| 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: | 2024-10-29 05:39:23 UTC |
| Source: | https://github.com/cran/polymapR |
Often there will be duplicate markers that can be put aside to speed up mapping. These may be added back to the maps afterwards.
add_dup_markers(maplist, bin_list, marker_assignments = NULL)add_dup_markers(maplist, bin_list, marker_assignments = NULL)
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 |
marker_assignments |
Optional argument to include the marker_assignments (output of |
A list with the following items:
List of maps, now with duplicate markers added
If required, marker assignment list with duplicate markers added
A dosage matrix for a random pairing tetraploid with five linkage groups.
ALL_dosages segregating_data screened_data screened_data2 screened_data3 TRI_dosagesALL_dosages segregating_data screened_data screened_data2 screened_data3 TRI_dosages
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
all_linkages_list_P1 all_linkages_list_P1_split all_linkages_list_P1_subsetall_linkages_list_P1 all_linkages_list_P1_split all_linkages_list_P1_subset
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_linkage_group quantifies per marker number of linkages to a linkage group and evaluates to which linkage group (and homologue(s)) the marker belongs.
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 )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 )
linkage_df |
A linkage |
LG_hom_stack |
A |
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 |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
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.
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)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)
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.
assign_SN_SN( linkage_df, LG_hom_stack, LOD_threshold, ploidy, LG_number, log = NULL )assign_SN_SN( linkage_df, LG_hom_stack, LOD_threshold, ploidy, LG_number, log = NULL )
linkage_df |
A |
LG_hom_stack |
A |
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. |
Returns a data.frame with the following columns:
SxN_Marker |
The markername |
Assigned_hom1 |
The assigned homologue |
Assigned_LG |
The assigned linkage group |
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)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)
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.
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 )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 )
cluster_stack |
A |
cluster_stack2 |
Optional. A |
linkage_df |
A linkage |
linkage_df2 |
Optional. A |
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_bridges |
The minimum number of bridge markers needed to assign two homologues together as coming from the same chromosomal linkage group.
See argument |
only_coupling |
Logical, should only coupling linkages be used in the process? By default |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
A data.frame with markers classified by homologue and linkage group.
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")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")
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.
calcSegtypeInfo(ploidy, ploidy2=NULL)calcSegtypeInfo(ploidy, ploidy2=NULL)
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. |
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).
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:
A vector of the ploidy+1 fractions of the dosages in the F1
An integer vector with the ratios as the simplest integers
A vector with the dosages present in this segtype
The allele frequency of the dosage allele in the F1
Boolean: does this segtype occur with polysomic inheritance?
Boolean: does this segtype occur with disomic inheritance?
Boolean: does this segtype occur with mixed inheritance (i.e. with polysomic inheritance in one parent and disomic inheritance in the other)?
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)
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
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"]])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"]])
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.
check_map( linkage_list, maplist, mapfn = "haldane", lod.thresh = 5, detail = 1, plottype = c("", "pdf", "png")[1], prefix = "" )check_map( linkage_list, maplist, mapfn = "haldane", lod.thresh = 5, detail = 1, plottype = c("", "pdf", "png")[1], prefix = "" )
linkage_list |
A named |
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. |
## Not run: data("maplist_P1","all_linkages_list_P1") check_map(linkage_list = all_linkages_list_P1, maplist = maplist_P1) ## End(Not run)## Not run: data("maplist_P1","all_linkages_list_P1") check_map(linkage_list = all_linkages_list_P1, maplist = maplist_P1) ## End(Not run)
Function to ensure there is consistent marker assignment to chromosomal linkage groups for biparental markers
check_marker_assignment( marker_assignment.P1, marker_assignment.P2, log = NULL, verbose = TRUE )check_marker_assignment( marker_assignment.P1, marker_assignment.P2, log = NULL, verbose = TRUE )
marker_assignment.P1 |
A marker assignment matrix for parent 1 with markernames as rownames and at least containing the column |
marker_assignment.P2 |
A marker assignment matrix for parent 2 with markernames as rownames and at least containing the column |
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? |
Returns a list of matrices with corrected marker assignments.
data("marker_assignments_P1"); data("marker_assignments_P2") check_marker_assignment(marker_assignments_P1,marker_assignments_P2)data("marker_assignments_P1"); data("marker_assignments_P2") check_marker_assignment(marker_assignments_P1,marker_assignments_P2)
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.
check_maxP(probgeno_df)check_maxP(probgeno_df)
probgeno_df |
A data frame as read from the scores file produced by function
|
This function does not return any value, is simply a visualisation tool to help assess data quality.
data("gp_df") check_maxP(gp_df)data("gp_df") check_maxP(gp_df)
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.
