Package 'polymapR'

Title: Linkage Analysis in Outcrossing Polyploids
Description: Creation of linkage maps in polyploid species from marker dosage scores of an F1 cross from two heterozygous parents. Currently works for outcrossing diploid, autotriploid, autotetraploid and autohexaploid species, as well as segmental allotetraploids. Methods are described in a manuscript of Bourke et al. (2018) <doi:10.1093/bioinformatics/bty371>. Since version 1.1.0, both discrete and probabilistic genotypes are acceptable input; for more details on the latter see Liao et al. (2021) <doi:10.1007/s00122-021-03834-x>.
Authors: Peter Bourke [aut, cre], Geert van Geest [aut], Roeland Voorrips [ctb], Yanlin Liao [ctb]
Maintainer: Peter Bourke <[email protected]>
License: GPL
Version: 1.1.6
Built: 2024-07-01 05:38:02 UTC
Source: https://github.com/cran/polymapR

Help Index


Add back duplicate markers after mapping

Description

Often there will be duplicate markers that can be put aside to speed up mapping. These may be added back to the maps afterwards.

Usage

add_dup_markers(maplist, bin_list, marker_assignments = NULL)

Arguments

maplist

A list of maps. Output of MDSMap_from_list.

bin_list

A list of marker bins containing marker duplicates. One of the list outputs of screen_for_duplicate_markers

marker_assignments

Optional argument to include the marker_assignments (output of check_marker_assignment). If included, marker assignment information will also be copied.

Value

A list with the following items:

maplist

List of maps, now with duplicate markers added

marker_assignments

If required, marker assignment list with duplicate markers added


A dosage matrix for a random pairing tetraploid with five linkage groups.

Description

A dosage matrix for a random pairing tetraploid with five linkage groups.

Usage

ALL_dosages

segregating_data

screened_data

screened_data2

screened_data3

TRI_dosages

Format

A matrix

An object of class matrix (inherits from array) with 2873 rows and 209 columns.

An object of class matrix (inherits from array) with 1417 rows and 209 columns.

An object of class matrix (inherits from array) with 1417 rows and 207 columns.

An object of class matrix (inherits from array) with 1417 rows and 200 columns.

An object of class matrix (inherits from array) with 250 rows and 202 columns.


A (nested) list of linkage data frames classified per linkage group and homologue

Description

A (nested) list of linkage data frames classified per linkage group and homologue

Usage

all_linkages_list_P1

all_linkages_list_P1_split

all_linkages_list_P1_subset

Format

An object of class list of length 5.

An object of class list of length 5.

An object of class list of length 5.


Assign non-SN markers to a linkage group and homologue(s).

Description

assign_linkage_group quantifies per marker number of linkages to a linkage group and evaluates to which linkage group (and homologue(s)) the marker belongs.

Usage

assign_linkage_group(
  linkage_df,
  LG_hom_stack,
  SN_colname = "marker_a",
  unassigned_marker_name = "marker_b",
  phase_considered = "coupling",
  LG_number,
  LOD_threshold = 3,
  ploidy,
  assign_homologue = T,
  log = NULL
)

Arguments

linkage_df

A linkage data.frame as output of linkage.

LG_hom_stack

A data.frame with markernames ("SxN_Marker"), linkage group ("LG") and homologue ("homologue")

SN_colname

The name of the column in linkage_df harbouring the 1.0 markers

unassigned_marker_name

The name of the column in linkage_df harbouring the marker that are to be assigned.

phase_considered

The phase that is used to assign the markers (deprecated)

LG_number

The number of chromosomes (linkage groups) in the species.

LOD_threshold

The LOD score at which a linkage to a linkage group is significant.

ploidy

The ploidy of the plant species.

assign_homologue

Logical. Should markers be assigned to homologues? If FALSE markers will be assigned to all homologues

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

Output is a data.frame with at least the following columns:

Assigned_LG

The assigned linkage group

Assigned_hom1

The homologue with most linkages

The columns LG1 - LGn and Hom1 - Homn give the number of hits per marker for that linkage group/homologue. Assigned_hom2 .. gives the nth homologue with most linkages.

Examples

data("SN_DN_P1", "LGHomDf_P1_1")
assigned_df<-assign_linkage_group(linkage_df = SN_DN_P1,
                     LG_hom_stack = LGHomDf_P1_1,
                     LG_number = 5, ploidy = 4)

Assign (leftover) 1.0 markers

Description

Some 1.0 markers might have had ambiguous linkages, or linkages with low LOD scores leaving them unlinked to a linkage group. assign_SN_SN finds 1.0 markers unlinked to a linkage group and tries to assign them.

Usage

assign_SN_SN(
  linkage_df,
  LG_hom_stack,
  LOD_threshold,
  ploidy,
  LG_number,
  log = NULL
)

Arguments

linkage_df

A data.frame as output of linkage with arguments markertype1=c(1,0) and markertype2=NULL.

LG_hom_stack

A data.frame with markernames ("SxN_Marker"), linkage group ("LG") and homologue ("homologue")

LOD_threshold

A LOD score at which linkages between markers are significant.

ploidy

Integer. The ploidy level of the plant species.

LG_number

Integer. Number of chromosomes (linkage groups)

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

Returns a data.frame with the following columns:

SxN_Marker

The markername

Assigned_hom1

The assigned homologue

Assigned_LG

The assigned linkage group

Examples

data("SN_SN_P1", "LGHomDf_P1_1")
SN_assigned<-assign_SN_SN(linkage_df = SN_SN_P1,
             LG_hom_stack = LGHomDf_P1_1,
             LOD_threshold= 4,
             ploidy=4,
             LG_number=5)

Use bridge markers to cluster homologues into linkage groups

Description

Clustering at high LOD scores results in marker clusters representing homologues. bridgeHomologues clusters these (pseudo)homologues to linkage groups using linkage information between 1.0 and bridge markers within a parent (e.g. 2.0 for a tetraploid). If parent-specific bridge markers (e.g. 2.0) cannot be used, biparental markers can also be used (e.g. 1.1, 1.2, 2.1, 2.2 and 1.3 markers). The linkage information between 1.0 and biparental markers can be combined.

Usage

bridgeHomologues(
  cluster_stack,
  cluster_stack2 = NULL,
  linkage_df,
  linkage_df2 = NULL,
  LOD_threshold = 5,
  automatic_clustering = TRUE,
  LG_number,
  parentname = "",
  min_links = 1,
  min_bridges = 1,
  only_coupling = FALSE,
  log = NULL
)

Arguments

cluster_stack

A data.frame with a column "marker" specifying markernames, and a column "cluster" specifying marker cluster

cluster_stack2

Optional. A cluster_stack for the other parent. Use this argument if cross-parent markers are used (e.g. when using 1.1 markers).

linkage_df

A linkage data.frame as output of linkage between bridge (e.g. 1.0 and 2.0) markers.

linkage_df2

Optional. A linkage_df specifying linkages between 1.0 and cross-parent markers in the other parent. Use this argument if cross-parent markers are used (e.g. when using 1.1, 2.1, 1.2 and/or 2.2 markers). The use of multiple types of cross-parent markers is allowed.

LOD_threshold

Integer. The LOD threshold specifying at which LOD score a link between 1.0 and bridging-type marker (e.g. 2.0) is used for clustering homologues.

automatic_clustering

Logical. Should clustering be executed without user input?

LG_number

Integer. Expected number of chromosomes (linkage groups)

parentname

Name of the parent. Used in the main title of the plot.

min_links

The minimum number of links between a bridge marker and a cluster for that bridge to be considered. In the case of a 2x0 marker for example, this argument means that the 2x0 marker must have at least min_links linkages of at least a LOD of LOD_threshold with markers from each of the clusters involved, to be considered a single bridging link. Make this number higher if there are a lot of spurious links.

min_bridges

The minimum number of bridge markers needed to assign two homologues together as coming from the same chromosomal linkage group. See argument min_links for further details.

only_coupling

Logical, should only coupling linkages be used in the process? By default FALSE

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A data.frame with markers classified by homologue and linkage group.

Examples

data("P1_homologues", "P2_homologues", "SN_DN_P1", "SN_SS_P1", "SN_SS_P2")
ChHomDf<-bridgeHomologues(cluster_stack = P1_homologues[["5"]],
                 linkage_df=SN_DN_P1,
                 LOD_threshold=4,
                 automatic_clustering=TRUE,
                 LG_number=5,
                 parentname="P1")

ChHomDf<-bridgeHomologues(cluster_stack = P1_homologues[["5"]],
                           cluster_stack2 = P2_homologues[["5"]],
                 linkage_df=SN_SS_P1,
                 linkage_df2=SN_SS_P2,
                 LOD_threshold=4,
                 automatic_clustering=TRUE,
                 LG_number=5,
                 parentname="P1")

Build a list of segregation types

Description

For each possible segregation type in an F1 progeny with given parental ploidy (and ploidy2, if parent2 has a different ploidy than parent1) information is given on the segregation ratios, parental dosages and whether the segregation is expected under polysomic, disomic and/or mixed inheritance.

Usage

calcSegtypeInfo(ploidy, ploidy2=NULL)

Arguments

ploidy

The ploidy of parent 1 (must be even, 2 (diploid) or larger).

ploidy2

The ploidy of parent 2. If omitted (default=NULL) it is assumed to be equal to ploidy.

Details

The names of the segregation types consist of a short sequence of digits (and sometimes letters), an underscore and a final number. This is interpreted as follows, for example segtype 121_0: 121 means that there are three consecutive dosages in the F1 population with frequency ratios 1:2:1, and the 0 after the underscore means that the lowest of these dosages is nulliplex. So 121_0 means a segregation of 1 nulliplex : 2 simplex : 1 duplex. A monomorphic F1 (one single dosage) is indicated as e.g. 1_4 (only one dosage, the 4 after the underscore means that this is monomorphic quadruplex). If UPPERCASE letters occur in the first part of the name these are interpreted as additional digits with values of A=10 to Z=35, e.g. 18I81_0 means a segregation of 1:8:18:8:1 (using the I as 18), with the lowest dosage being nulliplex.
With higher ploidy levels higher numbers (above 35) may be required. In that case each unique ratio number above 35 is assigned a lowercase letter. E.g. one segregation type in octaploids is 9bcb9_2: a 9:48:82:48:9 segregation where the lowest dosage is duplex.
Segregation types with more than 5 dosage classes are considered "complex" and get codes like c7e_1 (again in octoploids): this means a complex type (the first c) with 7 dosage classes; the e means that this is the fifth type with 7 classes. Again the _1 means that the lowest dosage is simplex. It is always possible (and for all segtype names with lowercase letters it is necessary) to look up the actual segregation ratios in the intratio item of the segtype. For octoploid segtype c7e_1 this shows 0:1:18:69:104:69:18:1:0 (the two 0's mean that nulli- and octoplexes do not occur).

Value

A list with for each different segregation type (segtype) one item. The names of the items are the names of the segtypes. Each item is itself a list with components:

freq

A vector of the ploidy+1 fractions of the dosages in the F1

intratios

An integer vector with the ratios as the simplest integers

expgeno

A vector with the dosages present in this segtype

allfrq

The allele frequency of the dosage allele in the F1

polysomic

Boolean: does this segtype occur with polysomic inheritance?

disomic

Boolean: does this segtype occur with disomic inheritance?

mixed

Boolean: does this segtype occur with mixed inheritance (i.e. with polysomic inheritance in one parent and disomic inheritance in the other)?

pardosage

Integer matrix with 2 columns and as many rows as there are parental dosage combinations for this segtype; each row has one possible combination of dosages for parent 1 (1st column) and parent 2 (2nd column)

parmode

Logical matrix with 3 columns and the same number of rows as pardosage. The 3 columns are named polysomic, disomic and mixed and tell if this parental dosage combination will generate this segtype under polysomic, disomic and mixed inheritance

Examples

si4 <- calcSegtypeInfo(ploidy=4) # two 4x parents: a 4x F1 progeny
print(si4[["11_0"]])

si3 <- calcSegtypeInfo(ploidy=4, ploidy2=2) # a 4x and a diplo parent: a 3x progeny
print(si3[["11_0"]])

Check the quality of a linkage map

Description

Perform a series of checks on a linkage map and visualise the results using heatplots. The difference between the pairwise and multi-point r estimates are also plotted against the LOD of the pairwise estimate. The weighted root mean square error of these differences (weighted by the LOD scores) is printed on the console.

