (type "character" or "numeric")
Subpopulation (type "character" or "numeric")
iR statistic (type "numeric")
-log10 p vlaue (type "numeric")

where each row describes a unique SNP. The column Population is filled with 1s by default, while Subpopulation contains the group IDs from groups, where the proportion of pairs IBD has been calculated for all pairs of isolates belonging to the same group as well as all pairs of isolates where each isolate belongs to a different group. If groups=NULL then Subpopulation will be filled with 0s also. The population columns have been included for plotting purposes. The data frame is headed chr, snp_id, pos_M, pos_bp, pop, subpop, iR and log10_pvalue respectively.

Examples

# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

# calculate the significance of IBD sharing
my_iR <- getIBDiR(ped.genotypes = png_genotypes,
                  ibd.matrix = my_matrix,
                  groups = NULL)
# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

# calculate the significance of IBD sharing
my_iR <- getIBDiR(ped.genotypes = png_genotypes,
                  ibd.matrix = my_matrix,
                  groups = NULL)

Binary IBD Matrix

Description

getIBDmatrix() produces a binary matrix of IBD (1) and non-IBD (0) results for each SNP and isolate pair combination. Each row identifies a unique SNP while each column identifies a unique isolate pair.

Usage

getIBDmatrix(ped.genotypes, ibd.segments)
getIBDmatrix(ped.genotypes, ibd.segments)

Arguments

`ped.genotypes`	A list containing 2 objects. See the `Value` description in `getGenotypes` for more details on this input.
`ibd.segments`	A data frame containing the IBD segments detected by isoRelate. See the `Value` description in `getIBDsegments` for more details on this input.

Value

A data frame with the first four columns:

Chromosome (type "character", "numeric" or "integer")
SNP identifiers (type "character")
Genetic map distance (centi morgans, cM) (type "numeric")
Base-pair position (type "integer")

where each row describes a unique SNP. Columns 1-4 are headed chr, snp_id, pos_M and pos_bp respectively. Columns 5 onwards contain the binary IBD information for each isolate pair, where a single column corresponds to a single pair. These columns are labeled with merged family IDs and isolate IDs separated by a slash symbol (/). For example fid1/iid1/fid2/iid2.

Examples

# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)
# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

Parameter Estimation

Description

getIBDparameters() estimates the number of meiosis and the probabilities of sharing 0, 1 and 2 alleles IBD between all pairwise combinations of isolates.

Usage

getIBDparameters(ped.genotypes, number.cores = 1)
getIBDparameters(ped.genotypes, number.cores = 1)

Arguments

`ped.genotypes`	A list containing 2 objects. See the `Value` description in `getGenotypes` for more details on this input. Note the family IDs and isolate IDs in object 1 of this list must match the family IDs and isolate IDs in the header of object 2 of this list.
`number.cores`	Positive integer. The number of cores used for parallel execution.

Value

A data frame with the following eight columns:

Family 1 ID
Isolate 1 ID
Family 2 ID
Isolate 2 ID
The number of meiosis separating the pair
Probability of sharing 0 alleles IBD
Probability of sharing 1 allele IBD
Probability of sharing 2 alleles IBD

where each row describes a unique pair of isolates. The data frame is headed fid1, iid1, fid2, iid2, m, ibd0, ibd1 and ibd2 respectively.

Examples

# following processing and filtering of genotype data,
# we estimate the proportion of genome shared IBD
my_parameters <- getIBDparameters(ped.genotypes = png_genotypes,
                                  number.cores = 1)

head(my_parameters)
# following processing and filtering of genotype data,
# we estimate the proportion of genome shared IBD
my_parameters <- getIBDparameters(ped.genotypes = png_genotypes,
                                  number.cores = 1)

head(my_parameters)

Genome Cluster Networks

Description

getIBDpclusters() produces a network of clusters of isolates that share a minimum proportion of genome IBD. Isolates that do not share a minimum proportion IBD are not included in the network or output. The networks are created using R package igraph.

Usage

getIBDpclusters(ped.genotypes, ibd.segments, prop = 1, hi.clust = FALSE)
getIBDpclusters(ped.genotypes, ibd.segments, prop = 1, hi.clust = FALSE)

Arguments

`ped.genotypes`	A list containing 2 objects. See the `Value` description in `getGenotypes` for more details on this input.
`ibd.segments`	A data frame containing the IBD segments detected by isoRelate. See the `Value` description in `getIBDsegments` for more details on this input.
`prop`	Numeric value between (0,1]. The minimum proportion of genome shared IBD between a pair of isolates in order for the pair to be included in the network. For example, if `prop=1` then two isolates will be included if they are IBD over the entire genome, i.e. identical isolates, whereas `prop=0.5` will include isolates that share at least 50% of their genome IBD. The default is `prop=1`.
`hi.clust`	Logical. Whether to perform hierarchical clustering using the `fastgreedy.community` approach in the `igraph` package.

Value

A list of three objects named clusters, i.network and hi.clust:

A list where each object contains the names of isolates that form a disjoint cluster in the network. If hierarchical clustering has been performed then the clusters may not be disjoint.
An igraph network used in the construction of network plots. See http://igraph.org/r/ doe more details.
Logical. Whether or not hierarchical clustering has been performed.

IBD Posterior Probabilities

Description

getIBDposterior() calculates the posterior probabilities of IBD sharing between pairs of isolates.

Usage

getIBDposterior(ped.genotypes, parameters, number.cores = 1, error = 0.001)
getIBDposterior(ped.genotypes, parameters, number.cores = 1, error = 0.001)

Arguments

`ped.genotypes`	A list containing 2 objects. See the `Value` description in `getGenotypes` for more details on this input.
`parameters`	A data frame containing meioses and IBD probability estimates for all pairwise combinations of isolates. See the `Value` description in `getIBDparameters` for more details on this input.
`number.cores`	Positive integer. The number of cores used for parallel execution.
`error`	The genotyping error rate. The default value is 0.001.

Value

A data frame with the first four columns:

Chromosome
SNP identifiers
Genetic map distance
Base-pair position

where each row describes a single SNP. These columns are headed chr, snp_id, pos_M and pos_bp respectively. Columns 5 onwards contain the posterior probabilities for each pair of isolates, where a single column corresponds to one pair of isolates. These columns are labeled with merged family IDs and isolate IDs separated by a slash symbol (/).

Examples

## Not run: 
# calculate the posterior probability of IBD sharing
# note: this can take a while to run if there are many pairs
my_posterior <- getIBDposterior(ped.genotypes = png_genotypes,
                                parameters = png_parameters,
                                number.cores = 1,
                                error = 0.001)

head(my_posterior[,1:10])

## End(Not run)
## Not run: 
# calculate the posterior probability of IBD sharing
# note: this can take a while to run if there are many pairs
my_posterior <- getIBDposterior(ped.genotypes = png_genotypes,
                                parameters = png_parameters,
                                number.cores = 1,
                                error = 0.001)

head(my_posterior[,1:10])

## End(Not run)

Proportion of Pairs IBD

Description

getIBDproportion() calculates the proportion of pairs inferred IBD at each SNP.

