Package 'polymapR'

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

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

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.

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.

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

Value

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

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)

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

Value

Returns a data.frame with the following columns:

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)

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

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")

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

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"]])

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

Examples

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

## End(Not run)

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

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)

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

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)

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

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)

Description

Example output of the checkF1 function

Usage

chk1

Format

An object of class list of length 2.

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

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)

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

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)

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

Value

NULL

Examples

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

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

Value

Returns a list containing the following components:

Examples

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

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

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)

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

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)

Description

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

Usage

convert_polyRAD(RADdata)

Arguments

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)

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

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)

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

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)

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

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)

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

Examples

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

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

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)

Description

Example output dataset of polyRAD::PipelineMapping2Parents function

Usage

exampleRAD_mapping

Format

An object of class RADdata of length 23.

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

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)

Description

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

Usage

get_markertype_combinations(ploidy, pairing, nonavailable_combinations = TRUE)

Arguments

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")

Description

An example of a genotype probability data frame

Usage

gp_df

Format

Data frame

Description

Function to generate an overview of genotype probabilities across a population

Usage

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

Arguments

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)

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.

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

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)

Description

A nested list with integrated maps

Usage

integrated.maplist

Format

An object of class list of length 5.

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.

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

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
                   )

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

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")

Description

A sample map

Usage

map1

Format

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

Description

A sample map

Usage

map2

Format

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

Description

A sample map

Usage

map3

Format

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

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.

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

Value

A list with the following components:

Examples

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

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

Value

Returns a list containing the following components:

Examples

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

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

Examples

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

## End(Not run)

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

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)))

Description

Example output dataset of updog::multidog function

Usage

mout

Format

An object of class multidog of length 2.

Description

overviewSNlinks is written to enable merging of homologue fractions. Fractions of homologues will have more markers in coupling than in repulsion, whereas separate homologues will only have markers in repulsion.

Usage

overviewSNlinks(
  linkage_df,
  LG_hom_stack,
  LG,
  LOD_threshold,
  ymax = NULL,
  log = NULL
)

Arguments

Examples

data("SN_SN_P1", "LGHomDf_P1_1")
overviewSNlinks(linkage_df=SN_SN_P1,
               LG_hom_stack=LGHomDf_P1_1,
               LG=5,
               LOD_threshold=3)

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.

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.

Description

This group of functions is called by linkage.

Arguments

Value

A list with the following items:

Description

Plots and returns frequency information for each markertype.

Usage

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

Arguments

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)

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

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")

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

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)

Description

A list of phased maps

Usage

phased.maplist

Format

An object of class list of length 5.

Description

Plot homologue position versus integrated positions

Usage

plot_hom_vs_LG(map_df, maplist_homologue)

Arguments

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"]])

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

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)))

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

Examples

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

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

Examples

data("SN_SN_P1")
r_LOD_plot(SN_SN_P1)

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

Value

A list with the following items:

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

Value

A list with the following items:

Description

This group of functions is called by linkage.

Arguments

Value

A list with the following items:

Description

This group of functions is called by linkage.

Arguments

Value

A list with the following items:

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

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)

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

Value

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

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

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)

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

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)

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.

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

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)

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

Examples

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

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

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)

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

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)

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

Examples

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

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

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)

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

Value

NULL

Examples

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