Usage

check_map(
  linkage_list,
  maplist,
  mapfn = "haldane",
  lod.thresh = 5,
  detail = 1,
  plottype = c("", "pdf", "png")[1],
  prefix = ""
)

Arguments

linkage_list

A named list with r and LOD of markers within linkage groups.

maplist

A list of maps. In the first column marker names and in the second their position.

mapfn

The map function used in generating the maps, either one of "haldane" or "kosambi". By default "haldane" is assumed.

lod.thresh

Numeric. Threshold for the LOD values to be displayed in heatmap, by default 5 (set at 0 to display all values)

detail

Level of detail for heatmaps, by default 1 cM. Values less than 0.5 cM can have serious performance implications.

plottype

Option to specify graphical device for plotting, (either png or pdf), or by default "", in which case plots are directly plotted within R

prefix

Optional prefix appended to plot names if outputting plots.

Examples

## Not run: 
data("maplist_P1","all_linkages_list_P1")
check_map(linkage_list = all_linkages_list_P1, maplist = maplist_P1)

## End(Not run)

Check for consistent marker assignment between both parents

Description

Function to ensure there is consistent marker assignment to chromosomal linkage groups for biparental markers

Usage

check_marker_assignment(
  marker_assignment.P1,
  marker_assignment.P2,
  log = NULL,
  verbose = TRUE
)

Arguments

marker_assignment.P1

A marker assignment matrix for parent 1 with markernames as rownames and at least containing the column "Assigned_LG"; the output of homologue_lg_assignment.

marker_assignment.P2

A marker assignment matrix for parent 2 with markernames as rownames and at least containing the column "Assigned_LG"; the output of homologue_lg_assignment.

log

Character string specifying the log filename to which standard output should be written. If NULL (by default) log is send to stdout.

verbose

Should messages be sent to stdout or log?

Value

Returns a list of matrices with corrected marker assignments.

Examples

data("marker_assignments_P1"); data("marker_assignments_P2")
check_marker_assignment(marker_assignments_P1,marker_assignments_P2)

check your dataset's maxP distribution

Description

Function to assess the distribution of maximum genotype probabilities (maxP), if these are available. The function plots a violin graph showing the distribution of the samples' maxP.

Usage

check_maxP(probgeno_df)

Arguments

probgeno_df

A data frame as read from the scores file produced by function saveMarkerModels of R package fitPoly, or alternatively, a data frame containing the following columns:

SampleName

Name of the sample (individual)

MarkerName

Name of the marker

P0

Probabilities of dosage score '0'

P1...

Probabilities of dosage score '1' etc. (up to max dosage, e.g. P4 for tetraploid population)

maxP

Maximum genotype probability identified for a particular individual and marker combination

maxgeno

Most probable dosage for a particular individual and marker combination

geno

Most probable dosage for a particular individual and marker combination, if maxP exceeds a user-defined threshold (e.g. 0.9), otherwise NA

Value

This function does not return any value, is simply a visualisation tool to help assess data quality.

Examples

data("gp_df")
check_maxP(gp_df)

Identify the best-fitting F1 segregation types

Description

For a given set of F1 and parental samples, this function finds the best-fitting segregation type using either discrete or probabilistic input data. It can also perform a dosage shift prior to selecting the segregation type.

Usage

checkF1(
  input_type = "discrete",
  dosage_matrix,
  probgeno_df,
  parent1,
  parent2,
  F1,
  ancestors = character(0),
  polysomic,
  disomic,
  mixed,
  ploidy,
  ploidy2,
  outfile = "",
  critweight = c(1, 0.4, 0.4),
  Pvalue_threshold = 1e-04,
  fracInvalid_threshold = 0.05,
  fracNA_threshold = 0.25,
  shiftmarkers,
  parentsScoredWithF1 = TRUE,
  shiftParents = parentsScoredWithF1,
  showAll = FALSE,
  append_shf = FALSE
)

Arguments

input_type

Can be either one of 'discrete' or 'probabilistic'. For the former (default), a dosage_matrix must be supplied, while for the latter a probgeno_df must be supplied.

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

probgeno_df

A data frame as read from the scores file produced by function saveMarkerModels of R package fitPoly, or alternatively, a data frame containing the following columns:

SampleName

Name of the sample (individual)

MarkerName

Name of the marker

P0

Probabilities of dosage score '0'

P1...

Probabilities of dosage score '1' etc. (up to max dosage, e.g. P4 for tetraploid population)

maxP

Maximum genotype probability identified for a particular individual and marker combination

maxgeno

Most probable dosage for a particular individual and marker combination

geno

Most probable dosage for a particular individual and marker combination, if maxP exceeds a user-defined threshold (e.g. 0.9), otherwise NA

parent1

character vector with the sample names of parent 1

parent2

character vector with the sample names of parent 2

F1

character vector with the sample names of the F1 individuals

ancestors

character vector with the sample names of any other ancestors or other samples of interest. The dosages of these samples will be shown in the output (shifted if shiftParents TRUE) but they are not used in the selection of the segregation type.

polysomic

if TRUE at least all polysomic segtypes are considered; if FALSE these are not specifically selected (but if e.g. disomic is TRUE, any polysomic segtypes that are also disomic will still be considered)

disomic

if TRUE at least all disomic segtypes are considered (see polysomic)

mixed

if TRUE at least all mixed segtypes are considered (see polysomic). A mixed segtype occurs when inheritance in one parent is polysomic (random chromosome pairing) and in the other parent disomic (fully preferential chromosome pairing)

ploidy

The ploidy of parent 1 (must be even, 2 (diploid) or larger).

ploidy2

The ploidy of parent 2. If omitted it is assumed to be equal to ploidy.

outfile

the tab-separated text file to write the output to; if NA a temporary file checkF1.tmp is created in the current working directory and deleted at end

critweight

NA or a numeric vector containing the weights of three quality criteria; do not need to sum to 1. If NA, the output will not contain a column qall_weights. Else the weights specify how qall_weights will be calculated from quality parameters q1, q2 and q3.

Pvalue_threshold

a minimum threshold value for the Pvalue of the bestParentfit segtype (with a smaller Pvalue the q1 quality parameter will be set to 0)

fracInvalid_threshold

a maximum threshold for the fracInvalid of the bestParentfit segtype (with a larger fraction of invalid dosages in the F1 the q1 quality parameter will be set to 0)

fracNA_threshold

a maximum threshold for the fraction of unscored F1 samples (with a larger fraction of unscored samples in the F1 the q3 quality parameter will be set to 0)

shiftmarkers

if specified, shiftmarkers must be a data frame with columns MarkerName and shift; for the markernames that match exactly (upper/lowercase etc) those in the input (either dosage_matrix or probgeno_df), the dosages are increased by the amount specified in column shift, e.g. if shift is -1, dosages 2..ploidy are converted to 1..(ploidy-1) and dosage 0 is a combination of old dosages 0 and 1, for all samples. The segregation check is then performed with the shifted dosages. A shift=NA is allowed, these markers will not be shifted. The sets of markers in the input (either dosage_matrix or probgeno_df) and shiftmarkers may be different, but markers may occur only once in shiftmarkers. A column shift is added at the end of the returned data frame.
If parameter shiftParents is TRUE, the parental and ancestor scores are shifted as the F1 scores, if FALSE they are not shifted.

parentsScoredWithF1

TRUE if parents are scored in the same experiment and the same fitPoly run as the F1, else FALSE. If TRUE, their fraction missing scores and conflicts tell something about the quality of the scoring. If FALSE (e.g. when the F1 is triploid and the parents are diploid and tetraploid) the quality of the F1 scores can be independent of that of the parents.
If not specified, TRUE is assumed if ploidy2 == ploidy and FALSE if ploidy2 != ploidy

shiftParents

only used if parameter shiftmarkers is specified. If TRUE, apply the shifts also to the parental and ancestor scores. By default TRUE if parentsScoredWithF1 is TRUE

showAll

(default FALSE) if TRUE, for each segtype 3 columns are added to the returned data frame with the frqInvalid, Pvalue and matchParents values for these segtype (see the description of the return value)

append_shf

if TRUE and parameter shiftmarkers is specified, _shf is appended to all marker names where shift is not 0. This is not required for any of the functions in this package but may prevent duplicated marker names when using other software.

Details

For each marker is tested how well the different segregation types fit with the observed parental and F1 dosages. The results are summarized by columns bestParentfit (which is the best fitting segregation type, taking into account the F1 and parental dosages) and columns qall_mult and/or qall_weights (how good is the fit of the bestParentfit segtype: 0=bad, 1=good).
Column bestfit in the results gives the segtype best fitting the F1 segregation without taking account of the parents. This bestfit segtype is used by function correctDosages, which tests for possible "shifts" in the marker models.
In case the parents are not scored together with the F1 (e.g. if the F1 is triploid and the parents are diploid and tetraploid) dosage_matrix should be edited to contain the parental as well as the F1 scores. In case the diploid and tetraploid parent are scored in the same run of function saveMarkerModels (from package fitPoly) the diploid is initially scored as nulliplex-duplex-quadruplex (dosage 0, 2 or 4); that must be converted to the true diploid dosage scores (0, 1 or 2). Similar corrections are needed with other combinations, such as a diploid parent scored together with a hexaploid population etc.

Value

A list containing two elements, checked_F1 and meta. meta is itself a list that stores the parameter settings used in running checkF1 which can be useful for later reference. The first element (checked_F1) contains the actual results: a data frame with one row per marker, with the following columns:

  • m: the sequential number of the marker (as assigned by fitPoly)

  • MarkerName: the name of the marker, with _shf appended if the marker is shifted and append_shf is TRUE

  • parent1: consensus dosage score of the samples of parent 1

  • parent2: consensus dosage score of the samples of parent 2

  • F1_0 ... F1_<ploidy>: the number of F1 samples with dosage scores 0 ... <ploidy>

  • F1_NA: the number of F1 samples with a missing dosage score

  • sample names of parents and ancestors: the dosage scores for those samples

  • bestfit: the best fitting segtype, considering only the F1 samples

  • frqInvalid_bestfit: for the bestfit segtype, the frequency of F1 samples with a dosage score that is invalid (that should not occur). The frequency is calculated as the number of invalid samples divided by the number of non-NA samples

  • Pvalue_bestfit: the chisquare test P-value for the observed distribution of dosage scores vs the expected fractions. For segtypes where only one dosage is expected (1_0, 1_1 etc) the binomial probability of the number of invalid scores is given, assuming an error rate of seg_invalidrate (hard-coded as 0.03)

  • matchParent_bestfit: indication how the bestfit segtype matches the consensus dosages of parent 1 and 2: "Unknown"=both parental dosages unknown; "No"=one or both parental dosages known and conflicting with the segtype; "OneOK"= only one parental dosage known, not conflicting with the segtype; "Yes"=both parental dosages known and combination matching with the segtype. This score is initially assigned based on only high-confidence parental consensus scores; if low-confidence dosages are confirmed by the F1, the matchParent for (only) the selected segtype is updated, as are the parental consensus scores.

  • bestParentfit: the best fitting segtype that does not conflict with the parental consensus scores

  • frqInvalid_bestParentfit, Pvalue_bestParentfit, matchParent_bestParentfit: same as the corresponding columns for bestfit. Note that matchParent_bestParentfit cannot be "No".