Usage

getIBDproportion(ped.genotypes, ibd.matrix, groups = NULL)
getIBDproportion(ped.genotypes, ibd.matrix, groups = NULL)

Arguments

ped.genotypes

A list containing 2 objects. See the Value description in getGenotypes for more details on this input.

ibd.matrix

A data frame containing the binary IBD information for each SNP and each pair. See the returned Value in getIBDmatrix for more details.

groups

A data frame with 3 columns of information:

Family ID
Isolate ID
Group ID

where, if specified, IBD proportions are calculated for

all pairs of isolates within the same group
all pairwise-group comparisons where isolates belong to different groups

Group ID, for example, can be the geographic regions where the isolates were collected. The default is groups=NULL and IBD proportions will be calculated over all pairs.

Value

A data frame the following 7 columns:

Chromosome (type "character", "numeric" or "integer")
SNP identifiers (type "character")
Genetic map distance (centi morgans, cM) (type "numeric")
Base-pair position (type "integer")
Population (type "character" or "numeric")
Subpopulation (type "character" or "numeric")
Proportion of pairs IBD (type "integer")

where each row describes a unique SNP. The column Population is filled with 1s by default, while Subpopulation contains the group IDs from groups, where the proportion of pairs IBD has been calculated for all isolates belonging to the same group as well as all isolates from different groups. If groups=NULL then Subpopulation will be filled with 1s also. The population columns have been included for plotting purposes. The data frame is headed chr, snp_id, pos_M, pos_bp, pop, subpop and prop_ibd respectively.

Examples

# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

# calculate the proportion of pairs IBD at each SNP
my_proportion <- getIBDproportion(ped.genotypes = png_genotypes,
                                  ibd.matrix = my_matrix,
                                  groups = NULL)

# creating a stratification dataset
my_groups <- png_genotypes[[1]][,1:3]
my_groups[1:10,"pid"] <- "a"
my_groups[11:25,"pid"] <- "b"
my_groups[26:38,"pid"] <- "c"

my_proportion <- getIBDproportion(ped.genotypes = png_genotypes,
                                  ibd.matrix = my_matrix,
                                  groups = my_groups)

head(my_proportion)
# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

# calculate the proportion of pairs IBD at each SNP
my_proportion <- getIBDproportion(ped.genotypes = png_genotypes,
                                  ibd.matrix = my_matrix,
                                  groups = NULL)

# creating a stratification dataset
my_groups <- png_genotypes[[1]][,1:3]
my_groups[1:10,"pid"] <- "a"
my_groups[11:25,"pid"] <- "b"
my_groups[26:38,"pid"] <- "c"

my_proportion <- getIBDproportion(ped.genotypes = png_genotypes,
                                  ibd.matrix = my_matrix,
                                  groups = my_groups)

head(my_proportion)

IBD Segment Detection

Description

getIBDsegments() detects genomic regions shared IBD between all pairwise combinations of isolates.

Usage

getIBDsegments(ped.genotypes, parameters, number.cores = 1,
  minimum.snps = 20, minimum.length.bp = 50000, error = 0.001)
getIBDsegments(ped.genotypes, parameters, number.cores = 1,
  minimum.snps = 20, minimum.length.bp = 50000, error = 0.001)

Arguments

`ped.genotypes`	A list containing 2 objects. See the `Value` description in `getGenotypes` for more details on this input.
`parameters`	A data frame containing meioses and IBD probability estimates for all pairwise combinations of isolates. See the `Value` description in `getIBDparameters` for more details on this input.
`number.cores`	Positive integer. The number of cores used for parallel execution.
`minimum.snps`	An integer value denoting the minimum number of SNPs in an IBD segment for it to be reported. The default value is 20 SNPs.
`minimum.length.bp`	The minimum length of a reported IBD segment. The default value is 50,000 bp.
`error`	The genotyping error rate. The default value is 0.001.

Value

A data frame with the following columns

Family 1 ID
Isolate 1 ID
Family 2 ID
Isolate 2 ID
Chromosome
Start SNP
End SNP
Start position bp
End position bp
Start position M
End position M
Number of SNPs
Length bp
Length M
IBD status (1 = one allele shared IBD, 2 = two alleles shared IBD)

where each row describes a unique IBD segment. The data frame is headed fid1, iid1, fid2, iid2, chr, start_snp, end_snp, start_position_bp, end_position_bp, start_position_M, end_position_M, number_snps, length_bp, length_M and ibd_status respectively.

Examples

## Not run: 
# prior to IBD detection, parameter estimates must be estimated.
# Assuming this has been done, IBD inference is performed
my_ibd <- getIBDsegments(ped.genotypes = png_genotypes,
                        parameters = png_parameters,
                        number.cores = 1,
                        minimum.snps = 20,
                        minimum.length.bp = 50000,
                        error = 0.001)

head(my_ibd)

## End(Not run)
## Not run: 
# prior to IBD detection, parameter estimates must be estimated.
# Assuming this has been done, IBD inference is performed
my_ibd <- getIBDsegments(ped.genotypes = png_genotypes,
                        parameters = png_parameters,
                        number.cores = 1,
                        minimum.snps = 20,
                        minimum.length.bp = 50000,
                        error = 0.001)

head(my_ibd)

## End(Not run)

IBD Segment Summary

Description

getIBDsummary() prints a brief summary of the detected IBD segments to the console.

Usage

getIBDsummary(ped.genotypes, ibd.segments)
getIBDsummary(ped.genotypes, ibd.segments)

Arguments

`ped.genotypes`	A list containing 2 objects. See the `Value` description in `getGenotypes` for more details on this input.
`ibd.segments`	A data frame containing the IBD segments detected by isoRelate. See the `Value` description in `link{getIBDsegments}` for more details on this input.

function to find IBD that overlap interval

Description

function to find IBD that overlap interval

Usage

getOverlap(region.1, region.2)
getOverlap(region.1, region.2)

Arguments

`region.1`	IBD segment boundary
`region.2`	interest interval

Group Combinations for Analysis

Description

Creates a data frame containing family IDs and isolate IDs for each pair to be analysed. Each row corresponds to a unique pair.

Usage

groupPairs(group)
groupPairs(group)

Arguments

group

A character vector of all family IDs

Call Genotypes from Haplotype Data

Description

haplotypeToGenotype transforms PLINK haplotype data into genotype data of the form -1, 0, 1 and 2 representing missing genotypes, homozygous reference, heterozygous and homozygous alternative respectively. Haploid isolates are coded as diploid although will not have heterozygous genotypes.

Usage

haplotypeToGenotype(haplotypes, moi)
haplotypeToGenotype(haplotypes, moi)

Arguments

`haplotypes`	An integer matrix of haplotype data in PLINK format. A allele is denoted 1, B allele is denoted 2 and missing data is denoted 0
`moi`	An integer vector of multiplicity of infection (MOI) estimates for each isoalte. Isolate MOI estimates should be ordered such that value `n` of `moi` corresponds to column `n` of `haplotypes`.