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 )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 )
input_type |
Can be either one of 'discrete' or 'probabilistic'. For the former (default), a |
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
|
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 |
polysomic |
if |
disomic |
if |
mixed |
if |
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 |
parentsScoredWithF1 |
|
shiftParents |
only used if parameter shiftmarkers is specified. If |
showAll |
(default |
append_shf |
if |
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.
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.
## 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$SampleName),c("P1","P2")),polysomic=T,disomic=F,mixed=F, ploidy=4) ## End(Not run)## Not run: data("ALL_dosages") chk1<-checkF1(input_type="discrete",dosage_matrix=ALL_dosages,parent1="P1",parent2="P2", F1=setdiff(colnames(ALL_dosages),c("P1","P2")),polysomic=T,disomic=F,mixed=F, ploidy=4) data("gp_df") chk1<-checkF1(input_type="probabilistic",probgeno_df=gp_df,parent1="P1",parent2="P2", F1=setdiff(levels(gp_df$SampleName),c("P1","P2")),polysomic=T,disomic=F,mixed=F, ploidy=4) ## End(Not run)
Example output of the checkF1 function
chk1chk1
An object of class list of length 2.
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.
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, ... )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, ... )
LG |
Integer. Linkage group to investigate. |
linkage_df |
A data.frame as output of |
LG_hom_stack |
A |
LOD_sequence |
A numeric or vector of numerics giving LOD threshold(s) at which clustering should be performed. |
modify_LG_hom_stack |
Logical. Should |
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: |
device |
Function of the graphics device to plot to (e.g. |
label.offset |
Offset of labels. Only used if |
cex.lab |
label character expansion. Only for |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
... |
Arguments passed to |
A modified LG_hom_stack data.frame if modify_LG_hom_stack = TRUE
data("SN_SN_P2", "LGHomDf_P2_1") #take only markers in coupling: SN_SN_P2_coupl <- SN_SN_P2[SN_SN_P2$phase=="coupling",] cluster_per_LG(LG = 2, linkage_df=SN_SN_P2_coupl, LG_hom_stack=LGHomDf_P2_1, LOD_sequence=seq(4,10,2), modify_LG_hom_stack=FALSE, nclust_out=4, network.layout="circular", device=NULL, label.offset=1.2, cex.lab=0.75)data("SN_SN_P2", "LGHomDf_P2_1") #take only markers in coupling: SN_SN_P2_coupl <- SN_SN_P2[SN_SN_P2$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_SN_markers clusters simplex nulliplex at different LOD scores.
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 )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 )
linkage_df |
A linkage data.frame as output of |
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, |
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 |
min_vertex_size |
Integer. The minimum vertex size. Only used if |
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). |
A (named) list of cluster stacks, each of which is a data.frame with columns "marker" and "cluster"
data("SN_SN_P1") cluster_list<-cluster_SN_markers(SN_SN_P1,LOD_sequence=c(4:10),parentname="P1",ploidy=4,LG_number=5)data("SN_SN_P1") cluster_list<-cluster_SN_markers(SN_SN_P1,LOD_sequence=c(4:10),parentname="P1",ploidy=4,LG_number=5)
This function allows the visualisation of connections between different maps, showing them side by side.
compare_maps( maplist, chm.wd = 0.2, bg.col = "white", links.col = "grey42", thin.links = NULL, type = "karyotype", ... )compare_maps( maplist, chm.wd = 0.2, bg.col = "white", links.col = "grey42", thin.links = NULL, type = "karyotype", ... )
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 |
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 |
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 |
NULL
data("map1","map2","map3") compare_maps(maplist=list("1a"=map1,"c08"=map2,"1b"=map3),bg.col=c("thistle","white","skyblue"))data("map1","map2","map3") compare_maps(maplist=list("1a"=map1,"c08"=map2,"1b"=map3),bg.col=c("thistle","white","skyblue"))
Assign markers to an LG based on consensus between two parents.