  • q1_segtypefit: a value from 0 (bad) to 1 (good), a measure of the fit of the bestParentfit segtype based on Pvalue, invalidP and whether bestfit is equal to bestParentfit

  • q2_parents: a value from 0 (bad) to 1 (good), based either on the quality of the parental scores (the number of missing scores and of conflicting scores, if parentsScoredWithF1 is TRUE) or on matchParents (No=0, Unknown=0.65, OneOK=0.9, Yes=1, if parentsScoredWithF1 is FALSE)

  • q3_fracscored: a value from 0 (bad) to 1 (good), based on the fraction of F1 samples that have a non-missing dosage score

  • qall_mult: a value from 0 (bad) to 1 (good), a summary quality score equal to the product q1*q2*q3. Equal to 0 if any of these is 0, hence sensitive to thresholds; a natural selection criterion would be to accept all markers with qall_mult > 0

  • qall_weights: a value from 0 (bad) to 1 (good), a weighted average of q1, q2 and q3, with weights as specified in parameter critweight. This column is present only if critweight is specified. In this case there is no "natural" threshold; a threshold for selection of markers must be obtained by inspecting XY-plots of markers over a range of qall_weights values

  • shift: if shiftmarkers is specified a column shift is added with for all markers the applied shift (for the unshifted markers the shift value is 0)

qall_mult and/or qall_weights can be used to compare the quality of the SNPs within one analysis and one F1 population but not between analyses or between different F1 populations.
If parameter showAll is TRUE there are 3 additional columns for each segtype with names frqInvalid_<segtype>, Pvalue_<segtype> and matchParent_<segtype>; see the corresponding columns for bestfit for an explanation. These extra columns are inserted directly before the bestfit column.

Examples

## Not run: 
data("ALL_dosages")
chk1<-checkF1(input_type="discrete",dosage_matrix=ALL_dosages,parent1="P1",parent2="P2",
F1=setdiff(colnames(ALL_dosages),c("P1","P2")),polysomic=T,disomic=F,mixed=F,
ploidy=4)
data("gp_df")
chk1<-checkF1(input_type="probabilistic",probgeno_df=gp_df,parent1="P1",parent2="P2",
F1=setdiff(levels(gp_df$SampleName),c("P1","P2")),polysomic=T,disomic=F,mixed=F,
ploidy=4)

## End(Not run)

Example output of the checkF1 function

Description

Example output of the checkF1 function

Usage

chk1

Format

An object of class list of length 2.


Cluster 1.0 markers into correct homologues per linkage group

Description

Clustering at one LOD score for all markers does usually not result in correct classification of homologues. Usually there are more clusters of (pseudo)homologues than expected. This function lets you inspect every linkage group separately and allows for clustering at a different LOD threshold per LG.

Usage

cluster_per_LG(
  LG,
  linkage_df,
  LG_hom_stack,
  LOD_sequence,
  modify_LG_hom_stack = FALSE,
  nclust_out = NULL,
  network.layout = c("circular", "stacked", "n"),
  device = NULL,
  label.offset = 1,
  cex.lab = 0.7,
  log = NULL,
  ...
)

Arguments

LG

Integer. Linkage group to investigate.

linkage_df

A data.frame as output of linkage with arguments markertype1 = c(1,0) and markertype2=NULL.

LG_hom_stack

A data.frame with columns "SxN_Marker" providing 1.0 markernames and "LG" and "homologue" providing linkage group and homologue respectively.

LOD_sequence

A numeric or vector of numerics giving LOD threshold(s) at which clustering should be performed.

modify_LG_hom_stack

Logical. Should LG_hom_stack be modified and returned?

nclust_out

Number of clusters in the output. If there are more clusters than this number only the nclust_out largest clusters are returned.

network.layout

Network layout: "circular" or "stacked". If "n" no network is plotted.

device

Function of the graphics device to plot to (e.g. pdf, png, jpeg). The active device is used when NULL

label.offset

Offset of labels. Only used if network.layout="circular".

cex.lab

label character expansion. Only for network.layout="circular".

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

...

Arguments passed to device.

Value

A modified LG_hom_stack data.frame if modify_LG_hom_stack = TRUE

Examples

data("SN_SN_P2", "LGHomDf_P2_1")
#take only markers in coupling:
SN_SN_P2_coupl <- SN_SN_P2[SN_SN_P2$phase=="coupling",]
cluster_per_LG(LG = 2,
               linkage_df=SN_SN_P2_coupl,
               LG_hom_stack=LGHomDf_P2_1,
               LOD_sequence=seq(4,10,2),
               modify_LG_hom_stack=FALSE,
               nclust_out=4,
               network.layout="circular",
               device=NULL,
               label.offset=1.2,
               cex.lab=0.75)

Cluster 1.0 markers

Description

cluster_SN_markers clusters simplex nulliplex at different LOD scores.

Usage

cluster_SN_markers(
  linkage_df,
  LOD_sequence = 7,
  independence_LOD = FALSE,
  LG_number,
  ploidy,
  parentname = "",
  plot_network = FALSE,
  min_clust_size = 1,
  plot_clust_size = TRUE,
  max_vertex_size = 5,
  min_vertex_size = 2,
  phase_considered = "All",
  log = NULL
)

Arguments

linkage_df

A linkage data.frame as output of linkage calculating linkage between 1.0 markers.

LOD_sequence

A numeric vector. Specifying a sequence of LOD thresholds at which clustering is performed.

independence_LOD

Logical. Should the LOD of independence be used for clustering? (by default, FALSE.)

LG_number

Expected number of chromosomes (linkage groups)

ploidy

Ploidy level of the parent for which clustering is to be performed

parentname

Name of parent

plot_network

Logical. Should a network be plotted. Recommended FALSE with large number of marker combinations.

min_clust_size

Integer. The minimum cluster size to be returned. By default, a minimum cluster size of 1 is used, meaning all markers are returned. Setting this to a higher number can be useful for cleaning out mini-clusters that don't show strong linkage to the rest of the marker set.

plot_clust_size

Logical. Should exact cluster size be plotted as vertex labels?

max_vertex_size

Integer. The maximum vertex size. Only used if plot_clust_size=FALSE.

min_vertex_size

Integer. The minimum vertex size. Only used if plot_clust_size=FALSE.

phase_considered

Character string. By default all phases are used, but "coupling" or "repulsion" are also allowed.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout (console).

Value

A (named) list of cluster stacks, each of which is a data.frame with columns "marker" and "cluster"

Examples

data("SN_SN_P1")
cluster_list<-cluster_SN_markers(SN_SN_P1,LOD_sequence=c(4:10),parentname="P1",ploidy=4,LG_number=5)

Compare linkage maps, showing links between connecting markers common to neighbouring maps

Description

This function allows the visualisation of connections between different maps, showing them side by side.

Usage

compare_maps(
  maplist,
  chm.wd = 0.2,
  bg.col = "white",
  links.col = "grey42",
  thin.links = NULL,
  type = "karyotype",
  ...
)

Arguments

maplist

A list of maps. This is probably most conveniently built on-the-fly in the function call itself. If names are assigned to different maps (list items) these will appear above the maps. In cases of multiple comparisons, for example comparing 1 map of interest to 3 others, the map of interest can be supplied multiple times in the list, interspersed between the other maps. See the example below for details.

chm.wd

The width in inches that linkage groups should be drawn. By default 0.2 inches is used.

bg.col

The background colour of the maps, by default white. It can be useful to use a different background colour for the maps. In this case, supply bg.col as a vector of colour identifiers, with the same length as maplist and corresponding to its elements in the same order. See the example below for details.

links.col

The colour with which links between maps are drawn, by default grey.

thin.links

Option to thin the plotting of links between maps, which might be useful if there are very many shared markers in a small genetic region. By default NULL, otherwise supply a value (in cM) for the minimum genetic distance between linking-lines (e.g. 0.5).

type

Plot type, by default "karyotype". If "scatter" is requested a scatter plot is drawn, but only if the comparison is between 2 maps.

...

option to supply arguments to the plot function (e.g. main = to add a title to the plot)

Value

NULL

Examples

data("map1","map2","map3")
compare_maps(maplist=list("1a"=map1,"c08"=map2,"1b"=map3),bg.col=c("thistle","white","skyblue"))

Consensus LG assignment

Description

Assign markers to an LG based on consensus between two parents.

Usage

consensus_LG_assignment(
  P1_assigned,
  P2_assigned,
  LG_number,
  ploidy,
  consensus_file = NULL,
  log = NULL
)

Arguments

P1_assigned

A marker assignment file of the first parent. Should contain the number of linkages per LG per marker.

P2_assigned

A marker assignment file of the second parent. Should be the same markertype as first parent and contain the number of linkages per LG per marker.

LG_number

Number of linkage groups (chromosomes).

ploidy

Ploidy level of plant species.

consensus_file

Filename of consensus output. No output is written if NULL.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

Returns a list containing the following components:

P1_assigned

A (modified) marker assignment matrix of the first parent.

P2_assigned

A (modified) marker assignment matrix of the second parent.

Examples

data("P1_SxS_Assigned", "P2_SxS_Assigned_2")
SxS_Assigned_list <- consensus_LG_assignment(P1_SxS_Assigned,P2_SxS_Assigned_2,5,4)

Find consensus linkage group names

Description

Chromosomes that should have same number, might have gotten different numbers between parents during clustering. consensus_LG_names uses markers present in both parents (usually 1.1 markers) to modify the linkage group numbers in one parent with the other as template

Usage

consensus_LG_names(
  modify_LG,
  template_SxS,
  modify_SxS,
  merge_LGs = TRUE,
  log = NULL
)

Arguments

modify_LG

A data.frame with markernames, linkage group ("LG") and homologue ("homologue"), in which the linkage group numbers will be modified

template_SxS

A file with assigned markers of which (at least) part is present in both parents of the template parent.

modify_SxS

A file with assigned markers of which (at least) part is present in both parents of the parent of which linkage group number are modified.

merge_LGs

Logical, by default TRUE. If FALSE, any discrepency in the number of linkage groups will not be merged, but removed instead. This can be needed if the number of chromosomes identified is not equal between parents, and the user wishes to proceed with a core set.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A modified modified_LG according to the template_SxS linkage group numbering

Examples

data("LGHomDf_P2_2", "P1_SxS_Assigned", "P2_SxS_Assigned")
consensus_LGHomDf<-consensus_LG_names(LGHomDf_P2_2, P1_SxS_Assigned, P2_SxS_Assigned)

Convert marker dosages to the basic types.

Description

Convert marker dosages to the basic types which hold the same information and for which linkage calculations can be performed.

Usage

convert_marker_dosages(
  dosage_matrix,
  ploidy,
  ploidy2 = NULL,
  parent1 = "P1",
  parent2 = "P2",
  marker_conversion_info = FALSE,
  log = NULL
)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

ploidy

ploidy level of the plant species. If parents have different ploidy level, ploidy of parent1.

ploidy2

ploidy level of the second parent. NULL if both parents have the same ploidy level.

parent1

Character string specifying the first (usually maternal) parentname.

parent2

Character string specifying the second (usually paternal) parentname.

marker_conversion_info

Logical, by default FALSE. Should marker conversion information be returned? This output can be useful for later map phasing step, if original marker coding is desired (which is most likely the case).

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A modified dosage matrix. If marker_conversion_info = TRUE, this function returns a list, with both the converted dosage_matrix, and information on the marker conversions performed per marker.

Examples

data("ALL_dosages")
conv<-convert_marker_dosages(dosage_matrix=ALL_dosages, ploidy = 4)

Convert (probabilistic) genotype calling results from polyRAD to input compatible with polymapR

Description

Convert (probabilistic) genotype calling results from polyRAD to input compatible with polymapR

Usage

convert_polyRAD(RADdata)

Arguments

RADdata

An RADdata (S3 class) object; output of the function PipelineMapping2Parents having followed the prior steps needed in the polyRAD pipeline. See the polyRAD vignette for details.