Value

A matrix with genotype calls where columns correspond to isolates and rows correspond to SNPs

Plasmodium Falciparum Gene Highlight Dataset

Description

Gene annotations for 31 commonly studied Plasmodium falciparum genes, including antimalarial drug resistance genes, vaccine candidates and var genes. Gene annotations are for the reference genome 3D7 were downloaded from http://www.plasmodb.org/common/downloads/Current_Release/Pfalciparum3D7/gff/data/, release PlasmoDB-28, last modified 23/03/2016.

Usage

highlight_genes
highlight_genes

Format

A data frame with 5 columns of information

chr: Chromosomes
start: Base-pair positions of start of genes
end: Base-pair positions of end of genes
name: Gene name, commonly abbreviated
gene_id: Gene id

Internal Function

Description

IBDLabel is a function used to label unique IBD segments in a pair of isolates for a particular chromosome by determining breakpoints in IBD vs non-IBD regions. IBD segments are labelled in sequential order genome wide.

Usage

IBDLabel(snp_id, number_snps)
IBDLabel(snp_id, number_snps)

Arguments

`snp_id`	A numeric vector of SNP identifiers for IBD segments on the chromosome of interest.
`number_snps`	integer. The number of IBD SNPs for the chromosome of interest.

Binary Matrix of IBD

Description

Creates a binary matrix of IBD (1) and non-IBD (0) with each row representing a single SNP and each column representing a unique pair. The number of rows is equal to the total number of SNPs and the number of columns is equal to the number of pairs.

Usage

IBDMatrix(chromosomes, positions_bp, number_pairs, ibd_pairs_colnumbers,
  ibd_chromosomes, ibd_start_bp, ibd_stop_bp)
IBDMatrix(chromosomes, positions_bp, number_pairs, ibd_pairs_colnumbers,
  ibd_chromosomes, ibd_start_bp, ibd_stop_bp)

Arguments

`chromosomes`	A character vector containing the corresponding chromosome for each SNP
`positions_bp`	A numeric vector containing the corresponding bp position for each SNP
`number_pairs`	Numeric. The total number of pairs analysed
`ibd_pairs_colnumbers`	A numeric vector corresponding to column numbers in the output matrix where each unique number refers to a unique pair with IBD inferred
`ibd_chromosomes`	A character vector containing the chromosome for each detected IBD segment
`ibd_start_bp`	A numeric vector containing the base-pair position for the start of each detected IBD segment
`ibd_stop_bp`	A numeric vector containing the base-pair position for the end of each detected IBD segment

IBDparameters() calculates IBD probabilities then estimates the number of meiosis for an isolate pair.

Description

IBDparameters() calculates IBD probabilities then estimates the number of meiosis for an isolate pair.

Usage

IBDparameters(genotypes, pop_allele_freqs, gender_1, gender_2)
IBDparameters(genotypes, pop_allele_freqs, gender_1, gender_2)

Arguments

`genotypes`	An integer matrix of genotype calls for a pair of isolates. Each coloumn represents and isolate and each row represents a SNP.
`pop_allele_freqs`	A numeric vector of population allele frequencies for each SNP.
`gender_1`	An integer denoting the MOI value of isoalte 1.
`gender_2`	An integer denoting the MOI value of isoalte 2.

Value

A vector of 4 values representing the number of meiosis and the probabilities of sharing 0, 1 and 2 alleles IBD respectively.

Internal Function

Description

IBDTable() produces summaries of detected IBD segments for a single pair of isolates. These summaries include the genetic map start and end of IBD segments in bp, cM and SNP identifiers; and lengths of IBD segments in bp, cM and SNPs.

Usage

IBDTable(ibd.results)
IBDTable(ibd.results)

Arguments

ibd.results

A data frame containing family ID and isolate ID for isolate 1, family ID and isolate ID for isolate 2, numeric SNP identifiers, chromosome identifiers, genetic map positions of SNPs in Morgans (M) and base-pairs (bp) and the Viterbi results respectively, for all SNPs.

Value

A data frame containing a summary of all IBD segments inferred for this pair of isolates. The data frame contains the following columns:

Family 1 ID
Isolate 1 ID
Family 2 ID
Isolate 2 ID
Chromosome
SNP identifier
Start SNP
End SNP
Start position bp
End position bp
Start position M
End position M
Number of SNPs
Length bp
Length M
IBD status (1 = 1 allele shared IBD, 2 = 2 alleles shared IBD)

Internal Function

Description

iRfunction() calculates the iR statistic used to assess the significance of excess IBD sharing at loci in the genome. The final statistic, -log10 (P-values), is returned for each SNP.

Usage

iRfunction(locus.matrix, frequency)
iRfunction(locus.matrix, frequency)

Arguments

`locus.matrix`	A data frame containing binary IBD values. See `getIBDmatrix` for more details.
`frequency`	A vector of population allele frequencies for each SNP.

Pair Combinations for Analysis

Description

Creates a data frame containing family IDs and isolate IDs for each pair to be analysed. Each row corresponds to a unique pair.

Usage

isolatePairs(fid, iid)
isolatePairs(fid, iid)

Arguments

`fid`	A character vector of all family IDs
`iid`	A character vector of all individual ID

Internal Function

Description

merge_lists() is a function used to merge summary IBD results for multiple pairs when running the IBD analysis on multiple cores

Usage

merge_lists(A, B)
merge_lists(A, B)

Arguments

`A`	List with 2 objects for one pair. Object 1 is the IBD summaries for a pair and object 2 is the IBD posterior probabilities.
`B`	List with 2 objects for another pair. Object 1 is the IBD summaries for a pair and object 2 is the IBD posterior probabilities.

Value

A list with 2 objects containing merged lists from A and B above.

IBD Segments For The Papua New Guinea Dataset

Description

The IBD segments inferred using isoRelate with the parameter settings as in the Vignette.

Usage

my_ibd
my_ibd

Format

A data frame with

fid1: Family 1 ID
iid1: Isolate 1 ID
fid2: Family 2 ID
iid2: Isolate 2 ID
chr: Chromosome
start_snp: IBD segment start SNP ID
end_snp: IBD segment end SNP ID
start_position_bp: IBD segment start SNP base-pair position
end_position_bp: IBD segment end SNP base-pair position
start_position_M: IBD segment start SNP morgan position
end_position_M: IBD segment end SNP morgan position
number_snps: Number of SNPs in IBD segment
length_bp: Length of IBD segment in base-pairs
length_M: Length of IBD segment in morgans
ibd_status: The number of alleles shared IBD (either 1 or 2)

Plot Cluster Networks

Description

plotIBDclusters() Produces a figure of an isoRelate cluster network, where unique isolates are represented by vertices and a line is drawn between two vertices if the isolates have been inferred IBD via the criteria specified in either getIBDiclusters or getIBDpclusters.The networks are created using the R package igraph.

