Description Usage Arguments Details Value Author(s) References Examples
All individuals in dat
whose population matches one of refpopnames
will be included. Leaveoneout will be used when calculating the loggenotype
probability for individual with respect to their own reference population,
if specified in the inputs (default is NON leaveoneout).
1 2 3 4 5 6 7 8 9 10  geneplot(dat, refpopnames, locnames, includepopnames = NULL,
prior = "Rannala", saddlepoint = T, leave_one_out = F,
logten = T, min_loci = 6, quantiles = c(0.01, 1), Ndraw = 1e+05,
plotit = T, plot_type = switch(as.character(length(refpopnames)), `2`
= "twopop", "manypop"), plot_bars = F, colvec = NA, shapevec = NA,
mark_impute = F, txt = "points", use_legend = T,
legend_pos = "bottomleft", xyrange = NULL, orderpop = NULL,
axispop = NULL, axis_labels = NULL, short_axis_labels = F,
grayscale_quantiles = F, dim1 = 1, dim2 = 2,
layout_already_set = F, cexpts = 1.4)

dat 
The data, in a data frame, with two columns labelled as 'id' and
'pop', and with two additional columns per locus. Missing data at any
locus should be marked as '0' for each allele.
The locus columns must be labelled in the format Loc1.a1, Loc1.a2,
Loc2.a1, Loc2.a2, etc.
Missing data must be for BOTH alleles at any locus. Missing data for
ONE allele at any locus will produce an error.
See 
refpopnames 
Character vector of reference population names, that must
match the values in the 'pop' column of 
locnames 
Character vector, names of the loci, which must match the
column names in the data so e.g. if dat has columns
id, pop, EV1.a1, EV1.a2, EV14.a1, EV14.a2, etc.
then you could use 'locnames = c("EV1","EV14") etc.
The locnames do not need to be in any particular order but all of them
must be in 
includepopnames 
Character vector (default NULL) of population names to
be included in the calculations. All individuals with 'pop' value in

prior 
(default="Rannala") String, either "Rannala" or "Baudouin", giving the choice of prior parameter for the Dirichlet priors for the allele frequency estimates. Both options define parameter values that depend on the number of alleles at each locus, k. "Baudouin" gives slightly more weight to rare alleles than "Rannala" does, or less weight to the data, so Baudouin may be more suitable for small reference samples, but there is no major difference between them. For more details, see McMillan and Fewster (2017), Biometrics. Additional options are "Half" or "Quarter" which specify parameters 1/2 or 1/4, respectively. These options have priors whose parameters do not depend on the number of alleles at each locus, and so may be more suitable for microsatellite data with varying numbers of alleles at each locus. 
saddlepoint 
Boolean (default TRUE), indicates whether or not to use the saddlepoint method for imputing missing data/leaveoneout results. For more details, see McMillan and Fewster (2017), Biometrics. 
leave_one_out 
Boolean (default FALSE), indicates whether or not to calculate leaveoneout results for any individual from the reference pops. If TRUE, any individual from a reference population will have their LogGenotypeProbability with respect to their own reference population after temporarily removing the individual's genotype from the sample data for that reference population. The individual's LogGenotypeProbabilities with respect to all populations they are not a member of will be calculated as normal. We STRONGLY RECOMMEND using leaveoneout=TRUE for any small reference samples (<30). 
logten 
(default TRUE) Boolean, indicates whether to use base 10 for the logarithms, or base e (i.e. natural logarithms). logten=TRUE is default because it's easier to recalculate the original nonlog numbers in your head when looking at the plots. Use FALSE for natural logarithms. 
min_loci 
(default 6) is the minimum number of loci that an individual
must have (within the set of loci defined in 
quantiles 
(default c(0.01,1.00)) Vector of probabilities, specifying the quantiles of the posterior distribution to be calculated. Default plots the 1% and 100% quantiles of the LogGenotypeProbability distributions for each of the reference populations. For example, only 1% of all possible genotypes that could arise from the given population will have LogGenotypeProbabilities below the 1% quantile, and 99% of all possible genotypes arising from that population will have LogGenotypeProbabilities above the 1% quantile. The 100% quantile is the maximum possible LogGenotypeProbability that any genotype can have with respect to this population. Quantile values will be provided as attributes to the output object of calc_logprob (see the Value section.) If no quantiles are wanted, supply quantiles=NULL. 
Ndraw 
(default 100000) is only used if saddlepoint=FALSE. Defines the number of draws that will be taken from the distribution of logposterior genotype probabilities for each reference population. These draws, i.e. simulated genotypes from the posterior distributions of the reference populations, are used when imputing the loggenotypeprobabilities for individuals with missing data, or when calculating quantiles of the distribution. For more details, see McMillan and Fewster (2017), Biometrics. 
plotit 
(default=TRUE) is whether to produce a plot, or whether to just do the calculations and spit out the table of loggenotype probabilities. FALSE just runs calc_logprob but not plot_logprob, so is equivalent to a call to calc_logprob. 
plot_type 
(default NULL) Can be used to specify "twopop" or "manypop"
plots. Defaults to "twopop" for 2 reference pops (i.e. 2 pops listed in