Value

A data frame which include columns: MarkerName, SampleName,P0 ~ Pploidy (e.g. P0 ~ P4 for tetraploid, which represents the probability assigning to this dosage), maxgeno (the most likely dosage), and maxP (the maximum probability)

Examples

data("exampleRAD_mapping")
convert_polyRAD(RADdata = exampleRAD_mapping)

Convert (probabilistic) genotype calling results from updog to input compatible with polymapR.

Description

Convert (probabilistic) genotype calling results from updog to input compatible with polymapR.

Usage

convert_updog(mout, output_type = "discrete", min_prob = 0.7)

Arguments

mout

An object of class multidog; output of the function multidog.

output_type

Output genotypes can be either "discrete" or "probabilistic", defaults to discrete.

min_prob

If genotypes are being discretised, sets the minimum posterior probability in order to call a genotype with confidence. If maxpostprob < min_prob, that genotype is made missing. A default of 0.7 is suggested with no particular motivation.

Value

If output_type is discrete, the function returns a dosage matrix with rownames given by marker names. Columns are organised as parent 1 genotype, parent 2 genotype and then F1 individuals. If output_type is probabilistic, then the output is a data frame which include columns: MarkerName, SampleName,P0 ~ Pploidy (e.g. P0 ~ P4 for tetraploid, which represents the probability assigning to this dosage), maxgeno (the most likely dosage), and maxP (the maximum probability)

Examples

data("mout")
convert_updog(mout)

Check if dosage scores may have to be shifted

Description

fitPoly sometimes uses a "shifted" model to assign dosage scores (e.g. all samples are assigned a dosage one higher than the true dosage). This happens mostly when there are only few dosages present among the samples. This function checks if a shift of +/-1 is possible.

Usage

correctDosages(chk, dosage_matrix, parent1, parent2, ploidy,
polysomic=TRUE, disomic=FALSE, mixed=FALSE,
absent.threshold=0.04)

Arguments

chk

data frame returned by function checkF1 when called without shiftmarkers

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

parent1

character vector with names of the samples of parent 1

parent2

character vector with names of the samples of parent 2

ploidy

ploidy of parents and F1 (correctDosages must not be used for F1 populations where the parents have a different ploidy, or where the parental genotypes are not scored together with the F1); same as used in the call to checkF1 that generated data.frame chk

polysomic

if TRUE at least all polysomic segtypes are considered; if FALSE these are not specifically selected (but if e.g. disomic is TRUE, any polysomic segtypes that are also disomic will still be considered); same as used in the call to checkF1 that generated data.frame chk

disomic

if TRUE at least all disomic segtypes are considered (see param polysomic); same as used in the call to checkF1 that generated data.frame chk

mixed

if TRUE at least all mixed segtypes are considered (see param polysomic). A mixed segtype occurs when inheritance in one parent is polysomic (random chromosome pairing) and in the other parent disomic (fully preferential chromosome pairing); same as used in the call to checkF1 that generated data.frame chk

absent.threshold

the threshold for the fraction of ALL samples that has the dosage that is assumed to be absent due to mis-fitting of fitPoly; should be at least the assumed error rate of the fitPoly scoring assuming the fitted model is correct

Details

A shift of -1 (or +1) is proposed when (1) the fraction of all samples with dosage 0 (or ploidy) is below absent.threshold, (2) the bestfit (not bestParentfit!) segtype in chk has one empty dosage on the low (or high) side and more than one empty dosage at the high (or low) side, and (3) the shifted consensus parental dosages do not conflict with the shifted segregation type.
The returned data.frame (or a subset, e.g. based on the values in the fracNotOk and parNA columns) can serve as parameter shiftmarkers in a new call to checkF1.
Based on the quality scores assigned by checkF1 to the original and shifted versions of each marker the user can decide if either or both should be kept. A data.frame combining selected rows of the original and shifted versions of the checkF1 output (which may contain both a shifted and an unshifted version of some markers) can then be used as input to compareProbes or writeDosagefile.

Value

a data frame with columns

  • markername

  • segtype: the bestfit (not bestParentfit!) segtype from chk

  • parent1, parent2: the consensus parental dosages; possibly low-confidence, so may be different from those reported in chk

  • shift: -1, 0 or 1: the amount by which this marker should be shifted

The next fields are only calculated if shift is not 0:

  • fracNotOk: the fraction of ALL samples that are in the dosage (0 or ploidy) that should be empty if the marker is indeed shifted.

  • parNA: the number of parental dosages that is missing (0, 1 or 2)


Create a phased homologue map list using the original dosages

Description

create_phased_maplist is a function for creating a phased maplist, using integrated map positions and original marker dosages.

Usage

create_phased_maplist(
  input_type = "discrete",
  maplist,
  dosage_matrix.conv,
  dosage_matrix.orig = NULL,
  probgeno_df,
  chk,
  remove_markers = NULL,
  original_coding = FALSE,
  N_linkages = 2,
  lower_bound = 0.05,
  ploidy,
  ploidy2 = NULL,
  marker_assignment.1,
  marker_assignment.2,
  parent1 = "P1",
  parent2 = "P2",
  marker_conversion_info = NULL,
  log = NULL,
  verbose = TRUE
)

Arguments

input_type

Can be either one of 'discrete' or 'probabilistic'. For the former (default), at least dosage_matrix.conv must be supplied, while for the latter chk must be supplied.

maplist

A list of maps. In the first column marker names and in the second their position.

dosage_matrix.conv

Matrix of marker dosage scores with markers in rows and individuals in columns. Note that dosages must be in converted form, i.e. after having run the convert_marker_dosages function. Errors may result otherwise.

dosage_matrix.orig

Optional, by default NULL.The unconverted dosages (i.e. raw dosage data before using the convert_marker_dosages function). Required if original_coding is TRUE.

probgeno_df

Probabilistic genotypes, for description see e.g. gp_overview. Required if probabilistic genotypes are used.

chk

Output list as returned by function checkF1. Required if probabilistic genotypes are used.

remove_markers

Optional vector of marker names to remove from the maps. Default is NULL.

original_coding

Logical. Should the phased map use the original marker coding or not? By default FALSE.

N_linkages

Number of significant linkages (as defined in homologue_lg_assignment) required for high-confidence linkage group assignment.

lower_bound

Numeric. Lower bound for the rate at which homologue linkages (fraction of total for that marker) are recognised.

ploidy

Integer. Ploidy of the organism.

ploidy2

Optional integer, by default NULL. Ploidy of parent 2, if different from parent 1.

marker_assignment.1

A marker assignment matrix for parent 1 with markernames as rownames and at least containing the column "Assigned_LG".

marker_assignment.2

A marker assignment matrix for parent 2 with markernames as rownames and at least containing the column "Assigned_LG".

parent1

character vector with names of the samples of parent 1

parent2

character vector with names of the samples of parent 2

marker_conversion_info

One of the list elements (named 'marker_conversion_info') generated by the function convert_marker_dosages when the argument marker_conversion_info was set to TRUE (not the default, so a user will typically have to re-run this step first). Required if original_coding is TRUE.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

verbose

Logical, by default TRUE. Should details of the phasing process be given?

Examples

## Not run: 
data("integrated.maplist", "screened_data3", "marker_assignments_P1","marker_assignments_P2")
create_phased_maplist(maplist = integrated.maplist,
                     dosage_matrix.conv = screened_data3,
                     marker_assignment.1=marker_assignments_P1,
                     marker_assignment.2=marker_assignments_P2,
                     ploidy = 4)
## End(Not run)

Create input files for TetraOrigin using an integrated linkage map list and marker dosage matrix

Description

createTetraOriginInput is a function for creating an input file for TetraOrigin, combining map positions with marker dosages.

Usage

createTetraOriginInput(
  maplist,
  dosage_matrix,
  bin_size = NULL,
  bounds = NULL,
  remove_markers = NULL,
  outdir = "TetraOrigin",
  output_stem = "TetraOrigin_input",
  plot_maps = TRUE,
  log = NULL
)

Arguments

maplist

A list of maps. In the first column marker names and in the second their position.

dosage_matrix

An integer matrix with markers in rows and individuals in columns. Either provide the unconverted dosages (i.e. before using the convert_marker_dosages function), or converted dosages (i.e. screened data), in matrix form. The analysis and results are unaffected by this choice, but it may be simpler to understand the results if converted dosages are used. Conversely, it may be advantageous to use the original unconverted dosages if particular marker alleles are being tracked for (e.g.) the development of selectable markers afterwards.

bin_size

Numeric. Size (in cM) of the bins to include. If NULL (by default) then all markers are used (no binning).

bounds

Numeric vector. If NULL (by default) then all positions are included, however if specified then output is limited to a specific region, which is useful for later fine-mapping work.

remove_markers

Optional vector of marker names to remove from the maps. Default is NULL.

outdir

Output directory to which input files for TetraOrigin are written.

output_stem

Character prefix to add to the .csv output filename.

plot_maps

Logical. Plot the marker positions of the selected markers using plot_map.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Examples

## Not run: 
data("integrated.maplist","ALL_dosages")
createTetraOriginInput(maplist=integrated.maplist,dosage_matrix=ALL_dosages,bin_size=10)
## End(Not run)

Generate linkage group and homologue structure of SxN markers

Description

Function which organises the output of cluster_SN_markers into a data frame of numbered linkage groups and homologues. Only use this function if it is clear from the graphical output of cluster_SN_markers that there are LOD scores present which define both chromosomes (lower LOD) and homologues (higher LOD).

Usage

define_LG_structure(cluster_list, LOD_chm, LOD_hom, LG_number, log = NULL)

Arguments

cluster_list

A list of cluster_stacks, the output of cluster_SN_markers.

LOD_chm

Integer. The LOD threshold specifying at which LOD score the markers divide into chromosomal groups

LOD_hom

Integer. The LOD threshold specifying at which LOD score the markers divide into homologue groups

LG_number

Integer. Expected number of chromosomes (linkage groups). Note that if this number of clusters are not present at LOD_chm, the function will abort.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A data.frame with markers classified by homologue and linkage group.

Examples

data("P1_homologues")
ChHomDf<-define_LG_structure(cluster_list=P1_homologues,LOD_chm=3.5,LOD_hom=5,LG_number=5)

Example output dataset of polyRAD::PipelineMapping2Parents function

Description

Example output dataset of polyRAD::PipelineMapping2Parents function

Usage

exampleRAD_mapping

Format

An object of class RADdata of length 23.


Linkage analysis between all markertypes within a linkage group.

Description

finish_linkage_analysis is a wrapper for linkage, or in the case of probabilistic genotypes, linkage.gp. The function performs linkage calculations between all markertypes within a linkage group.

Usage

finish_linkage_analysis(
  input_type = "discrete",
  marker_assignment,
  dosage_matrix,
  probgeno_df,
  chk,
  marker_combinations = NULL,
  parent1 = "P1",
  parent2 = "P2",
  which_parent = 1,
  ploidy,
  ploidy2 = NULL,
  convert_palindrome_markers = TRUE,
  pairing = "random",
  prefPars = c(0, 0),
  LG_number,
  verbose = TRUE,
  log = NULL,
  ...
)

Arguments

input_type

Can be either one of 'discrete' or 'probabilistic'. For the former (default), dosage_matrix must be supplied, while for the latter probgeno_df and chk must be supplied.

marker_assignment

A marker assignment matrix with markernames as rownames and at least containing the column "Assigned_LG".

dosage_matrix

A named integer matrix with markers in rows and individuals in columns.

probgeno_df

A data frame as read from the scores file produced by function saveMarkerModels of R package fitPoly, or alternatively, a data frame containing the following columns:

SampleName

Name of the sample (individual)

MarkerName

Name of the marker

P0

Probabilities of dosage score '0'

P1...