Usage

plotIBDclusters(ped.genotypes, clusters, groups = NULL, vertex.color = NULL,
  vertex.frame.color = "white", vertex.size = 4, vertex.name = FALSE,
  edge.color = "gray60", edge.width = 0.8, mark.border = "white",
  mark.col = "gray94", add.legend = TRUE, legend.x = -1.5,
  legend.y = -0.25, layout = NULL, return.layout = FALSE)
plotIBDclusters(ped.genotypes, clusters, groups = NULL, vertex.color = NULL,
  vertex.frame.color = "white", vertex.size = 4, vertex.name = FALSE,
  edge.color = "gray60", edge.width = 0.8, mark.border = "white",
  mark.col = "gray94", add.legend = TRUE, legend.x = -1.5,
  legend.y = -0.25, layout = NULL, return.layout = FALSE)

Arguments

`ped.genotypes`	A list containing 2 objects. See the `Value` description in `getGenotypes` for more details on this input.
`clusters`	A named list of three objects containing network information. See the `Value` description in either `getIBDiclusters` or `getIBDpclusters` for more details on this input.
`groups`	A data frame with 3 columns of information: Family ID Isolate ID Group ID Group ID, for example, can be the geographic regions where the isolates were collected. If `groups` is specified then each isolate in the pedigree must belong to a group. Vertices in the network will be colored according to group allocation. The default is `groups=NULL` and all vertices will have the same color.
`vertex.color`	A vector of characters or numeric values of the vertex colors in the network. If `groups` is specified then `vertex.color` should contain the same number of colors as unique groups.
`vertex.frame.color`	Character string or numeric value. A single color that will be used as the vertex border. Default is `vertex.fame.color="white"`.
`vertex.size`	Numeric value indicating the size of the vertices in the network. Default is `vertex.size=4`.
`vertex.name`	Logical. Whether to add isolate names to the vertices. Default is `vertex.name=FALSE`.
`edge.color`	Character string or numeric value. A single color to be used for all edges. Default is `edge.color="gray60"`.
`edge.width`	Numeric. A single value indicating the width of the edges. Default is `edge.width=0.8`.
`mark.border`	Character string or numeric value. A single color to be used for all borders in hierarchical clustering groups. Default is `mark.border="white"`.
`mark.col`	Character string or numeric value. A single color to be used to fill hierarchical clustering groupings. Default is `mark.col="gray94"`.
`add.legend`	Logical. Whether to include a legend in the plot. Default is `add.legend=TRUE`.
`legend.x`	Numerical. A single value indicating the x-coordinate of the legend, with default `legend.x=-1.5`.
`legend.y`	Numerical. A single value indicating the y-coordinate of the legend, with default `legend.y=-0.25`.
`layout`	A matrix containing the x and y coordinates of the vertices, generated using the Fruchterman-Reingold force-directed layout.
`return.layout`	Logical. Whether or not to return the layout matrix (vertex positions) in the network. This layout can be used as the input for the parameter `layout` to avoid different network configurations each time `plotClusters()` is run on the same network.

Examples

# generate the isolates who are IBD over the Plasmodium falciparum CRT gene
my_i_clusters <- getIBDiclusters(ped.genotypes = png_genotypes,
                                 ibd.segments = png_ibd,
                                 interval = c("Pf3D7_07_v3", 403222, 406317),
                                 prop=0,
                                 hi.clust = FALSE)

str(my_i_clusters)

# creating a stratification dataset
my_groups <- png_genotypes[[1]][,1:3]
my_groups[1:10,"pid"] <- "a"
my_groups[11:25,"pid"] <- "b"
my_groups[26:38,"pid"] <- "c"

# plot the network of clusters
plotIBDclusters(ped.genotypes = png_genotypes,
                clusters = my_i_clusters,
                groups = my_groups,
                vertex.color = NULL,
                vertex.frame.color = "white",
                vertex.size = 4,
                vertex.name = FALSE,
                edge.color = "gray60",
                edge.width = 0.8,
                mark.border = "white",
                mark.col = "gray94",
                add.legend = TRUE,
                legend.x = -1.5,
                legend.y = -0.25,
                layout = NULL,
                return.layout = FALSE)

# generate the isolates who share at least than 90% of their genome IBD
my_p_clusters <- getIBDpclusters(ped.genotypes = png_genotypes,
                                 ibd.segments = png_ibd,
                                 prop=0.9,
                                 hi.clust = FALSE)

# plot the network of clusters
plotIBDclusters(ped.genotypes = png_genotypes,
                clusters = my_p_clusters,
                groups = my_groups,
                vertex.color = NULL,
                vertex.frame.color = "white",
                vertex.size = 4,
                vertex.name = FALSE,
                edge.color = "gray60",
                edge.width = 0.8,
                mark.border = "white",
                mark.col = "gray94",
                add.legend = TRUE,
                legend.x = -1.5,
                legend.y = -0.25,
                layout = NULL,
                return.layout = FALSE)
# generate the isolates who are IBD over the Plasmodium falciparum CRT gene
my_i_clusters <- getIBDiclusters(ped.genotypes = png_genotypes,
                                 ibd.segments = png_ibd,
                                 interval = c("Pf3D7_07_v3", 403222, 406317),
                                 prop=0,
                                 hi.clust = FALSE)

str(my_i_clusters)

# creating a stratification dataset
my_groups <- png_genotypes[[1]][,1:3]
my_groups[1:10,"pid"] <- "a"
my_groups[11:25,"pid"] <- "b"
my_groups[26:38,"pid"] <- "c"

# plot the network of clusters
plotIBDclusters(ped.genotypes = png_genotypes,
                clusters = my_i_clusters,
                groups = my_groups,
                vertex.color = NULL,
                vertex.frame.color = "white",
                vertex.size = 4,
                vertex.name = FALSE,
                edge.color = "gray60",
                edge.width = 0.8,
                mark.border = "white",
                mark.col = "gray94",
                add.legend = TRUE,
                legend.x = -1.5,
                legend.y = -0.25,
                layout = NULL,
                return.layout = FALSE)

# generate the isolates who share at least than 90% of their genome IBD
my_p_clusters <- getIBDpclusters(ped.genotypes = png_genotypes,
                                 ibd.segments = png_ibd,
                                 prop=0.9,
                                 hi.clust = FALSE)

# plot the network of clusters
plotIBDclusters(ped.genotypes = png_genotypes,
                clusters = my_p_clusters,
                groups = my_groups,
                vertex.color = NULL,
                vertex.frame.color = "white",
                vertex.size = 4,
                vertex.name = FALSE,
                edge.color = "gray60",
                edge.width = 0.8,
                mark.border = "white",
                mark.col = "gray94",
                add.legend = TRUE,
                legend.x = -1.5,
                legend.y = -0.25,
                layout = NULL,
                return.layout = FALSE)

Plot iR Statistics

Description

plotIBDiR() plots the -log10 (p-values) used to assess the significance of excess IBD sharing.