consensus_LG_assignment( P1_assigned, P2_assigned, LG_number, ploidy, consensus_file = NULL, log = NULL )consensus_LG_assignment( P1_assigned, P2_assigned, LG_number, ploidy, consensus_file = NULL, log = NULL )
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. |
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. |
data("P1_SxS_Assigned", "P2_SxS_Assigned_2") SxS_Assigned_list <- consensus_LG_assignment(P1_SxS_Assigned,P2_SxS_Assigned_2,5,4)data("P1_SxS_Assigned", "P2_SxS_Assigned_2") SxS_Assigned_list <- consensus_LG_assignment(P1_SxS_Assigned,P2_SxS_Assigned_2,5,4)
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
consensus_LG_names( modify_LG, template_SxS, modify_SxS, merge_LGs = TRUE, log = NULL )consensus_LG_names( modify_LG, template_SxS, modify_SxS, merge_LGs = TRUE, log = NULL )
modify_LG |
A |
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 |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
A modified modified_LG according to the template_SxS linkage group numbering
data("LGHomDf_P2_2", "P1_SxS_Assigned", "P2_SxS_Assigned") consensus_LGHomDf<-consensus_LG_names(LGHomDf_P2_2, P1_SxS_Assigned, P2_SxS_Assigned)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 which hold the same information and for which linkage calculations can be performed.
convert_marker_dosages( dosage_matrix, ploidy, ploidy2 = NULL, parent1 = "P1", parent2 = "P2", marker_conversion_info = FALSE, log = NULL )convert_marker_dosages( dosage_matrix, ploidy, ploidy2 = NULL, parent1 = "P1", parent2 = "P2", marker_conversion_info = FALSE, log = NULL )
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 |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
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.
data("ALL_dosages") conv<-convert_marker_dosages(dosage_matrix=ALL_dosages, ploidy = 4)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
convert_polyRAD(RADdata)convert_polyRAD(RADdata)
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. |
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)
data("exampleRAD_mapping") convert_polyRAD(RADdata = exampleRAD_mapping)data("exampleRAD_mapping") convert_polyRAD(RADdata = exampleRAD_mapping)
Convert (probabilistic) genotype calling results from updog to input compatible with polymapR.
convert_updog(mout, output_type = "discrete", min_prob = 0.7)convert_updog(mout, output_type = "discrete", min_prob = 0.7)
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. |
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)
data("mout") convert_updog(mout)data("mout") convert_updog(mout)
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.
correctDosages(chk, dosage_matrix, parent1, parent2, ploidy, polysomic=TRUE, disomic=FALSE, mixed=FALSE, absent.threshold=0.04)correctDosages(chk, dosage_matrix, parent1, parent2, ploidy, polysomic=TRUE, disomic=FALSE, mixed=FALSE, absent.threshold=0.04)
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 |
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.
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_phased_maplist is a function for creating a phased maplist, using
integrated map positions and original marker dosages.
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 )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 )
input_type |
Can be either one of 'discrete' or 'probabilistic'. For the former (default), at least |
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 |
dosage_matrix.orig |
Optional, by default |
probgeno_df |
Probabilistic genotypes, for description see e.g. |
chk |
Output list as returned by function |
remove_markers |
Optional vector of marker names to remove from the maps. Default is |
original_coding |
Logical. Should the phased map use the original marker coding or not? By default |
N_linkages |
Number of significant linkages (as defined in |
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 |
marker_assignment.1 |
A marker assignment matrix for parent 1 with markernames as rownames and at least containing the column |
marker_assignment.2 |
A marker assignment matrix for parent 2 with markernames as rownames and at least containing the column |
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 |
log |
Character string specifying the log filename to which standard output should be written. If |
verbose |
Logical, by default |
## 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)## 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)
createTetraOriginInput is a function for creating an input file for TetraOrigin, combining
map positions with marker dosages.
createTetraOriginInput( maplist, dosage_matrix, bin_size = NULL, bounds = NULL, remove_markers = NULL, outdir = "TetraOrigin", output_stem = "TetraOrigin_input", plot_maps = TRUE, log = NULL )createTetraOriginInput( maplist, dosage_matrix, bin_size = NULL, bounds = NULL, remove_markers = NULL, outdir = "TetraOrigin", output_stem = "TetraOrigin_input", plot_maps = TRUE, log = NULL )
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 |
bin_size |
Numeric. Size (in cM) of the bins to include. If |
bounds |
Numeric vector. If |
remove_markers |
Optional vector of marker names to remove from the maps. Default is |
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 |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
## Not run: data("integrated.maplist","ALL_dosages") createTetraOriginInput(maplist=integrated.maplist,dosage_matrix=ALL_dosages,bin_size=10) ## End(Not run)## Not run: data("integrated.maplist","ALL_dosages") createTetraOriginInput(maplist=integrated.maplist,dosage_matrix=ALL_dosages,bin_size=10) ## End(Not run)
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).
define_LG_structure(cluster_list, LOD_chm, LOD_hom, LG_number, log = NULL)define_LG_structure(cluster_list, LOD_chm, LOD_hom, LG_number, log = NULL)
cluster_list |
A list of cluster_stacks, the output of |
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. |
A data.frame with markers classified by homologue and linkage group.
data("P1_homologues") ChHomDf<-define_LG_structure(cluster_list=P1_homologues,LOD_chm=3.5,LOD_hom=5,LG_number=5)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
exampleRAD_mappingexampleRAD_mapping
An object of class RADdata of length 23.