Probabilities of dosage score '1' etc. (up to max dosage, e.g. P4 for tetraploid population)

maxP

Maximum genotype probability identified for a particular individual and marker combination

maxgeno

Most probable dosage for a particular individual and marker combination

geno

Most probable dosage for a particular individual and marker combination, if maxP exceeds a user-defined threshold (e.g. 0.9), otherwise NA

chk

Output list as returned by function checkF1. This argument is only needed if probabilistic genotypes are used.

marker_combinations

A matrix with four columns specifying marker combinations to calculate linkage. If NULL all combinations are used for which there are rf functions. Dosages of markers should be in the same order as specified in the names of rf functions. E.g. if using 1.0_2.0 and 1.0_3.0 types use: matrix(c(1,0,2,0,1,0,3,0), byrow = TRUE, ncol = 4)

parent1

Character string specifying the identifier of parent 1, by default "P1"

parent2

Character string specifying the identifier of parent 2, by default "P2"

which_parent

Integer, either 1 or 2, with default 1, where 1 or 2 refers to parent1 or parent2 respectively.

ploidy

Integer ploidy level of parent1, and also by default parent2. Argument ploidy2 can be used if parental ploidies differ.

ploidy2

Integer, by default NULL. If parental ploidies differ, use this to specify the ploidy of parent2.

convert_palindrome_markers

Logical. Should markers that behave the same for both parents be converted to a workable format for that parent? E.g.: should 3.1 markers be converted to 1.3?

pairing

Type of pairing at meiosis, with options "random" or "preferential". By default, random pairing is assumned.

prefPars

The estimates for preferential pairing parameters for parent 1 and 2, in range 0 <= p < 2/3. By default this is c(0,0) (so, no preferential pairing). See the function test_prefpairing and the vignette for more details.

LG_number

Number of linkage groups (chromosomes).

verbose

Should messages be sent to stdout or log?

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

...

(Other) arguments passed to linkage

Value

Returns a matrix with marker assignments. Number of linkages of 1.0 markers are artificial.

Examples

## Not run: 
data("screened_data3", "marker_assignments_P1")
linkages_list_P1<-finish_linkage_analysis(marker_assignment=marker_assignments_P1,
                                          dosage_matrix=screened_data3,
                                          parent1="P1",
                                          parent2="P2",
                                          which_parent=1,
                                          convert_palindrome_markers=FALSE,
                                          ploidy=4,
                                          pairing="random",
                                          LG_number=5)
                                          
## End(Not run)

Visualize and get all markertype combinations for which there are functions in polymapR

Description

Visualize and get all markertype combinations for which there are functions in polymapR

Usage

get_markertype_combinations(ploidy, pairing, nonavailable_combinations = TRUE)

Arguments

ploidy

Ploidy level

pairing

Type of pairing. Either "random" or "preferential".

nonavailable_combinations

Logical. Should nonavailable combinations be plotted with grey lines?

Value

A matrix with two columns. Each row represents a function with the first and second markertype.

Examples

get_markertype_combinations(ploidy = 4, pairing = "random")

An example of a genotype probability data frame

Description

An example of a genotype probability data frame

Usage

gp_df

Format

Data frame


gp_overview

Description

Function to generate an overview of genotype probabilities across a population

Usage

gp_overview(probgeno_df, cutoff = 0.7, alpha = 0.1)

Arguments

probgeno_df

A data frame as read from the scores file produced by function saveMarkerModels of R package fitPoly, or equivalently, a data frame containing the following columns:

SampleName

Name of the sample (individual)

MarkerName

Name of the marker

P0

Probabilities of dosage score '0'

P1...

Probabilities of dosage score '1' etc. (up to max dosage, e.g. P4 for tetraploid population)

maxP

Maximum genotype probability identified for a particular individual and marker combination

maxgeno

Most probable dosage for a particular individual and marker combination

geno

Most probable dosage for a particular individual and marker combination, if maxP exceeds a user-defined threshold (e.g. 0.9), otherwise NA

cutoff

a filtering threshold, by default 0.7, to identify individuals with more than alpha non-missing (maximum) genotype probabilities falling below this cut-off. In other words, by using this default settings (cutoff = 0.7 and alpha = 0.1), you require that 90 in one of the possible genotype dosage classes. This can help identify problematic individuals with many examples of diffuse genotype calls. Lowering the threshold allows more diffuse calls to be accepted.

alpha

Option to specify the quantile of an individuals' scores that will be used to test against cutoff, by default 0.1.

Value

a list with the following elements:

probgeno_df

Input data, filtered based on chosen cutoff

population_overview

data.frame containing summary statistics of each individual's genotyping scores

Examples

## Not run: 
data("gp_df")
gp_overview(gp_df)

## End(Not run)

A list of objects needed to build the probabilistic genotype vignette

Description

A list of objects needed to build the probabilistic genotype vignette

Usage

gp_vignette_data

Format

An object of class list of length 15.


Assign markers to linkage groups and homologues.

Description

This is a wrapper combining linkage (or linkage.gp) and assign_linkage_group. It is used to assign all marker types to linkage groups by using linkage information with 1.0 markers. It allows for input of marker assignments for which this analysis has already been performed.

Usage

homologue_lg_assignment(
  input_type = "discrete",
  dosage_matrix,
  probgeno_df,
  chk,
  assigned_list,
  assigned_markertypes,
  SN_functions = NULL,
  LG_hom_stack,
  parent1 = "P1",
  parent2 = "P2",
  which_parent = 1,
  ploidy,
  ploidy2 = NULL,
  convert_palindrome_markers = TRUE,
  pairing = "random",
  LG_number,
  LOD_threshold = 3,
  write_intermediate_files = TRUE,
  log = NULL,
  ...
)

Arguments

input_type

Can be either one of 'discrete' or 'probabilistic'. For the former (default), dosage_matrix must be supplied, while for the latter probgeno_df and chk must be supplied.

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

probgeno_df

A data frame as read from the scores file produced by function saveMarkerModels of R package fitPoly, or alternatively, a data frame containing the following columns:

SampleName

Name of the sample (individual)

MarkerName

Name of the marker

P0

Probabilities of dosage score '0'

P1...

Probabilities of dosage score '1' etc. (up to max dosage, e.g. P4 for tetraploid population)

maxP

Maximum genotype probability identified for a particular individual and marker combination

maxgeno

Most probable dosage for a particular individual and marker combination

geno

Most probable dosage for a particular individual and marker combination, if maxP exceeds a user-defined threshold (e.g. 0.9), otherwise NA

chk

Output list as returned by function checkF1. This argument is only needed if probabilistic genotypes are used.

assigned_list

List of data.frames with marker assignments for which the assignment analysis is already performed.

assigned_markertypes

List of integer vectors of length 2. Specifying the markertypes in the same order as assigned_list.

SN_functions

A vector of function names to be used. If NULL all remaining linkage functions with SN markers are used.

LG_hom_stack

A data.frame with markernames ("SxN_Marker"), linkage group ("LG") and homologue ("homologue")

parent1

A character string specifying name of parent1.

parent2

A character string specifying the name of parent2.

which_parent

Integer, either 1 or 2, with default 1, where 1 or 2 refers to parent1 or parent2 respectively.

ploidy

Ploidy level of parent 1. If parent 2 has the same ploidy level, then also the ploidy level of parent 2.

ploidy2

Integer, by default NULL. If parental ploidies differ, use this to specify the ploidy of parent 2. Note that in cross-ploidy situations, ploidy2 must be smaller than ploidy.

convert_palindrome_markers

Logical. Should markers that behave the same for both parents be converted to a workable format for that parent? E.g.: should 3.1 markers be converted to 1.3?

pairing

Type of pairing. Either "random" or "preferential". By default random pairing is assumed.

LG_number

Expected number of chromosomes (linkage groups).

LOD_threshold

LOD threshold at which a linkage is considered significant.

write_intermediate_files

Logical. Write intermediate linkage files to working directory?

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

...

Arguments passed to linkage

Value

A data.frame specifying marker assignments to linkage group and homologue.

Examples

## Not run: 
data("screened_data3", "P1_SxS_Assigned", "P1_DxN_Assigned", "LGHomDf_P1_1")
Assigned_markers<-homologue_lg_assignment(dosage_matrix = screened_data3,
                                          assigned_list = list(P1_SxS_Assigned, P1_DxN_Assigned),
                                          assigned_markertypes = list(c(1,1), c(2,0)),
                                          LG_hom_stack = LGHomDf_P1_1,ploidy=4,LG_number = 5,
                                          write_intermediate_files=FALSE)
                         
## End(Not run)

A nested list with integrated maps

Description

A nested list with integrated maps

Usage

integrated.maplist

Format

An object of class list of length 5.


A data.frame specifying the assigned homologue and linkage group number per SxN marker

Description

A data.frame specifying the assigned homologue and linkage group number per SxN marker

Usage

LGHomDf_P1_1

LGHomDf_P2_1

LGHomDf_P2_2

Format

  • SxN_Marker. Markername of simplex nulliplex marker

  • homologue. Assigned homologue number

  • LG Assigned. linkage group number

An object of class data.frame with 195 rows and 3 columns.

An object of class data.frame with 195 rows and 3 columns.


Calculate recombination frequency, LOD and phase

Description

linkage is used to calculate recombination frequency, LOD and phase within one type of marker or between two types of markers.

Usage

linkage(
  dosage_matrix,
  markertype1 = c(1, 0),
  markertype2 = NULL,
  parent1 = "P1",
  parent2 = "P2",
  which_parent = 1,
  ploidy,
  ploidy2 = NULL,
  G2_test = FALSE,
  convert_palindrome_markers = TRUE,
  LOD_threshold = 0,
  pairing = "random",
  prefPars = c(0, 0),
  combinations_per_iter = NULL,
  iter_RAM = 500,
  ncores = 1,
  verbose = TRUE,
  full_output = FALSE,
  log = NULL
)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

markertype1

A vector of length 2 specifying the first markertype to compare. The first element specifies the dosage in which_parent (see below), the second in the other parent.

markertype2

A vector of length 2 specifying the first markertype to compare. This argument is optional. If not specified, the function will calculate linkage within the markertype as specified by markertype1. The first element specifies the dosage in which_parent (see below), the second in the other parent.

parent1

Character string specifying the name of parent1 as provided in the column-names of dosage_matrix. By default, "P1".

parent2

Character string specifying the other parent as provided in the column-names of dosage_matrix. By default, "P2".

which_parent

Integer, either 1 or 2, with default 1, where 1 or 2 refers to parent1 or parent2 respectively. For example, if you wish to estimate linkage between markers with alleles that are polymorphic (i.e. segregating) and originates from parent1, then which_parent = 1. A bi-parental marker is a marker such as a 1x1 marker, so having a segregating allele in both parents. For linkage estimation between pairs of bi-parental markers, the result does not depend on this argument. For linkage estimation between e.g. a 1x0 and 1x1 marker, then which_parent should be 1. Similarly, to calculate linkage between 0x1 and 1x1 markers, which_parent should be 2.

ploidy

Integer. The ploidy of the parent 1. If parent2 has the same ploidy level, then also the ploidy level of parent 2.

ploidy2

Integer, by default NULL. If parental ploidies differ, use this to specify the ploidy of parent2.

G2_test

Apply a G2 test (LOD of independence) in addition to the LOD of linkage.

convert_palindrome_markers

Logical. Should markers that behave the same for both parents be converted to a workable format for that parent? E.g.: should 3.1 markers be converted to 1.3? If unsure, set to TRUE.