Usage

plotIBDiR(ibd.iR, interval = NULL, annotation.genes = NULL,
  annotation.genes.color = NULL, highlight.genes = NULL,
  highlight.genes.labels = TRUE, highlight.genes.color = NULL,
  highlight.genes.alpha = 0.1, point.size = 1, point.color = NULL,
  add.rug = FALSE, plot.title = NULL, add.legend = FALSE,
  facet.label = TRUE, facet.scales = "fixed")
plotIBDiR(ibd.iR, interval = NULL, annotation.genes = NULL,
  annotation.genes.color = NULL, highlight.genes = NULL,
  highlight.genes.labels = TRUE, highlight.genes.color = NULL,
  highlight.genes.alpha = 0.1, point.size = 1, point.color = NULL,
  add.rug = FALSE, plot.title = NULL, add.legend = FALSE,
  facet.label = TRUE, facet.scales = "fixed")

Arguments

`ibd.iR`	A data frame containing the iR summary statistics for each SNP. See the returned `Value` in `getIBDiR` for more details. If multiple subpopulations are specified (column name "subpop") then the iR statistics for each subpopulation will be plotted on a separate facet in the figure (see http://docs.ggplot2.org/current/facet_grid.html on faceting). If there are many subpopulations (>8) it may be better to plot subsets of the subpopulations as apposed to all subpopulations in a single figure. If multiple populations (column name "pop") are specified then only the first population will be included in the figure. Genomic locations of annotation genes can be included in the figure and specific intervals can be highlighted.
`interval`	A vector of length 3 containing the genomic locations of a specific region to plot. This vector should contain the chromosome ID, the start of the interval in base-pairs and the end of the interval in base-pairs; in this order respectively. The default is `interval=NULL` which will plot iR statistics over all chromosomes in `ibd.iR`.
`annotation.genes`	A data frame containing information on annotation genes to be included in the figure. This data frame must have at least 5 columns of information: Chromosome (type `"numeric"` or `"integer"`) Gene name (type `"character"`) Start location of the gene in base-pairs (type `"numeric"` or `"integer"`) End location of the gene in base-pairs (type `"numeric"` or `"integer"`) Gene strand (+ or -) (type `"character"`) `annotation.genes` must contain the following headers `chr, name, start, end` and `strand`. This data frame does not have to be in a specific order, however it must contain all of the above information with respective labels. The default is annotation.genes=NULL.
`annotation.genes.color`	A vector of characters or numeric values containing the two colors according to gene stand (positive (+) or negative (-))
`highlight.genes`	A data frame containing information of genes or regions to highlight. The data frame must have at least 4 columns of information: Chromosome (type `"numeric"` or `"integer"`) Gene name (type `"character"`) Start location of the gene in base-pairs (type `"numeric"` or `"integer"`) End location of the gene in base-pairs (type `"numeric"` or `"integer"`) `highlight.genes` should contain the following headers `chr, name, start` and `end`. This data frame does not have to be in a specific order, however it must contain all of the above information with respective labels. The default is highlight.genes=NULL.
`highlight.genes.labels`	Logical. Whether to include gene names as labels in the figure. The default is `highlight.genes.labels=FALSE`.
`highlight.genes.color`	Character string or numeric value. A single color that will be used to highlight a region/gene. The default is `highlight.genes.color=NULL`.
`highlight.genes.alpha`	Numeric. A single value between 0 and 1 indicating the gene color transparency. The default is `highlight.genes.alpha=0.1`.
`point.size`	Numeric. The size of the points in the figures. The default is `point.size=1`.
`point.color`	A vector of characters or numeric values denoting the color of points to be plotted. If there are multiple subpopulations then the number of colors specified should equal the number of subpopulations. The default is `point.color=NULL` which will use isoRelate default colors.
`add.rug`	Logical. Whether to include SNP positions as a rug in the figure. The default is `add.rug=FALSE`
`plot.title`	A character string of a title to be added to the figure The default is `plot.title=NULL` which does not add a title to the plot.
`add.legend`	Logical. Whether a legend containing subpopulation information should be plotted. The default is `add.legend=FALSE`.
`facet.label`	Logical. Whether to include facet labels if multiple subpopulations (column name "subpop") are specified.
`facet.scales`	A character string of either `"fixed"`, `"free"`, `"free_x"` or `"free_y"` specifying the facet axis-scales. The default is `facet.scales="fixed"`

Examples

# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

# calculate the significance of IBD sharing
my_iR <- getIBDiR(ped.genotypes = png_genotypes,
                  ibd.matrix = my_matrix,
                  groups = NULL)

# plot the iR statistics
plotIBDiR(ibd.iR = my_iR,
          interval = NULL,
          annotation.genes = NULL,
          annotation.genes.color = NULL,
          highlight.genes = NULL,
          highlight.genes.labels = FALSE,
          highlight.genes.color = NULL,
          highlight.genes.alpha = 0.1,
          point.size = 1,
          point.color = NULL,
          add.rug = FALSE,
          plot.title = "Significance of IBD sharing",
          add.legend = FALSE,
          facet.label = TRUE,
          facet.scales = "fixed")
# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

# calculate the significance of IBD sharing
my_iR <- getIBDiR(ped.genotypes = png_genotypes,
                  ibd.matrix = my_matrix,
                  groups = NULL)

# plot the iR statistics
plotIBDiR(ibd.iR = my_iR,
          interval = NULL,
          annotation.genes = NULL,
          annotation.genes.color = NULL,
          highlight.genes = NULL,
          highlight.genes.labels = FALSE,
          highlight.genes.color = NULL,
          highlight.genes.alpha = 0.1,
          point.size = 1,
          point.color = NULL,
          add.rug = FALSE,
          plot.title = "Significance of IBD sharing",
          add.legend = FALSE,
          facet.label = TRUE,
          facet.scales = "fixed")

Plot The Proportion of Pairs IBD

Description

plotIBDproportions() plots the proportion of pairs IBD for each SNP across the genome.

Usage

plotIBDproportions(ibd.proportions, interval = NULL,
  annotation.genes = NULL, annotation.genes.color = NULL,
  highlight.genes = NULL, highlight.genes.labels = TRUE,
  highlight.genes.color = NULL, highlight.genes.alpha = 0.1,
  line.color = NULL, add.rug = TRUE, plot.title = NULL,
  add.legend = TRUE, facet.label = TRUE, facet.scales = "fixed",
  subpop.facet = FALSE)
plotIBDproportions(ibd.proportions, interval = NULL,
  annotation.genes = NULL, annotation.genes.color = NULL,
  highlight.genes = NULL, highlight.genes.labels = TRUE,
  highlight.genes.color = NULL, highlight.genes.alpha = 0.1,
  line.color = NULL, add.rug = TRUE, plot.title = NULL,
  add.legend = TRUE, facet.label = TRUE, facet.scales = "fixed",
  subpop.facet = FALSE)

