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
)Can be either one of 'discrete' or 'probabilistic'. For the former (default), a dosage_matrix must be supplied,
while for the latter a probgeno_df must be supplied.
An integer matrix with markers in rows and individuals in columns.
A data frame as read from the scores file produced by function
saveMarkerModels of R package fitPoly, or alternatively, a data frame containing the following columns:
SampleName Name of the sample (individual)
MarkerName Name of the marker
P0 Probabilities of dosage score '0'
P1... Probabilities of dosage score '1' etc. (up to max offspring dosage, e.g. P4 for tetraploid population)
maxP Maximum genotype probability identified for a particular individual and marker combination
maxgeno Most probable dosage for a particular individual and marker combination
geno
Most probable dosage for a particular individual and marker combination, if maxP exceeds a user-defined threshold (e.g. 0.9), otherwise NA
character vector with the sample names of parent 1
character vector with the sample names of parent 2
character vector with the sample names of the F1 individuals
character vector with the sample names of any other
ancestors or other samples of interest. The dosages of these samples will
be shown in the output (shifted if shiftParents TRUE) but they are not used
in the selection of the segregation type.
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)
if TRUE at least all disomic segtypes are considered (see
polysomic)
if TRUE at least all mixed segtypes are considered (see
polysomic). A mixed segtype occurs when inheritance in one parent is
polysomic (random chromosome pairing) and in the other parent disomic (fully
preferential chromosome pairing)
The ploidy of parent 1 (must be even, 2 (diploid) or larger).
The ploidy of parent 2. If omitted it is assumed to be equal to ploidy.
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
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.
a minimum threshold value for the Pvalue of the bestParentfit segtype (with a smaller Pvalue the q1 quality parameter will be set to 0)
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)
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)
if specified, shiftmarkers must be a data frame with
columns MarkerName and shift; for the markernames that match exactly
(upper/lowercase etc) those in the input (either dosage_matrix or probgeno_df), the dosages are increased by the
amount specified in column shift,
e.g. if shift is -1, dosages 2..ploidy are converted to 1..(ploidy-1)
and dosage 0 is a combination of old dosages 0 and 1, for all samples.
The segregation check is then performed with the shifted dosages.
A shift=NA is allowed, these markers will not be shifted.
The sets of markers in the input (either dosage_matrix or probgeno_df) and shiftmarkers
may be different, but markers may occur only once in shiftmarkers.
A column shift is added at the end of the returned data frame.
If parameter shiftParents is TRUE, the parental and ancestor scores are
shifted as the F1 scores, if FALSE they are not shifted.
TRUE if parents are scored in the same experiment
and the same fitPoly run as the F1, else FALSE.
If TRUE, their fraction missing scores
and conflicts tell something about the quality of the scoring. If FALSE
(e.g. when the F1 is triploid and the parents are diploid and tetraploid) the
quality of the F1 scores can be independent of that of the parents.
If not specified, TRUE is assumed if ploidy2 == ploidy and FALSE if
ploidy2 != ploidy
only used if parameter shiftmarkers is specified. If TRUE,
apply the shifts also to the parental and ancestor scores.
By default TRUE if parentsScoredWithF1 is TRUE
(default FALSE) if TRUE, for each segtype 3 columns
are added to the returned data frame with the frqInvalid, Pvalue and
matchParents values for these segtype (see the description of the return value)
if TRUE and parameter shiftmarkers is specified, _shf is
appended to all marker names where shift is not 0. This is not required for
any of the functions in this package but may prevent duplicated marker names
when using other software.
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.
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.
# 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)
# }
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