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.
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, ... )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, ... )
input_type |
Can be either one of 'discrete' or 'probabilistic'. For the former (default), |
marker_assignment |
A marker assignment matrix with markernames as rownames and at least containing the column |
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
|
chk |
Output list as returned by function |
marker_combinations |
A matrix with four columns specifying marker combinations to calculate linkage.
If |
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 |
Integer, by default |
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 |
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 |
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 |
Returns a matrix with marker assignments. Number of linkages of 1.0 markers are artificial.
## 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)## 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
get_markertype_combinations(ploidy, pairing, nonavailable_combinations = TRUE)get_markertype_combinations(ploidy, pairing, nonavailable_combinations = TRUE)
ploidy |
Ploidy level |
pairing |
Type of pairing. Either "random" or "preferential". |
nonavailable_combinations |
Logical. Should nonavailable combinations be plotted with grey lines? |
A matrix with two columns. Each row represents a function with the first and second markertype.
get_markertype_combinations(ploidy = 4, pairing = "random")get_markertype_combinations(ploidy = 4, pairing = "random")
An example of a genotype probability data frame
gp_dfgp_df
Data frame
Function to generate an overview of genotype probabilities across a population
gp_overview(probgeno_df, cutoff = 0.7, alpha = 0.1)gp_overview(probgeno_df, cutoff = 0.7, alpha = 0.1)
probgeno_df |
A data frame as read from the scores file produced by function
|
cutoff |
a filtering threshold, by default 0.7, to identify individuals with more than |
alpha |
Option to specify the quantile of an individuals' scores that will be used to test against |
a list with the following elements:
Input data, filtered based on chosen cutoff
data.frame containing summary statistics of each individual's genotyping scores
## Not run: data("gp_df") gp_overview(gp_df) ## End(Not run)## Not run: data("gp_df") gp_overview(gp_df) ## End(Not run)
A list of objects needed to build the probabilistic genotype vignette
gp_vignette_datagp_vignette_data
An object of class list of length 15.
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.
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, ... )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, ... )
input_type |
Can be either one of 'discrete' or 'probabilistic'. For the former (default), |
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
|
chk |
Output list as returned by function |
assigned_list |
List of |
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 |
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 |
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 |
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 |
A data.frame specifying marker assignments to linkage group and homologue.
## 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)## 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
integrated.maplistintegrated.maplist
An object of class list of length 5.
data.frame specifying the assigned homologue and linkage group number per SxN markerA data.frame specifying the assigned homologue and linkage group number per SxN marker
LGHomDf_P1_1 LGHomDf_P2_1 LGHomDf_P2_2LGHomDf_P1_1 LGHomDf_P2_1 LGHomDf_P2_2
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.
linkage is used to calculate recombination frequency, LOD and phase within one type of marker or between two types of markers.
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 )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 )
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 |
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 |
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 |
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 |
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 |
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 |
verbose |
Should messages be sent to stdout? |
full_output |
Logical, by default |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
Returns a data.frame with columns:
first marker of comparison. If markertype2 is specified, it has the type of markertype1.
second marker of comparison. It has the type of markertype2 if specified.
(estimated) recombinations frequency
(estimated) LOD score
phase between markers
data("screened_data3") SN_SN_P1 <- linkage(dosage_matrix = screened_data3, markertype1 = c(1,0), which_parent = 1, ploidy = 4, pairing = "random", ncores = 1 )data("screened_data3") SN_SN_P1 <- linkage(dosage_matrix = screened_data3, markertype1 = c(1,0), which_parent = 1, ploidy = 4, pairing = "random", ncores = 1 )
linkage.gp is used to calculate recombination frequency, LOD and phase within one type of marker or between two types of markers.
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 )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 )
probgeno_df |
A data frame as read from the scores file produced by function
|
chk |
Output list as returned by function |
pardose |
Option to include the most likely (discrete) parental dosage scores, used mainly for internal calls of this function. By default |
markertype1 |
A vector of length 2 specifying the first markertype to compare. The first element specifies the dosage in |
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 |
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 |
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 |
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 |
verbose |
Should messages be sent to stdout? |
check_qall_mult |
Check the |
method |
Either |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
Returns a data.frame with columns:
first marker of comparison. If markertype2 is specified, it has the type of markertype1.
second marker of comparison. It has the type of markertype2 if specified.
recombination frequency
LOD score associated with r
phase between markers
data("gp_df","chk1") SN_SN_P1.gp <- linkage.gp(probgeno_df = gp_df, chk = chk1, markertype1 = c(1,0), target_parent = "P1")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
map1map1
An object of class data.frame with 100 rows and 2 columns.