Arguments

`ibd.proportions`	A data frame containing the proportion of pairs IBD at each SNP. See the returned `Value` in `getIBDproportion` for more details. If multiple subpopulations are specified (column name "subpop") then the proportions for each subpopulation will be plotted, either in a single facet or over multiple facets. See http://docs.ggplot2.org/current/facet_grid.html on faceting. If multiple populations (column name "pop") are specified then the proportions for each population will be plotted on a separate facet, with all subpopulations in a single facet. If there are many populations or subpopulations (>8) it may be better to subset the populations to those of interest before plotting. Genomic locations of annotation genes can be included in the figure and specific genes or regions can be highlighted.
`interval`	A vector of length 3 containing the genomic locations of a specific region to plot. This vector should contain the chromosome ID, the start of the interval in base-pairs and the end of the interval in base-pairs; in this order respectively. The default is `interval=NULL` which will plot the proportions over all chromosomes in `ibd.proportions`.
`annotation.genes`	A data frame containing information on annotation genes to be included in the figure. This data frame must have at least 5 columns of information: Chromosome (type `"numeric"` or `"integer"`) Gene name (type `"character"`) Start location of the gene in base-pairs (type `"numeric"` or `"integer"`) End location of the gene in base-pairs (type `"numeric"` or `"integer"`) Gene strand (+ or -) (type `"character"`) `annotation.genes` must contain the following headers `chr, name, start, end` and `strand`. This data frame does not have to be in a specific order, however it must contain all of the above information with respective labels. The default is annotation.genes=NULL.
`annotation.genes.color`	A vector of characters or numeric values containing the two colors representing gene stand (positive (+) or negative (-))
`highlight.genes`	A data frame containing information of genes or regions to highlight. The data frame must have at least 4 columns of information: Chromosome (type `"numeric"` or `"integer"`) Gene name (type `"character"`) Start location of the gene in base-pairs (type `"numeric"` or `"integer"`) End location of the gene in base-pairs (type `"numeric"` or `"integer"`) `highlight.genes` should contain the following headers `chr, name, start` and `end`. This data frame does not have to be in a specific order, however it must contain all of the above information with respective labels. The default is highlight.genes=NULL.
`highlight.genes.labels`	Logical. Whether to include gene names as labels in the figure. The default is `highlight.genes.labels=FALSE`.
`highlight.genes.color`	Character string or numeric value. A single color that will be used to highlight a region/gene. The default is `highlight.genes.color=NULL`.
`highlight.genes.alpha`	Numeric. A single value between 0 and 1 indicating the gene color transparency. The default is `highlight.genes.alpha=0.1`.
`line.color`	A vector of characters or numeric values denoting the color of lines to be plotted. If there are multiple populations/subpopulations then the number of colors specified should equal the number of unique populations/subpopulations combinations. The default is `line.color=NULL` which will use isoRelate default colors.
`add.rug`	Logical. Whether to include SNP positions as a rug in the figure. The default is `add.rug=FALSE`
`plot.title`	A character string of a title to be added to the figure The default is `plot.title=NULL` which does not add a title to the plot.
`add.legend`	Logical. Whether a legend containing subpopulation information should be plotted. The default is `add.legend=FALSE`.
`facet.label`	Logical. Whether to include facet labels if multiple populations/subpopulations (column names "pop" and "subpop") are specified.
`facet.scales`	A character string of either `"fixed"`, `"free"`, `"free_x"` or `"free_y"` specifying the facet axis-scales. The default is `facet.scales="fixed"`
`subpop.facet`	Logical. Whether to plot subpopulations in separate facets. The default is `subpop.facet=FALSE`. If `subpop.facet=TRUE` and there are multiple populations then subpopulations will not be drawn in separate facets.

Examples

# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

# calculate the proportion of pairs IBD at each SNP
my_proportion <- getIBDproportion(ped.genotypes = png_genotypes,
                                  ibd.matrix = my_matrix,
                                  groups = NULL)

# plot the proportion of pairs IBD
plotIBDproportions(ibd.proportions = my_proportion,
                   interval = NULL,
                   annotation.genes = NULL,
                   annotation.genes.color = NULL,
                   highlight.genes = NULL,
                   highlight.genes.labels = TRUE,
                   highlight.genes.color = NULL,
                   highlight.genes.alpha = 0.1,
                   add.rug = FALSE,
                   plot.title = "Proportion of pairs IBD in PNG",
                   add.legend = FALSE,
                   line.color = NULL,
                   facet.label = TRUE,
                   facet.scales = "fixed",
                   subpop.facet = FALSE)

# creating a stratification dataset
my_groups <- png_genotypes[[1]][,1:3]
my_groups[1:10,"pid"] <- "a"
my_groups[11:25,"pid"] <- "b"
my_groups[26:38,"pid"] <- "c"

my_proportion <- getIBDproportion(ped.genotypes = png_genotypes,
                                  ibd.matrix = my_matrix,
                                  groups = my_groups)

# plot the proportion of pairs IBD
plotIBDproportions(ibd.proportions = my_proportion,
                   interval = NULL,
                   annotation.genes = NULL,
                   annotation.genes.color = NULL,
                   highlight.genes = NULL,
                   highlight.genes.labels = FALSE,
                   highlight.genes.color = NULL,
                   highlight.genes.alpha = 0.1,
                   line.color = NULL,
                   add.rug = FALSE,
                   plot.title = "Proportion of pairs IBD in PNG - with stratification",
                   add.legend = FALSE,
                   facet.label = TRUE,
                   facet.scales = "fixed",
                   subpop.facet = TRUE)
# generate a binary IBD matrix
my_matrix <- getIBDmatrix(ped.genotypes = png_genotypes,
                          ibd.segments = png_ibd)

# calculate the proportion of pairs IBD at each SNP
my_proportion <- getIBDproportion(ped.genotypes = png_genotypes,
                                  ibd.matrix = my_matrix,
                                  groups = NULL)

# plot the proportion of pairs IBD
plotIBDproportions(ibd.proportions = my_proportion,
                   interval = NULL,
                   annotation.genes = NULL,
                   annotation.genes.color = NULL,
                   highlight.genes = NULL,
                   highlight.genes.labels = TRUE,
                   highlight.genes.color = NULL,
                   highlight.genes.alpha = 0.1,
                   add.rug = FALSE,
                   plot.title = "Proportion of pairs IBD in PNG",
                   add.legend = FALSE,
                   line.color = NULL,
                   facet.label = TRUE,
                   facet.scales = "fixed",
                   subpop.facet = FALSE)

# creating a stratification dataset
my_groups <- png_genotypes[[1]][,1:3]
my_groups[1:10,"pid"] <- "a"
my_groups[11:25,"pid"] <- "b"
my_groups[26:38,"pid"] <- "c"

my_proportion <- getIBDproportion(ped.genotypes = png_genotypes,
                                  ibd.matrix = my_matrix,
                                  groups = my_groups)

# plot the proportion of pairs IBD
plotIBDproportions(ibd.proportions = my_proportion,
                   interval = NULL,
                   annotation.genes = NULL,
                   annotation.genes.color = NULL,
                   highlight.genes = NULL,
                   highlight.genes.labels = FALSE,
                   highlight.genes.color = NULL,
                   highlight.genes.alpha = 0.1,
                   line.color = NULL,
                   add.rug = FALSE,
                   plot.title = "Proportion of pairs IBD in PNG - with stratification",
                   add.legend = FALSE,
                   facet.label = TRUE,
                   facet.scales = "fixed",
                   subpop.facet = TRUE)

Plot IBD Segments

Description

plotIBDsegments() plots IBD segments for pairs across the genome. IBD segments are depicted by colored blocks.