LOD_threshold

Minimum LOD score of linkages to report. Recommended to use for large number (> millions) of marker comparisons in order to reduce memory usage.

pairing

Type of chromosomal pairing behaviour during meiosis, either "random" or "preferential". By default, random pairing is assumed (i.e. polysomic inheritance) is assumed. Note that this default does not affect linkage estimation in a diploid, where pairing is arguably not random.

prefPars

The estimates for preferential pairing parameters for the target and other parent, respectively, in range 0 <= p < 2/3. By default this is c(0,0) (so, no preferential pairing). See the function test_prefpairing and the vignette for more details.

combinations_per_iter

Optional integer. Number of marker combinations per iteration.

iter_RAM

A (very) conservative estimate of working memory in megabytes used per core. It only takes the size frequency matrices into account. Actual usage is more, especially with large number of linkages that are reported. Reduce memory usage by using a higher LOD_threshold.

ncores

Number of cores to use. Works both for Windows and UNIX (using doParallel). Use parallel::detectCores() to find out how many cores you have available.

verbose

Should messages be sent to stdout?

full_output

Logical, by default FALSE. If TRUE, the complete output over all phases and showing marker combination counts is returned.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

Returns a data.frame with columns:

marker_a

first marker of comparison. If markertype2 is specified, it has the type of markertype1.

marker_b

second marker of comparison. It has the type of markertype2 if specified.

r

(estimated) recombinations frequency

LOD

(estimated) LOD score

phase

phase between markers

Examples

data("screened_data3")
SN_SN_P1 <- linkage(dosage_matrix = screened_data3,
                   markertype1 = c(1,0),
                   which_parent = 1,
                   ploidy = 4,
                   pairing = "random",
                   ncores = 1
                   )

Calculate recombination frequency, LOD and phase using genotype probabilities

Description

linkage.gp is used to calculate recombination frequency, LOD and phase within one type of marker or between two types of markers.

Usage

linkage.gp(
  probgeno_df,
  chk,
  pardose = NULL,
  markertype1 = c(1, 0),
  markertype2 = NULL,
  target_parent = match.arg(c("P1", "P2")),
  G2_test = FALSE,
  LOD_threshold = 0,
  prefPars = c(0, 0),
  combinations_per_iter = NULL,
  iter_RAM = 500,
  ncores = 2,
  verbose = TRUE,
  check_qall_mult = FALSE,
  method = "approx",
  log = NULL
)

Arguments

probgeno_df

A data frame as read from the scores file produced by function saveMarkerModels of R package fitPoly, or alternatively, a data frame containing the following columns:

SampleName

Name of the sample (individual)

MarkerName

Name of the marker

P0

Probabilities of dosage score '0'

P1...

Probabilities of dosage score '1' etc. (up to max dosage, e.g. P4 for tetraploid population)

maxP

Maximum genotype probability identified for a particular individual and marker combination

maxgeno

Most probable dosage for a particular individual and marker combination

geno

Most probable dosage for a particular individual and marker combination, if maxP exceeds a user-defined threshold (e.g. 0.9), otherwise NA

chk

Output list as returned by function checkF1

pardose

Option to include the most likely (discrete) parental dosage scores, used mainly for internal calls of this function. By default NULL

markertype1

A vector of length 2 specifying the first markertype to compare. The first element specifies the dosage in target_parent (and the second in the other parent).

markertype2

A vector of length 2 specifying the first markertype to compare. This argument is optional. If not specified, the function will calculate linkage within the markertype as specified by markertype1. The first element specifies the dosage in target_parent (and the second in the other parent).

target_parent

Which parent is being targeted (only acceptable options are "P1" or "P2"), ie. which parent is of specific interest? If this is the maternal parent, please specify as "P1". If the paternal parent, please use "P2". The actual identifiers of the two parents are entered using the arguments parent1_replicates and parent2_replicates.

G2_test

Apply a G2 test (LOD of independence) in addition to the LOD of linkage.

LOD_threshold

Minimum LOD score of linkages to report. Recommended to use for large number (> millions) of marker comparisons in order to reduce memory usage.

prefPars

The estimates for preferential pairing parameters for parent 1 and 2, in range 0 <= p < 2/3. By default this is c(0,0) (so, no preferential pairing). See the function test_prefpairing and the vignette for more details.

combinations_per_iter

Optional integer. Number of marker combinations per iteration.

iter_RAM

A (very) conservative estimate of working memory in megabytes used per core. It only takes the size frequency matrices into account. Actual usage is more, especially with large number of linkages that are reported. Reduce memory usage by using a higher LOD_threshold.

ncores

Number of cores to use. Works both for Windows and UNIX (using doParallel). Use parallel::detectCores() to find out how many cores you have available.

verbose

Should messages be sent to stdout?

check_qall_mult

Check the qall_mult column of chk, and filter out markers with qall_mult = 0. By default FALSE.

method

Either "approx" or "mappoly". If "approx" (the default method), then an approximated estimator is used which introduces a small amount of bias in the estimator of recombination frequency. If method "mappoly" is specified, the full likelihood is used in the estimation, leading to an unbiased estimator (this has been implemented in the mappoly package of Marcelo Mollinari). The mappoly method has higher computational demands which may introduce problems for larger datasets, but will lead to higher accuracy overall.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

Returns a data.frame with columns:

marker_a:

first marker of comparison. If markertype2 is specified, it has the type of markertype1.

marker_b:

second marker of comparison. It has the type of markertype2 if specified.

r:

recombination frequency

LOD:

LOD score associated with r

phase:

phase between markers

Examples

data("gp_df","chk1")
SN_SN_P1.gp <- linkage.gp(probgeno_df = gp_df,
                          chk = chk1,
                          markertype1 = c(1,0),
                          target_parent = "P1")

A sample map

Description

A sample map

Usage

map1

Format

An object of class data.frame with 100 rows and 2 columns.


A sample map

Description

A sample map

Usage

map2

Format

An object of class data.frame with 100 rows and 2 columns.


A sample map

Description

A sample map

Usage

map3

Format

An object of class data.frame with 60 rows and 2 columns.


A list of maps of one parent

Description

A list of maps of one parent

Usage

maplist_P1

maplist_P1_subset

maplist_P2_subset

Format

An object of class list of length 5.

An object of class list of length 5.

An object of class list of length 5.


Perform binning of markers.

Description

marker_binning allows for binning of very closely linked markers and choses one representative.

Usage

marker_binning(
  dosage_matrix,
  linkage_df,
  r_thresh = NA,
  lod_thresh = NA,
  target_parent = "P1",
  other_parent = "P2",
  max_marker_nr = NULL,
  max_iter = 10,
  log = NULL
)

Arguments

dosage_matrix

A dosage matrix.

linkage_df

A linkage data.frame.

r_thresh

Numeric. Threshold at which markers are binned. Is calculated if NA.

lod_thresh

Numeric. Threshold at which markers are binned. Is calculated if NA.

target_parent

A character string specifying the name of the target parent.

other_parent

A character string specifying the name of the other parent.

max_marker_nr

The maximum number of markers per homologue. If specified, LOD threshold is optimized based on this number.

max_iter

Maximum number of iterations to find optimum LOD threshold. Only used if max_marker_nr is specified.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A list with the following components:

binned_df

A linkage data.frame with binned markers removed.

removed

A data.frame containing binned markers and their representatives.

left

Integer. Number markers left.

Examples

data("screened_data3", "all_linkages_list_P1_split")
binned_markers<-marker_binning(screened_data3, all_linkages_list_P1_split[["LG2"]][["homologue3"]])

Summarize marker data

Description

Gives a frequency table of different markertypes, relative frequency per markertype of incompatible offspring and the names of incompatible progeny.

Usage

marker_data_summary(
  dosage_matrix,
  ploidy,
  ploidy2 = NULL,
  pairing = c("random", "preferential"),
  parent1 = "P1",
  parent2 = "P2",
  progeny_incompat_cutoff = 0.1,
  verbose = TRUE,
  shortform = FALSE,
  log = NULL
)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

ploidy

Integer. Ploidy of parent 1, and .

ploidy2

Ploidy of parent 2, by default NULL, as it is assumed ploidy2 equals ploidy.

pairing

Type of pairing. "random" or "preferential".

parent1

Column name of first parent. Usually maternal parent.

parent2

Column name of second parent. Usually paternal parent.

progeny_incompat_cutoff

The relative number of incompatible dosages per genotype that results in reporting this genotype as incompatible. Incompatible dosages are greater than maximum number of alleles than can be inherited or smaller than the minimum number of alleles that can be inherited.

verbose

Logical, by default TRUE - should intermediate messages be written to stout?

shortform

Logical, by default FALSE. Returns only a shortened output with parental dosage summary, used internally by some functions.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

Returns a list containing the following components:

parental_info

frequency table of different markertypes. Names start with parentnames, and behind that the dosage score.

offspring_incompatible

Rate of incompatible ("impossible") marker scores (given as percentages of the total number of observed marker scores per marker class)

progeny_incompatible

progeny names having incompatible dosage scores higher than threshold at progeny_incompat_cutoff.

Examples

data("ALL_dosages")
summary_list<-marker_data_summary(dosage_matrix = ALL_dosages, ploidy = 4)

Wrapper function for MDSMap to generate linkage maps from list of pairwise linkage estimates

Description

Create multidimensional scaling maps from a list of linkages

Usage

MDSMap_from_list(
  linkage_list,
  write_to_file = FALSE,
  mapdir = "mapping_files_MDSMap",
  plot_prefix = "",
  log = NULL,
  ...
)

Arguments

linkage_list

A named list with r and LOD of markers within linkage groups.

write_to_file

Should output be written to a file? By default FALSE, if TRUE then output, including plots from MDSMap are saved in the same directory as the one used for input files. These plots are currently saved as pdf images. If a different plot format is required (e.g. for publications), then run the MDSMap function estimate.map (or similar) directly and save the output with a different plotting function as wrapper around the map function call.

mapdir

Directory to which map input files are initially written. Also used for output if write_to_file=TRUE

plot_prefix

prefix for the filenames of output plots.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

...

Arguments passed to estimate.map.

Examples

## Not run: 
data("all_linkages_list_P1")
maplist_P1 <- MDSMap_from_list(all_linkages_list_P1[1])

## End(Not run)

Merge homologues

Description

Based on additional information, homologue fragments, separated during clustered should be merged again. merge_homologues allows to merge homologues per linkage group based on user input.

Usage

merge_homologues(LG_hom_stack, ploidy, LG, mergeList = NULL, log = NULL)

Arguments

LG_hom_stack

A data.frame with markernames, linkage group ("LG") and homologue ("homologue")

ploidy

The ploidy level of the plant species.

LG

The linkage group where the to be merged homologue fragments are in.

mergeList

A list of vectors of length 2, specifying the numbers of the homologue fragments to be merged. User input is asked if NULL.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A modified LG_hom_stack

Examples

data("LGHomDf_P2_1")
merged<-merge_homologues(LGHomDf_P2_1,ploidy=4,LG=2,mergeList=list(c(1,5)))

Example output dataset of updog::multidog function

Description

Example output dataset of updog::multidog function

Usage

mout

Format

An object of class multidog of length 2.


A list of cluster stacks at different LOD scores

Description

A list of cluster stacks at different LOD scores

Usage

P1_homologues

P2_homologues

P2_homologues_triploid

Format

A list with with LOD thresholds as names. The list contains dataframes with the following format:

  • marker. markername

  • pseudohomologue. name of (pseudo)homologue

An object of class list of length 10.

An object of class list of length 15.


A data.frame with marker assignments

Description

A data.frame with marker assignments

Usage

P1_SxS_Assigned

P2_SxS_Assigned

P2_SxS_Assigned_2

P1_DxN_Assigned

P2_DxN_Assigned

marker_assignments_P1

marker_assignments_P2

Format

A data.frame with at least the following columns:

  • Assigned_LG. The assigned linkage group

  • Assigend_hom1. The homologue with most linkages

The columns LG1 - LGn and Hom1 - Homn give the number of hits per marker for that linkage group/homologue. Assigned_hom2 .. gives the nth homologue with most linkages.

An object of class matrix (inherits from array) with 301 rows and 14 columns.

An object of class matrix (inherits from array) with 301 rows and 14 columns.

An object of class matrix (inherits from array) with 111 rows and 14 columns.

An object of class matrix (inherits from array) with 101 rows and 14 columns.

An object of class matrix (inherits from array) with 1094 rows and 16 columns.

An object of class matrix (inherits from array) with 1127 rows and 16 columns.