A sample map
map2map2
An object of class data.frame with 100 rows and 2 columns.
A sample map
map3map3
An object of class data.frame with 60 rows and 2 columns.
A list of maps of one parent
maplist_P1 maplist_P1_subset maplist_P2_subsetmaplist_P1 maplist_P1_subset maplist_P2_subset
An object of class list of length 5.
An object of class list of length 5.
An object of class list of length 5.
marker_binning allows for binning of very closely linked markers and choses one representative.
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 )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 )
dosage_matrix |
A dosage |
linkage_df |
A linkage |
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 |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
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. |
data("screened_data3", "all_linkages_list_P1_split") binned_markers<-marker_binning(screened_data3, all_linkages_list_P1_split[["LG2"]][["homologue3"]])data("screened_data3", "all_linkages_list_P1_split") binned_markers<-marker_binning(screened_data3, all_linkages_list_P1_split[["LG2"]][["homologue3"]])
Gives a frequency table of different markertypes, relative frequency per markertype of incompatible offspring and the names of incompatible progeny.
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 )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 )
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 |
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 |
shortform |
Logical, by default |
log |
Character string specifying the log filename to which standard output should be written. If |
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. |
data("ALL_dosages") summary_list<-marker_data_summary(dosage_matrix = ALL_dosages, ploidy = 4)data("ALL_dosages") summary_list<-marker_data_summary(dosage_matrix = ALL_dosages, ploidy = 4)
Create multidimensional scaling maps from a list of linkages
MDSMap_from_list( linkage_list, write_to_file = FALSE, mapdir = "mapping_files_MDSMap", plot_prefix = "", log = NULL, ... )MDSMap_from_list( linkage_list, write_to_file = FALSE, mapdir = "mapping_files_MDSMap", plot_prefix = "", log = NULL, ... )
linkage_list |
A named |
write_to_file |
Should output be written to a file? By default |
mapdir |
Directory to which map input files are initially written. Also used for output if |
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 |
## Not run: data("all_linkages_list_P1") maplist_P1 <- MDSMap_from_list(all_linkages_list_P1[1]) ## End(Not run)## Not run: data("all_linkages_list_P1") maplist_P1 <- MDSMap_from_list(all_linkages_list_P1[1]) ## End(Not run)
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.
merge_homologues(LG_hom_stack, ploidy, LG, mergeList = NULL, log = NULL)merge_homologues(LG_hom_stack, ploidy, LG, mergeList = NULL, log = NULL)
LG_hom_stack |
A |
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 |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
A modified LG_hom_stack
data("LGHomDf_P2_1") merged<-merge_homologues(LGHomDf_P2_1,ploidy=4,LG=2,mergeList=list(c(1,5)))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
moutmout
An object of class multidog of length 2.
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.
overviewSNlinks( linkage_df, LG_hom_stack, LG, LOD_threshold, ymax = NULL, log = NULL )overviewSNlinks( linkage_df, LG_hom_stack, LG, LOD_threshold, ymax = NULL, log = NULL )
linkage_df |
A data.frame as output of |
LG_hom_stack |
A data.frame with a column "SxN_Marker" specifying markernames, a column "homologue" specifying homologue cluster and "LG" specifying linkage group. |
LG |
Integer. Linkage group number of interest. |
LOD_threshold |
Numeric. LOD threshold of linkages which are plotted. |
ymax |
Maximum y-limit of the plots. |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
data("SN_SN_P1", "LGHomDf_P1_1") overviewSNlinks(linkage_df=SN_SN_P1, LG_hom_stack=LGHomDf_P1_1, LG=5, LOD_threshold=3)data("SN_SN_P1", "LGHomDf_P1_1") overviewSNlinks(linkage_df=SN_SN_P1, LG_hom_stack=LGHomDf_P1_1, LG=5, LOD_threshold=3)
A list of cluster stacks at different LOD scores
P1_homologues P2_homologues P2_homologues_triploidP1_homologues P2_homologues P2_homologues_triploid
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.
data.frame with marker assignmentsA data.frame with marker assignments
P1_SxS_Assigned P2_SxS_Assigned P2_SxS_Assigned_2 P1_DxN_Assigned P2_DxN_Assigned marker_assignments_P1 marker_assignments_P2P1_SxS_Assigned P2_SxS_Assigned P2_SxS_Assigned_2 P1_DxN_Assigned P2_DxN_Assigned marker_assignments_P1 marker_assignments_P2
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.