Usage

plotIBDsegments(ped.genotypes, ibd.segments, interval = NULL,
  annotation.genes = NULL, annotation.genes.color = NULL,
  highlight.genes = NULL, highlight.genes.labels = TRUE,
  highlight.genes.color = NULL, highlight.genes.alpha = 0.1,
  segment.height = 0.5, segment.color = NULL, number.per.page = NULL,
  fid.label = TRUE, iid.label = TRUE, ylabel.size = 9, add.rug = FALSE,
  plot.title = NULL, add.legend = TRUE)
plotIBDsegments(ped.genotypes, ibd.segments, interval = NULL,
  annotation.genes = NULL, annotation.genes.color = NULL,
  highlight.genes = NULL, highlight.genes.labels = TRUE,
  highlight.genes.color = NULL, highlight.genes.alpha = 0.1,
  segment.height = 0.5, segment.color = NULL, number.per.page = NULL,
  fid.label = TRUE, iid.label = TRUE, ylabel.size = 9, add.rug = FALSE,
  plot.title = NULL, add.legend = TRUE)

Arguments

`ped.genotypes`	A list containing 3 objects. See the `Value` description in `getGenotypes` for more details on this input.
`ibd.segments`	A data frame containing the IBD segments inferred from pairs of isolates See the returned `value` in `getIBDsegments` for more details.
`interval`	A vector of length 3 containing the genomic locations of a specific region to plot. This vector should contain the chromosome ID, the start of the interval in base-pairs and the end of the interval in base-pairs; in this order respectively. The default is `interval=NULL` which will plot the segments over all chromosomes.
`annotation.genes`	A data frame containing information on annotation genes to be included in the figure. This data frame must have at least 5 columns of information: Chromosome (type `"numeric"` or `"integer"`) Gene name (type `"character"`) Start location of the gene in base-pairs (type `"numeric"` or `"integer"`) End location of the gene in base-pairs (type `"numeric"` or `"integer"`) Gene strand (+ or -) (type `"character"`) `annotation.genes` must contain the following headers `chr, name, start, end` and `strand`. This data frame does not have to be in a specific order, however it must contain all of the above information with respective labels. The default is annotation.genes=NULL.
`annotation.genes.color`	A vector of characters or numeric values containing the two colors representing gene stand (positive (+) or negative (-))
`highlight.genes`	A data frame containing information of genes or regions to highlight. The data frame must have at least 4 columns of information: Chromosome (type `"numeric"` or `"integer"`) Gene name (type `"character"`) Start location of the gene in base-pairs (type `"numeric"` or `"integer"`) End location of the gene in base-pairs (type `"numeric"` or `"integer"`) `highlight.genes` should contain the following headers `chr, name, start` and `end`. This data frame does not have to be in a specific order, however it must contain all of the above information with respective labels. The default is highlight.genes=NULL.
`highlight.genes.labels`	Logical. Whether to include gene names as labels in the figure. The default is `highlight.genes.labels=FALSE`.
`highlight.genes.color`	Character string or numeric value. A single color that will be used to highlight a region/gene. The default is `highlight.genes.color=NULL`.
`highlight.genes.alpha`	Numeric. A single value between 0 and 1 indicating the gene color transparency. The default is `highlight.genes.alpha=0.1`.
`segment.height`	A numeric value giving the hight of IBD segment blocks, such that 0 < segment.height <= 1. The default is `segment.height=0.5`.
`segment.color`	A vector of characters or numeric values denoting the color of the segments to be plotted. Two colors must be specified, one for segments with 1 allele IBD and one for segments with 2 alleles IBD.
`number.per.page`	A numeric value indicating the maximum number of IBD pairs to plot in a single graphics window. The default is `number.per.page=NULL` which will plot all IBD pairs in a single window. This may not be ideal when there are many IBD pairs. If `number.per.page` is set, it is recommended to plot the output to a file as opposed to the R plotting window.
`fid.label`	Logical. If `fid.label=TRUE`, family IDs will be included in the y-axis labels; otherwise family IDs will be omitted. The default is `add.fid.name=TRUE`.
`iid.label`	Logical. If `iid.label=TRUE`, isolate IDs will be included in the y-axis labels; otherwise isolate IDs will be omitted. The default is `add.iid.name=TRUE`.
`ylabel.size`	A numeric value indicating the size of the y-axis labels if drawn. The default is `ylabel.size=9`.
`add.rug`	Logical. Whether to include SNP positions as a rug in the figure. The default is `add.rug=FALSE`
`plot.title`	A character string of a title to be added to the figure The default is `plot.title=NULL` which does not add a title to the plot.
`add.legend`	Logical. If `add.legend=TRUE`, a legend specifying the IBD status (1 allele IBD or 2 alleles IBD) will be included. The default is `add.legend=TRUE`.

Examples

# plot IBD segments
plotIBDsegments(ped.genotypes = png_genotypes,
                ibd.segments = png_ibd,
                interval = NULL,
                annotation.genes = NULL,
                annotation.genes.color = NULL,
                highlight.genes = NULL,
                highlight.genes.labels = FALSE,
                highlight.genes.color = NULL,
                highlight.genes.alpha = 0.1,
                segment.height = 0.6,
                number.per.page = NULL,
                fid.label = FALSE,
                iid.label = FALSE,
                ylabel.size = 9,
                add.rug = FALSE,
                plot.title = "Distribution of IBD segments in PNG",
                add.legend = TRUE,
                segment.color = NULL)

# plot IBD segments over an interval: chromosome 7: 350000 - 550000
plotIBDsegments(ped.genotypes = png_genotypes,
                ibd.segments = png_ibd,
                interval = c("Pf3D7_07_v3",350000,550000),
                annotation.genes = annotation_genes,
                annotation.genes.color = NULL,
                highlight.genes = highlight_genes,
                highlight.genes.labels = FALSE,
                highlight.genes.color = NULL,
                highlight.genes.alpha = 0.1,
                segment.height = 0.8,
                number.per.page = NULL,
                fid.label = FALSE,
                iid.label = FALSE,
                ylabel.size = 9,
                add.rug = TRUE,
                plot.title = "Distribution of IBD segments in PNG",
                add.legend = TRUE,
                segment.color = c("purple","green"))
# plot IBD segments
plotIBDsegments(ped.genotypes = png_genotypes,
                ibd.segments = png_ibd,
                interval = NULL,
                annotation.genes = NULL,
                annotation.genes.color = NULL,
                highlight.genes = NULL,
                highlight.genes.labels = FALSE,
                highlight.genes.color = NULL,
                highlight.genes.alpha = 0.1,
                segment.height = 0.6,
                number.per.page = NULL,
                fid.label = FALSE,
                iid.label = FALSE,
                ylabel.size = 9,
                add.rug = FALSE,
                plot.title = "Distribution of IBD segments in PNG",
                add.legend = TRUE,
                segment.color = NULL)

# plot IBD segments over an interval: chromosome 7: 350000 - 550000
plotIBDsegments(ped.genotypes = png_genotypes,
                ibd.segments = png_ibd,
                interval = c("Pf3D7_07_v3",350000,550000),
                annotation.genes = annotation_genes,
                annotation.genes.color = NULL,
                highlight.genes = highlight_genes,
                highlight.genes.labels = FALSE,
                highlight.genes.color = NULL,
                highlight.genes.alpha = 0.1,
                segment.height = 0.8,
                number.per.page = NULL,
                fid.label = FALSE,
                iid.label = FALSE,
                ylabel.size = 9,
                add.rug = TRUE,
                plot.title = "Distribution of IBD segments in PNG",
                add.legend = TRUE,
                segment.color = c("purple","green"))

Filtered Genotypes For The Papua New Guinea Dataset

Description

Processed raw genotype data with the parameter settings as in the Vignette.