Calculate recombination frequency, LOD and log-likelihood from frequency tables in a preferential pairing tetraploid

Description

This group of functions is called by linkage.

Arguments

x

A frequency table of the different classes of dosages in the progeny. The column names start with "n_". Followed by the dosage of the first marker and then of the second.

p1

Preferential pairing parameter for parent 1, numeric value in range 0 <= p1 < 2/3

p2

Preferential pairing parameter for parent 2, numeric value in range 0 <= p2 < 2/3

ncores

Number of cores to use for parallel processing (deprecated).

Value

A list with the following items:

r_mat

A matrix with recombination frequencies for the different phases

LOD_mat

A matrix with LOD scores for the different phases

logL_mat

A matrix with log likelihood ratios for the different phases

phasing_strategy

A character string specifying the phasing strategy. "MLL" for maximum likelihood en "MINR" for minimum recombination frequency.

possible_phases

The phases between markers that are possible. Same order and length as column names of output matrices.


Calculate frequency of each markertype.

Description

Plots and returns frequency information for each markertype.

Usage

parental_quantities(
  dosage_matrix,
  parent1 = "P1",
  parent2 = "P2",
  log = NULL,
  ...
)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

parent1

Character string specifying the first (usually maternal) parentname.

parent2

Character string specifying the second (usually paternal) parentname.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

...

Arguments passed to barplot

Value

A named vector containing the frequency of each markertype in the dataset.

Examples

data("ALL_dosages","screened_data")
parental_quantities(dosage_matrix=ALL_dosages)
parental_quantities(dosage_matrix=screened_data)

Perform a PCA on progeny

Description

Principal component analysis in order to identify individuals that deviate from the population.

Usage

PCA_progeny(dosage_matrix, highlight = NULL, colors = NULL, log = NULL)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

highlight

A list of character vectors specifying individual names that should be highlighted

colors

Highlight colors. Vector of the same length as highlight.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Details

Missing values are imputed by taking the mean of marker dosages per marker.

Examples

data("ALL_dosages")
PCA_progeny(dosage_matrix=ALL_dosages, highlight=list(c("P1", "P2")), colors="red")

Phase 1.0 markers at the diploid level

Description

phase_SN_diploid phases simplex x nulliplex markers for a diploid parent.

Usage

phase_SN_diploid(
  linkage_df,
  cluster_list,
  LOD_chm = 3.5,
  LG_number,
  independence_LOD = FALSE,
  log = NULL
)

Arguments

linkage_df

A linkage data.frame as output of linkage calculating linkage between 1.0 markers.

cluster_list

A list of cluster_stacks, the output of cluster_SN_markers.

LOD_chm

Integer. The LOD threshold specifying at which LOD score the markers divide into chromosomal groups

LG_number

Expected number of chromosomes (linkage groups)

independence_LOD

Logical. Should the LOD of independence be used for clustering? (by default, FALSE.)

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout (console).

Value

A data.frame with markers classified by homologue and linkage group.

Examples

data("SN_SN_P2_triploid","P2_homologues_triploid")
cluster_list2<-phase_SN_diploid(SN_SN_P2_triploid,P2_homologues_triploid,LOD_chm=5,LG_number = 3)

A list of phased maps

Description

A list of phased maps

Usage

phased.maplist

Format

An object of class list of length 5.


Plot homologue position versus integrated positions

Description

Plot homologue position versus integrated positions

Usage

plot_hom_vs_LG(map_df, maplist_homologue)

Arguments

map_df

A dataframe of a map that defines a linkage group.

maplist_homologue

A list of maps were each item represents a homoloogue.

Examples

data("integrated.maplist", "maplist_P1_subset")
colnames(integrated.maplist[["LG2"]]) <- c("marker", "position", "QTL_LOD")
plot_hom_vs_LG(map_df = integrated.maplist[["LG2"]],
               maplist_homologue = maplist_P1_subset[["LG2"]])

Plot linkage maps

Description

Makes a simple plot of a list of generated linkage maps

Usage

plot_map(
  maplist,
  highlight = NULL,
  bg_col = "grey",
  highlight_col = "yellow",
  colname_in_mark = NULL,
  colname_beside_mark = NULL,
  palette_in_mark = colorRampPalette(c("white", "purple")),
  palette_beside_mark = colorRampPalette(c("white", "green")),
  color_by_type = FALSE,
  dosage_matrix = NULL,
  parent1 = "P1",
  parent2 = "P2",
  legend = FALSE,
  ...,
  legend.args = list(x = 1, y = 120)
)

Arguments

maplist

A list of maps. In the first column marker names and in the second their position.

highlight

A list of the same length of maplist with vectors of length 2 that specifies the limits in cM from and to which the plotted chromosomes should be highlighted.

bg_col

The background colour of the map.

highlight_col

The color of the highlight. Only used if highlight is specified.

colname_in_mark

Optional. The column name of the value to be plotted as marker color.

colname_beside_mark

Optional. The column name of the value to be plotted beside the markers.

palette_in_mark, palette_beside_mark

Color palette used to plot values. Only used if colnames of the values are specified.

color_by_type

Logical. Should the markers be coloured by type? If TRUE, dosage_matrix should be specified.

dosage_matrix

Optional (by default NULL). Dosage matrix of marker genotypes, input of linkage

parent1

Character string specifying the first (usually maternal) parentname.

parent2

Character string specifying the second (usually paternal) parentname.

legend

Logical. Should a legend be drawn?

...

Arguments passed to plot

legend.args

Optional extra arguments to pass to legend, by default a list with x = 1 and y = 120 (position of the legend). Additional arguments should be passed using name = value, i.e. as a named list. Note that arguments lty (= 1) and lwd (= 2) have already been used internally (as well as legend and col), so cannot be re-specified without causing an error.

Examples

data("maplist_P1")
plot_map(maplist = maplist_P1, colname_in_mark = "nnfit", bg_col = "white",
         palette_in_mark = colorRampPalette(c("blue", "purple", "red")),
         highlight = list(c(20, 60),
         c(60,80),
         c(20,30),
         c(40,70),
         c(60,80)))

Visualise the phased homologue maplist

Description

plot_phased_maplist is a function for visualising a phased maplist, the output of create_phased_maplist

Usage

plot_phased_maplist(
  phased.maplist,
  ploidy,
  ploidy2 = NULL,
  cols = c("black", "darkred", "navyblue"),
  width = 0.2,
  mapTitles = NULL
)

Arguments

phased.maplist

A list of phased linkage maps, the output of create_phased_maplist

ploidy

Integer. Ploidy of the organism.

ploidy2

Optional integer, by default NULL. Ploidy of parent 2, if different from parent 1.

cols

Vector of colours for the integrated, parent1 and parent2 maps, respectively.

width

Width of the linkage maps, by default 0.2

mapTitles

Optional vector of titles for maps, by default names of maplist, or titles LG1, LG2 etc. are used.

Examples

data("phased.maplist")
plot_phased_maplist(phased.maplist, ploidy = 4)

Plot r versus LOD

Description

r_LOD_plot plots r versus LOD, colour separated for different phases.

Usage

r_LOD_plot(
  linkage_df,
  plot_main = "",
  chm = NA,
  r_max = 0.5,
  tidyplot = TRUE,
  nbins = 200
)

Arguments

linkage_df

A linkage data.frame as output of linkage.

plot_main

A character string specifying the main title

chm

Integer specifying chromosome

r_max

Maximum r value to plot

tidyplot

If TRUE (by default), an attempt is made to reduce the plot density using hexagonal binning from the ggplot2 package. This is recommended for large datasets, where the number of pairwise estimates becomes high.

nbins

The number of bins in each direction, passed to ggplot2::geom_hex. Only used if tidyplot = TRUE. Increasing this number can lead to slower but more accurate plotting.

Examples

data("SN_SN_P1")
r_LOD_plot(SN_SN_P1)

Calculate recombination frequency, LOD and log-likelihood from frequency tables in a random pairing diploid cross.

Description

This group of functions is called by linkage.

Usage

r2_1.0_1.0(x, ncores = 1)

r2_1.0_1.1(x, ncores = 1)

r2_1.1_1.1(x, ncores = 1)

Arguments

x

A frequency table of the different classes of dosages in the progeny. The column names start with "n_". Followed by the dosage of the first marker and then of the second.

ncores

Number of cores to use for parallel processing (deprecated).

Value

A list with the following items:

r_mat

A matrix with recombination frequencies for the different phases

LOD_mat

A matrix with LOD scores for the different phases

logL_mat

A matrix with log likelihood ratios for the different phases

phasing_strategy

A character string specifying the phasing strategy. "MLL" for maximum likelihood en "MINR" for minimum recombination frequency.

possible_phases

The phases between markers that are possible. Same order and length as column names of output matrices.


Calculate recombination frequency, LOD and log-likelihood from frequency tables in a random pairing triploid from a 4x2 or 2x4 cross.

Description

This group of functions is called by linkage.

Usage

r3_2_1.0_1.0(x, ncores = 1)

r3_2_1.0_1.1(x, ncores = 1)

r3_2_1.0_1.2(x, ncores = 1)

r3_2_1.2_1.2(x, ncores = 1)

Arguments

x

A frequency table of the different classes of dosages in the progeny. The column names start with "n_". Followed by the dosage of the first marker and then of the second.

ncores

Number of cores to use for parallel processing (deprecated).

Value

A list with the following items:

r_mat

A matrix with recombination frequencies for the different phases

LOD_mat

A matrix with LOD scores for the different phases

logL_mat

A matrix with log likelihood ratios for the different phases

phasing_strategy

A character string specifying the phasing strategy. "MLL" for maximum likelihood en "MINR" for minimum recombination frequency.

possible_phases

The phases between markers that are possible. Same order and length as column names of output matrices.


Calculate recombination frequency, LOD and log-likelihood from frequency tables in a random pairing tetraploid

Description

This group of functions is called by linkage.

Arguments

x

A frequency table of the different classes of dosages in the progeny. The column names start with "n_". Followed by the dosage of the first marker and then of the second.

ncores

Number of cores to use for parallel processing (deprecated).

Value

A list with the following items:

r_mat

A matrix with recombination frequencies for the different phases

LOD_mat

A matrix with LOD scores for the different phases

logL_mat

A matrix with log likelihood ratios for the different phases

phasing_strategy

A character string specifying the phasing strategy. "MLL" for maximum likelihood en "MINR" for minimum recombination frequency.

possible_phases

The phases between markers that are possible. Same order and length as column names of output matrices.


Calculate recombination frequency, LOD and log-likelihood from frequency tables in a random pairing hexaploid

Description

This group of functions is called by linkage.

Arguments

x

A frequency table of the different classes of dosages in the progeny. The column names start with "n_". Followed by the dosage of the first marker and then of the second.

Value

A list with the following items:

r_mat

A matrix with recombination frequencies for the different phases

LOD_mat

A matrix with LOD scores for the different phases

logL_mat

A matrix with log likelihood ratios for the different phases

phasing_strategy

A character string specifying the phasing strategy. "MLL" for maximum likelihood en "MINR" for minimum recombination frequency.

possible_phases

The phases between markers that are possible. Same order and length as column names of output matrices.


Screen for duplicate individuals

Description

screen_for_duplicate_individuals identifies and merges duplicate individuals.

Usage

screen_for_duplicate_individuals(
  dosage_matrix,
  cutoff = NULL,
  plot_cor = TRUE,
  log = NULL
)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

cutoff

Correlation coefficient cut off. At this correlation coefficient, individuals are merged. If NULL user input will be asked after plotting.

plot_cor

Logical. Should correlation coefficients be plotted? Can be memory/CPU intensive with high number of individuals.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A matrix similar to dosage_matrix, with merged duplicate individuals.

Examples

## Not run: 
#user input:
data("segregating_data")
screen_for_duplicate_individuals(dosage_matrix=segregating_data,cutoff=0.9,plot_cor=TRUE)

## End(Not run)

Screen for duplicate individuals using weighted genotype probabilities

Description

screen_for_duplicate_individuals.gp identifies and merges duplicate individuals based on probabilistic genotypes. See screen_for_duplicate_individuals for the original function.