This group of functions is called by linkage.
x |
A frequency table of the different classes of dosages in the progeny. The column names start with |
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). |
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. |
possible_phases |
The phases between markers that are possible. Same order and length as column names of output matrices. |
Plots and returns frequency information for each markertype.
parental_quantities( dosage_matrix, parent1 = "P1", parent2 = "P2", log = NULL, ... )parental_quantities( dosage_matrix, parent1 = "P1", parent2 = "P2", log = NULL, ... )
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 |
A named vector containing the frequency of each markertype in the dataset.
data("ALL_dosages","screened_data") parental_quantities(dosage_matrix=ALL_dosages) parental_quantities(dosage_matrix=screened_data)data("ALL_dosages","screened_data") parental_quantities(dosage_matrix=ALL_dosages) parental_quantities(dosage_matrix=screened_data)
Principal component analysis in order to identify individuals that deviate from the population.
PCA_progeny(dosage_matrix, highlight = NULL, colors = NULL, log = NULL)PCA_progeny(dosage_matrix, highlight = NULL, colors = NULL, log = NULL)
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 |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
Missing values are imputed by taking the mean of marker dosages per marker.
data("ALL_dosages") PCA_progeny(dosage_matrix=ALL_dosages, highlight=list(c("P1", "P2")), colors="red")data("ALL_dosages") PCA_progeny(dosage_matrix=ALL_dosages, highlight=list(c("P1", "P2")), colors="red")
phase_SN_diploid phases simplex x nulliplex markers for a diploid parent.
phase_SN_diploid( linkage_df, cluster_list, LOD_chm = 3.5, LG_number, independence_LOD = FALSE, log = NULL )phase_SN_diploid( linkage_df, cluster_list, LOD_chm = 3.5, LG_number, independence_LOD = FALSE, log = NULL )
linkage_df |
A linkage data.frame as output of |
cluster_list |
A list of cluster_stacks, the output of |
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, |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout (console). |
A data.frame with markers classified by homologue and linkage group.
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)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
phased.maplistphased.maplist
An object of class list of length 5.
Plot homologue position versus integrated positions
plot_hom_vs_LG(map_df, maplist_homologue)plot_hom_vs_LG(map_df, maplist_homologue)
map_df |
A dataframe of a map that defines a linkage group. |
maplist_homologue |
A list of maps were each item represents a homoloogue. |
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"]])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"]])
Makes a simple plot of a list of generated linkage maps
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) )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) )
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 |
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 |
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 |
legend.args |
Optional extra arguments to pass to |
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)))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)))
plot_phased_maplist is a function for visualising a phased maplist, the output of
create_phased_maplist
plot_phased_maplist( phased.maplist, ploidy, ploidy2 = NULL, cols = c("black", "darkred", "navyblue"), width = 0.2, mapTitles = NULL )plot_phased_maplist( phased.maplist, ploidy, ploidy2 = NULL, cols = c("black", "darkred", "navyblue"), width = 0.2, mapTitles = NULL )
phased.maplist |
A list of phased linkage maps, the output of |
ploidy |
Integer. Ploidy of the organism. |
ploidy2 |
Optional integer, by default |
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. |
data("phased.maplist") plot_phased_maplist(phased.maplist, ploidy = 4)data("phased.maplist") plot_phased_maplist(phased.maplist, ploidy = 4)
r_LOD_plot plots r versus LOD, colour separated for different phases.
r_LOD_plot( linkage_df, plot_main = "", chm = NA, r_max = 0.5, tidyplot = TRUE, nbins = 200 )r_LOD_plot( linkage_df, plot_main = "", chm = NA, r_max = 0.5, tidyplot = TRUE, nbins = 200 )
linkage_df |
A linkage data.frame as output of |
plot_main |
A character string specifying the main title |
chm |
Integer specifying chromosome |
r_max |
Maximum r value to plot |
tidyplot |
If |
nbins |
The number of bins in each direction, passed to ggplot2::geom_hex. Only used if |
data("SN_SN_P1") r_LOD_plot(SN_SN_P1)data("SN_SN_P1") r_LOD_plot(SN_SN_P1)
This group of functions is called by linkage.
r2_1.0_1.0(x, ncores = 1) r2_1.0_1.1(x, ncores = 1) r2_1.1_1.1(x, ncores = 1)r2_1.0_1.0(x, ncores = 1) r2_1.0_1.1(x, ncores = 1) r2_1.1_1.1(x, ncores = 1)
x |
A frequency table of the different classes of dosages in the progeny. The column names start with |
ncores |
Number of cores to use for parallel processing (deprecated). |
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. |
possible_phases |
The phases between markers that are possible. Same order and length as column names of output matrices. |
This group of functions is called by linkage.