Usage

png_genotypes
png_genotypes

Format

A list of two objects named pedigree and genotypes:

pedigree is a data frame with the following information:
1. Family ID
2. Isolate ID
3. Paternal ID. This is not used by isoRelate and is set to zero.
4. Maternal ID. This is not used by isoRelate and is set to zero.
5. Multiplicity of infection - 1 for single infection and 2 for multiple infection
6. Affection status of isolate. This is set to 2 and is ignored by isoRelate.
genotypes is a data frame with the first 5 columns:
1. Chromosome
2. SNP identifier
3. Genetic map distance
4. Base-pair position
5. Population allele frequency
where each row describes a single SNP. Columns 6 onwards contain the genotype data for each isolate, where a single column corresponds to a single isolate. These columns are labeled with merged family IDs and isolate IDs separated by a slash symbol (/).

IBD Segments For The Papua New Guinea Dataset

Description

The IBD segments inferred using isoRelate with the parameter settings as in the Vignette.

Usage

png_ibd
png_ibd

Format

A data frame with

fid1: Family 1 ID
iid1: Isolate 1 ID
fid2: Family 2 ID
iid2: Isolate 2 ID
chr: Chromosome
start_snp: IBD segment start SNP ID
end_snp: IBD segment end SNP ID
start_position_bp: IBD segment start SNP base-pair position
end_position_bp: IBD segment end SNP base-pair position
start_position_M: IBD segment start SNP morgan position
end_position_M: IBD segment end SNP morgan position
number_snps: Number of SNPs in IBD segment
length_bp: Length of IBD segment in base-pairs
length_M: Length of IBD segment in morgans
ibd_status: The number of alleles shared IBD (either 1 or 2)

IBD Parameter Estimates For The Papua New Guinea Dataset

Description

The parameter estimates inferred using isoRelate with the parameter settings as in the Vignette.

Usage

png_parameters
png_parameters

Format

A data frame with

fid1: Family 1 ID
iid1: Isolate 1 ID
fid2: Family 2 ID
iid2: Isolate 2 ID
m: The estimated number of meiosis separating each pair of isolates
ibd0: The estimated proportion of genome with 0 alleles IBD
ibd1: The estimated proportion of genome with 1 allele IBD
ibd2: The estimated proportion of genome with 2 alleles IBD

Papua New Guinea Plasmodium Falciparum Dataset

Description

A PED/MAP file containing gneotype information for 38 isolates from Madang, Papua New Guinea. This dataset was released as part of the MalariaGEN Pf3k consortium in VCF file format, which underwent extensive data processing to result in the final SNP set in the PED/MAP file. This data, and more, is available from https://www.malariagen.net/projects/pf3k.

Usage

png_pedmap
png_pedmap

Format

List with two objects

PED: PLINK PED data frame. See getGenotypes for details.
MAP: PLINK MAP data frame. See getGenotypes for details.

See getGenotypes for details.

Round digits to specified decimal places

Description

Round digits to specified decimal places

Usage

roundDecimal(number, digits)
roundDecimal(number, digits)

Arguments

`number`	A number to round
`digits`	The number of digits to round to

The transition probabilities for 2 diploid chromosomes

Description

The transition probabilities for 2 diploid chromosomes

Usage

transitionProbDD(omega_0, omega_1, omega_2, meiosis, dist_cM, ibd_current,
  ibd_previous)
transitionProbDD(omega_0, omega_1, omega_2, meiosis, dist_cM, ibd_current,
  ibd_previous)

Arguments

`omega_0`	The probability of sharing 0 alleles IBD
`omega_1`	The probability of sharing 1 allele IBD
`omega_2`	The probability of sharing 2 alleles IBD
`meiosis`	The number of meiosis separating the two isoaltes
`dist_cM`	The genetic map distance (cM) between SNP i and SNP j
`ibd_current`	The IBD state of SNP j
`ibd_previous`	The IBD state of SNP i

The transition probabilities for 1 haploid and 1 diploid chromosome

Description

The transition probabilities for 1 haploid and 1 diploid chromosome

Usage

transitionProbHD(omega_0, meiosis, dist_cM, ibd_current, ibd_previous)
transitionProbHD(omega_0, meiosis, dist_cM, ibd_current, ibd_previous)

Arguments

`omega_0`	The probability of sharing 0 alleles IBD
`meiosis`	The number of meiosis separating the two isoaltes
`dist_cM`	The genetic map distance (cM) between SNP i and SNP j
`ibd_current`	The IBD state of SNP j
`ibd_previous`	The IBD state of SNP i

The transition probabilities for 2 haploid chromosomes

Description

The transition probabilities for 2 haploid chromosomes

Usage

transitionProbHH(omega_0, meiosis, dist_cM, ibd_current, ibd_previous)
transitionProbHH(omega_0, meiosis, dist_cM, ibd_current, ibd_previous)

Arguments

`omega_0`	The probability of sharing 0 alleles IBD
`meiosis`	The number of meiosis separating the two isoaltes
`dist_cM`	The genetic map distance (cM) between SNP i and SNP j
`ibd_current`	The IBD state of SNP j
`ibd_previous`	The IBD state of SNP i

Matrices of all possible genotype combinations between pairs, given MOI

Description

Matrices of all possible genotype combinations between pairs, given MOI

Usage

trueGenotypes(gender_1, gender_2)
trueGenotypes(gender_1, gender_2)

Arguments

`gender_1`	The MOI estimate of isolate 1
`gender_2`	The MOI estimate of isolate 2

Package 'isoRelate'

Help Index

The matrix A in the equation Ax=b for 2 diploid chromosomes

Description

Usage

Arguments

The matrix A in the equation Ax=b for 1 haploid and 1 diploid chromosome

Description

Usage

Arguments

The matrix A in the equation Ax=b for 2 haploid chromosomes

Description

Usage

Arguments

Plasmodium Falciparum Gene Annotation Dataset

Description

Usage

Format

Internal function

Description

Usage

Arguments

The vector b in the equation Ax=b for 2 diploid chromosomes

Description

Usage

Arguments

The vector b in the equation Ax=b for 1 haploid chromosome and 1 diploid chromosome

Description

Usage

Arguments

The vector b in the equation Ax=b for 2 haploid chromosomes

Description

Usage

Arguments

Calculate alpha

Description

Usage

Arguments

Calculate beta

Description

Usage

Arguments

Calculate gamma

Description

Usage

Arguments

Calculate the log-likelihood of the data

Description

Usage

Arguments

Estimation of Meiosis

Description

Usage

Arguments

Value

Calculate Missingness Proportions

Description

Usage

Arguments

Value

Calculate Allele Frequencies for SNPs from Genotype Data

Description

Usage

Arguments

Calculate scale

Description

Usage

Arguments

Calculate the Viterbi sequence

Description

Usage

Arguments

A function that prints summary information of clusters

Description

Usage

Arguments

The emission probabilities for 2 diploid chromosomes

Description

Usage

Arguments

`getColourPaletteMinor()` generates a specified number of shades of a given colour.