Usage

screen_for_duplicate_individuals.gp(
  probgeno_df,
  ploidy,
  parent1 = "P1",
  parent2 = "P2",
  F1,
  cutoff = 0.95,
  plot_cor = TRUE,
  log = NULL
)

Arguments

probgeno_df

A data frame as read from the scores file produced by function saveMarkerModels of R package fitPoly, or alternatively, a data frame containing the following columns:

SampleName

Name of the sample (individual)

MarkerName

Name of the marker

P0

Probabilities of dosage score '0'

P1...

Probabilities of dosage score '1' etc. (up to max dosage, e.g. P4 for tetraploid population)

maxP

Maximum genotype probability identified for a particular individual and marker combination

maxgeno

Most probable dosage for a particular individual and marker combination

geno

Most probable dosage for a particular individual and marker combination, if maxP exceeds a user-defined threshold (e.g. 0.9), otherwise NA

ploidy

The ploidy of parent 1

parent1

character vector with the sample names of parent 1

parent2

character vector with the sample names of parent 2

F1

character vector with the sample names of the F1 individuals

cutoff

Correlation coefficient cut off to declare duplicates. At this correlation coefficient, individuals are merged. If NULL user input will be asked after plotting.

plot_cor

Logical. Should correlation coefficients be plotted? Can be memory/CPU intensive with high number of individuals.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A data frame similar to input probgeno_df, but with duplicate individuals merged.


Screen for and remove duplicated markers

Description

screen_for_duplicate_markers identifies and merges duplicate markers.

Usage

screen_for_duplicate_markers(
  dosage_matrix,
  merge_NA = TRUE,
  plot_cluster_size = TRUE,
  ploidy,
  ploidy2 = NULL,
  LG_number,
  estimate_bin_size = FALSE,
  log = NULL
)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

merge_NA

Logical. Should missing values be imputed if non-NA in duplicated marker? By default, TRUE. If FALSE the dosage scores of representing marker are represented in the filtered_dosage_matrix.

plot_cluster_size

Logical. Should an informative plot about duplicate cluster size be given? By default, TRUE.

ploidy

Ploidy level of parent 1. Only needed if estimate_bin_size is TRUE

ploidy2

Integer, by default NULL. If parental ploidies differ, use this to specify the ploidy of parent 2. Only needed if estimate_bin_size is TRUE

LG_number

Expected number of chromosomes (linkage groups). Only needed if estimate_bin_size is TRUE

estimate_bin_size

Logical, by default FALSE. If TRUE, a very rudimentary calculation is made to estimate the average size of a marker bin, assuming a uniform distribution of cross-over events and on average one cross-over per bivalent.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A list containing:

bin_list

list of binned markers. The list names are the representing markers. This information can later be used to enrich the map with binned markers.

filtered_dosage_matrix

dosage_matrix with merged duplicated markers. The markers will be given the name of the marker with least missing values.

Examples

data("screened_data3")
dupmscreened <- screen_for_duplicate_markers(screened_data3)

Screen marker data for NA values

Description

screen_for_NA_values identifies and can remove rows or columns of a marker dataset based on the relative frequency of missing values.

Usage

screen_for_NA_values(
  dosage_matrix,
  margin = 1,
  cutoff = NULL,
  parentnames = c("P1", "P2"),
  plot_breakdown = FALSE,
  log = NULL,
  print.removed = TRUE
)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

margin

An integer at which margin the missing value frequency will be calculated. A value of 1 means rows (markers), 2 means columns (individuals)

cutoff

Missing value frequency cut off. At this frequency, rows or columns are removed from the dataset. If NULL user input will be asked after plotting the missing value frequency histogram.

parentnames

A character vector of length 2, specifying the parent names.

plot_breakdown

Logical. Should the percentage of markers removed as breakdown per markertype be plotted? Can only be used if margin = 1.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

print.removed

Logical. Should removed instances be printed?

Value

A matrix similar to dosage_matrix, with rows or columns removed that had a higher missing value frequency than specified.

Examples

data("segregating_data","screened_data")
screened_markers<-screen_for_NA_values(dosage_matrix=segregating_data, margin=1, cutoff=0.1)
screened_indiv<-screen_for_NA_values(dosage_matrix=screened_data, margin=2, cutoff=0.1)

A linkage data.frame.

Description

A linkage data.frame.

Usage

SN_SN_P1

SN_SN_P2

SN_SS_P1

SN_SS_P2

SN_DN_P1

SN_DN_P2

SN_SN_P2_triploid

Format

  • marker_a. First marker in comparison

  • marker_b. Second marker in comparison

  • r. recombination frequency

  • LOD. LOD score

  • phase. The phase between markers

An object of class linkage_df (inherits from data.frame) with 19306 rows and 5 columns.

An object of class linkage_df (inherits from data.frame) with 53152 rows and 5 columns.

An object of class linkage_df (inherits from data.frame) with 59494 rows and 5 columns.

An object of class linkage_df (inherits from data.frame) with 19536 rows and 5 columns.

An object of class linkage_df (inherits from data.frame) with 19897 rows and 5 columns.

An object of class data.frame with 6655 rows and 5 columns.


Identify deviations in LOD scores between pairs of simplex x nulliplex markers

Description

SNSN_LOD_deviations checks whether the LOD scores obtained in the case of pairs of simplex x nulliple markers are compatible with expectation. This can help identify problematic linkage estimates which can adversely affect marker clustering.

Usage

SNSN_LOD_deviations(
  linkage_df,
  ploidy,
  N,
  plot_expected = TRUE,
  alpha = c(0.05, 0.2),
  phase = c("coupling", "repulsion")
)

Arguments

linkage_df

A linkage data.frame as output of linkage.

ploidy

Integer. The ploidy level of the species.

N

Numeric. The number of F1 individuals in the mapping population.

plot_expected

Logical. Plot the observed and expected relationship between r and LOD.

alpha

Numeric. Vector of upper and lower tolerances around expected line.

phase

Character string. Specify which phase to examine for deviations (usually this is "coupling" phase).

Value

A vector of deviations in LOD scores outside the range defined by tolerances input alpha

Examples

data("SN_SN_P1")
SNSN_LOD_deviations(SN_SN_P1,ploidy = 4, N = 198)

Check for and estimate preferential pairing

Description

Identify closely-mapped repulsion-phase simplex x nulliplex markers and test these for preferential pairing, including estimating a preferential pairing parameter.

Usage

test_prefpairing(
  dosage_matrix,
  maplist,
  LG_hom_stack,
  target_parent = "P1",
  other_parent = "P2",
  ploidy,
  min_cM = 0.5,
  adj.method = "fdr",
  verbose = TRUE
)

Arguments

dosage_matrix

An integer matrix with markers in rows and individuals in columns.

maplist

A list of integrated chromosomal maps, as generated by e.g. MDSMap_from_list. In the first column marker names and in the second their position.

LG_hom_stack

A data.frame with markernames ("SxN_Marker"), linkage group ("LG") and homologue ("homologue"), the output of define_LG_structure or bridgeHomologues usually.

target_parent

Character string specifying the parent to be tested for preferential pairing as provided in the columnnames of dosage_matrix, by default "P1".

other_parent

The other parent, by default "P2"

ploidy

The ploidy level of the species, by default 4 (tetraploid) is assumed.

min_cM

The smallest distance to be considered a true distance on the linkage map, by default distances less than 0.5 cM are considered essentially zero.

adj.method

Method to correct p values of Binomial test for multiple testing, by default the FDR correction is used, other options are available, inherited from p.adjust

verbose

Should messages be sent to stdout? If NULL log is send to stdout.

Examples

data("ALL_dosages","integrated.maplist","LGHomDf_P1_1")
P1pp <- test_prefpairing(ALL_dosages,integrated.maplist,LGHomDf_P1_1,ploidy=4)

Write out a nested list

Description

Write a nested list into a directory structure

Usage

write_nested_list(
  nested_list,
  directory,
  save_as_object = FALSE,
  object_prefix = directory,
  extension = if (save_as_object) ".Rdata" else ".txt",
  ...
)

Arguments

nested_list

A nested list.

directory

Character string. Directory name to which to write the structure.

save_as_object

Logical. Save as R object?

object_prefix

Character. Prefix of R object. Only used if save_as_object = TRUE.

extension

Character. File extension. Default is ".txt".

...

Arguments passed to write.table

Examples

## Not run: 
data("all_linkages_list_P1_subset")
write_nested_list(nested_list = all_linkages_list_P1_subset,
                  directory = "all_linkages_P1",
                  sep="\t")
## End(Not run)

Write pwd files from a nested list

Description

A wrapper for write.pwd, which allows to write multiple pwd files with a directory structure according to the nested linkage list.

Usage

write_pwd_list(
  linkages_list,
  target_parent,
  binned = FALSE,
  dir = getwd(),
  log = NULL
)

Arguments

linkages_list

A nested list with linkage group on the first level and homologue on the second.

target_parent

A character string specifying the name of the target parent.

binned

Logical. Are the markers binned? This information is used in the pwd header.

dir

A character string specifying the directory in which the files are written. Defaults to working directory.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Examples

## Not run: 
data("all_linkages_list_P1_split")
write_pwd_list(all_linkages_list_P1_split, target_parent="P1", binned=FALSE)
## End(Not run)

Write MapChart file

Description

Write a .mct file of a maplist for external plotting with MapChart software (Voorrips ).

Usage

write.mct(
  maplist,
  mapdir = "mapping_files_MDSMap",
  file_info = paste("; MapChart file created on", Sys.Date()),
  filename = "MapFile",
  precision = 2,
  showMarkerNames = FALSE
)

Arguments

maplist

A list of maps. In the first column marker names and in the second their position. All map data are compiled into a single MapChart file.

mapdir

Directory to which .mct files are written, by default the same directory as for MDSMap_from_list

file_info

A character string added to the first lines of the .mct file, by default a datestamp is recorded.

filename

Character string of filename to write the .mct file to, by default "MapFile"

precision

To how many decimal places should marker positions be specified (default = 2)?

showMarkerNames

Logical, by default FALSE, if TRUE, the marker names will be diplayed in the MapChart output as well.

Examples

## Not run: 
data("integrated.maplist")
write.mct(integrated.maplist)
## End(Not run)

Write a JoinMap compatible .pwd file from linkage data.frame.

Description

Output of this function allows to use JoinMap to perform the marker ordering step.

Usage

write.pwd(linkage_df, pwd_file, file_info, log = NULL)

Arguments

linkage_df

A linkage data.frame.

pwd_file

A character string specifying a file open for writing.

file_info

A character string added to the first lines of the .pwd file.

log

Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Examples

## Not run: 
data("all_linkages_list_P1_split")
write.pwd(all_linkages_list_P1_split[["LG3"]][["homologue1"]],
           "LG3_homologue1_P1.pwd",
           "Please feed me to JoinMap")
## End(Not run)

Write TetraploidSNPMap input file

Description

Output the phased linkage map files into format readable by TetraploidSNPMap (Hackett et al. 2017) to perform QTL analysis.

Usage

write.TSNPM(
  phased.maplist,
  outputdir = "TetraploidSNPMap_QTLfiles",
  filename = "TSNPM",
  ploidy,
  verbose = FALSE
)

Arguments

phased.maplist

Phased maps in list format, the output of create_phased_maplist

outputdir

Directory to which TetraploidSNPMap files are written, by default written to "TetraploidSNPMap_QTLfiles" folder

filename

Character string of filename stem to write the output files to, by default "TSNPM" with linkage groups names appended

ploidy

The ploidy of the species, currently only 4 is supported by TetraploidSNPMap

verbose

Should messages be sent to stdout?

Value

NULL

Examples

## Not run: 
data("phased.maplist")
write.TSNPM(phased.maplist,ploidy=4)
## End(Not run)