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)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)
x |
A frequency table of the different classes of dosages in the progeny. The column names start with |
ncores |
Number of cores to use for parallel processing (deprecated). |
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. |
possible_phases |
The phases between markers that are possible. Same order and length as column names of output matrices. |
This group of functions is called by linkage.
x |
A frequency table of the different classes of dosages in the progeny. The column names start with |
ncores |
Number of cores to use for parallel processing (deprecated). |
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. |
possible_phases |
The phases between markers that are possible. Same order and length as column names of output matrices. |
This group of functions is called by linkage.
x |
A frequency table of the different classes of dosages in the progeny. The column names start with |
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. |
possible_phases |
The phases between markers that are possible. Same order and length as column names of output matrices. |
screen_for_duplicate_individuals identifies and merges duplicate individuals.
screen_for_duplicate_individuals( dosage_matrix, cutoff = NULL, plot_cor = TRUE, log = NULL )screen_for_duplicate_individuals( dosage_matrix, cutoff = NULL, plot_cor = TRUE, log = NULL )
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. |
A matrix similar to dosage_matrix, with merged duplicate individuals.
## Not run: #user input: data("segregating_data") screen_for_duplicate_individuals(dosage_matrix=segregating_data,cutoff=0.9,plot_cor=TRUE) ## End(Not run)## 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.gp identifies and merges duplicate individuals based on probabilistic genotypes.
See screen_for_duplicate_individuals for the original function.
screen_for_duplicate_individuals.gp( probgeno_df, ploidy, parent1 = "P1", parent2 = "P2", F1, cutoff = 0.95, plot_cor = TRUE, log = NULL )screen_for_duplicate_individuals.gp( probgeno_df, ploidy, parent1 = "P1", parent2 = "P2", F1, cutoff = 0.95, plot_cor = TRUE, log = NULL )
probgeno_df |
A data frame as read from the scores file produced by function
|
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 |
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 |
A data frame similar to input probgeno_df, but with duplicate individuals merged.
screen_for_duplicate_markers identifies and merges duplicate markers.
screen_for_duplicate_markers( dosage_matrix, merge_NA = TRUE, plot_cluster_size = TRUE, ploidy, ploidy2 = NULL, LG_number, estimate_bin_size = FALSE, log = NULL )screen_for_duplicate_markers( dosage_matrix, merge_NA = TRUE, plot_cluster_size = TRUE, ploidy, ploidy2 = NULL, LG_number, estimate_bin_size = FALSE, log = NULL )
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, |
plot_cluster_size |
Logical. Should an informative plot about duplicate cluster size be given? By default, |
ploidy |
Ploidy level of parent 1. Only needed if |
ploidy2 |
Integer, by default |
LG_number |
Expected number of chromosomes (linkage groups). Only needed if |
estimate_bin_size |
Logical, by default |
log |
Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout. |
A list containing:
list of binned markers. The list names are the representing markers. This information can later be used to enrich the map with binned markers.
dosage_matrix with merged duplicated markers. The markers will be given the name of the marker with least missing values.
data("screened_data3") dupmscreened <- screen_for_duplicate_markers(screened_data3)data("screened_data3") dupmscreened <- screen_for_duplicate_markers(screened_data3)
screen_for_NA_values identifies and can remove rows or columns of a marker dataset based on the relative frequency of missing values.
screen_for_NA_values( dosage_matrix, margin = 1, cutoff = NULL, parentnames = c("P1", "P2"), plot_breakdown = FALSE, log = NULL, print.removed = TRUE )screen_for_NA_values( dosage_matrix, margin = 1, cutoff = NULL, parentnames = c("P1", "P2"), plot_breakdown = FALSE, log = NULL, print.removed = TRUE )
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? |
A matrix similar to dosage_matrix, with rows or columns removed that had a higher missing value frequency than specified.
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)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)
data.frame.A linkage data.frame.
SN_SN_P1 SN_SN_P2 SN_SS_P1 SN_SS_P2 SN_DN_P1 SN_DN_P2 SN_SN_P2_triploidSN_SN_P1 SN_SN_P2 SN_SS_P1 SN_SS_P2 SN_DN_P1 SN_DN_P2 SN_SN_P2_triploid
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.
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.
SNSN_LOD_deviations( linkage_df, ploidy, N, plot_expected = TRUE, alpha = c(0.05, 0.2), phase = c("coupling", "repulsion") )SNSN_LOD_deviations( linkage_df, ploidy, N, plot_expected = TRUE, alpha = c(0.05, 0.2), phase = c("coupling", "repulsion") )
linkage_df |
A linkage data.frame as output of |
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). |
A vector of deviations in LOD scores outside the range defined by tolerances input alpha
data("SN_SN_P1") SNSN_LOD_deviations(SN_SN_P1,ploidy = 4, N = 198)data("SN_SN_P1") SNSN_LOD_deviations(SN_SN_P1,ploidy = 4, N = 198)
Identify closely-mapped repulsion-phase simplex x nulliplex markers and test these for preferential pairing, including estimating a preferential pairing parameter.
test_prefpairing( dosage_matrix, maplist, LG_hom_stack, target_parent = "P1", other_parent = "P2", ploidy, min_cM = 0.5, adj.method = "fdr", verbose = TRUE )test_prefpairing( dosage_matrix, maplist, LG_hom_stack, target_parent = "P1", other_parent = "P2", ploidy, min_cM = 0.5, adj.method = "fdr", verbose = TRUE )
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. |
LG_hom_stack |
A |
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 |
verbose |
Should messages be sent to stdout? If |
data("ALL_dosages","integrated.maplist","LGHomDf_P1_1") P1pp <- test_prefpairing(ALL_dosages,integrated.maplist,LGHomDf_P1_1,ploidy=4)data("ALL_dosages","integrated.maplist","LGHomDf_P1_1") P1pp <- test_prefpairing(ALL_dosages,integrated.maplist,LGHomDf_P1_1,ploidy=4)
Write a nested list into a directory structure
write_nested_list( nested_list, directory, save_as_object = FALSE, object_prefix = directory, extension = if (save_as_object) ".Rdata" else ".txt", ... )write_nested_list( nested_list, directory, save_as_object = FALSE, object_prefix = directory, extension = if (save_as_object) ".Rdata" else ".txt", ... )
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 |
extension |
Character. File extension. Default is ".txt". |
... |
Arguments passed to |
## 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)## 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)
A wrapper for write.pwd, which allows to write multiple pwd files with a directory structure according to the nested linkage list.
write_pwd_list( linkages_list, target_parent, binned = FALSE, dir = getwd(), log = NULL )write_pwd_list( linkages_list, target_parent, binned = FALSE, dir = getwd(), log = NULL )
linkages_list |
A nested |
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. |
## Not run: data("all_linkages_list_P1_split") write_pwd_list(all_linkages_list_P1_split, target_parent="P1", binned=FALSE) ## End(Not run)## 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 a .mct file of a maplist for external plotting with MapChart software (Voorrips ).
write.mct( maplist, mapdir = "mapping_files_MDSMap", file_info = paste("; MapChart file created on", Sys.Date()), filename = "MapFile", precision = 2, showMarkerNames = FALSE )write.mct( maplist, mapdir = "mapping_files_MDSMap", file_info = paste("; MapChart file created on", Sys.Date()), filename = "MapFile", precision = 2, showMarkerNames = FALSE )
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 |
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 |
## Not run: data("integrated.maplist") write.mct(integrated.maplist) ## End(Not run)## Not run: data("integrated.maplist") write.mct(integrated.maplist) ## End(Not run)
Output of this function allows to use JoinMap to perform the marker ordering step.
write.pwd(linkage_df, pwd_file, file_info, log = NULL)write.pwd(linkage_df, pwd_file, file_info, log = NULL)
linkage_df |
A linkage |
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. |
## 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)## 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)
Output the phased linkage map files into format readable by TetraploidSNPMap (Hackett et al. 2017) to perform QTL analysis.
write.TSNPM( phased.maplist, outputdir = "TetraploidSNPMap_QTLfiles", filename = "TSNPM", ploidy, verbose = FALSE )write.TSNPM( phased.maplist, outputdir = "TetraploidSNPMap_QTLfiles", filename = "TSNPM", ploidy, verbose = FALSE )
phased.maplist |
Phased maps in list format, the output of |
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? |
NULL
## Not run: data("phased.maplist") write.TSNPM(phased.maplist,ploidy=4) ## End(Not run)## Not run: data("phased.maplist") write.TSNPM(phased.maplist,ploidy=4) ## End(Not run)