plot_bars 
(default FALSE) Specify what type of plot to use for >2
reference populations.
FALSE (default) plots PCA of the outputs from 
colvec 
(default=grDevices::rainbow(npop, s=0.5, start=0.625, end=0.42)) Vector
of colours for plotting. The colours correspond to populations
specified in the order of 
shapevec 
Vector of shapes for the plotting points.
These are named shapes from the following list:
"Circle", "Square", "Diamond", "TriangleUp", "TriangleDown", "OpenSquare",
"OpenCircle", "OpenTriangleUp", "Plus", "Cross", "OpenDiamond",
"OpenTriangleDown", "Asterisk"
which correspond to the following pch values for R plots:
21, 22, 23, 24, 25, 0, 1, 2, 3, 4, 5, 6, 8.
Do not use the numbers, use the words, which will be automatically
converted within plot_logprob into the appropriate codes.
The elements of shapevec correspond to the populations specified in the
order of 
mark_impute 
(default FALSE) Boolean, indicates whether to mark individuals with missing data using asterisks. 
txt 
(default "points") Defines whether to plot individuals as points on
the GenePlot ( 
use_legend 
(default TRUE) Plot the legend (or FALSE for don't plot the legend). 
legend_pos 
(default "bottomleft") Define where to plot the legend,
uses the same position labels as in the 
xyrange 
(default NULL) Specify the xyrange as a vector, will be the same range for both axes. Default is slightly wider than the range of the calculated LogGenotypeProbabilities for all individuals in the plot. 
orderpop 
Specify the plotting order for the populations. E.g. if orderpop=c("Pop4", "Pop2"), then points for individuals from Pop4 will be plotted first, then individuals from Pop2 will be plotted over the top of them, etc. Default is NULL, in which case populations are plotted in order of size, so the population with the largest number of points is plotted at the bottom, and the population with the smallest number of individuals/points is plotted over the top, so as not to be obscured. 
axispop 
is used when 
axis_labels 
(default NULL) Used for plots with 2 reference pops.
Character vector, 2 elements, can be used to specify more readable axis
labels. Defaults to the 'pop' labels in 
short_axis_labels 
(default FALSE) Used for plots with 2 reference pops. FALSE (default) gives fulllength axis labels of the form "Log10 genotype probability for population Pop1" TRUE gives shortform axis labels of the form "LGP10 for population Pop1" 
grayscale_quantiles 
(default FALSE) Used for plots with 2 reference pops. FALSE (default) plots the quantile lines using colvec colours TRUE plots the quantile lines in gray (as the default colours can be quite pale, the grayscale quantile lines can be easier to see than the default coloured ones). 
dim1 
(default 1) Used for plots with more than 2 reference pops, when plot_bars=FALSE. Specifies which principal component should be plotted on xaxis. 
dim2 
(default 2) Used for plots with more than 2 reference pops, when plot_bars=FALSE. Specifies which principal component should be plotted on yaxis. 
layout_already_set 
(default=FALSE) Boolean, used for plots with more
than 2 reference pops, when plot_bars=TRUE.
Indicates whether the 
cexpts 
(default 1.4) Specify the size of the points in the plot. 
Default is NON leaveoneout but WE STRONGLY RECOMMEND USING LEAVEONEOUT, ESPECIALLY FOR SMALL SAMPLES (<30).
includepopnames
specifies which additional populations are plotted.
It defaults to NULL, in which case only refpopnames
will be plotted.
includepopnames
can specify the populations in any order.
If refpopnames
are missing from includepopnames
, they will be added.
NOTE that if a population is not in includepopnames
/refpopnames
,
then any alleles private to that population will NOT be included in the
prior / posterior. Thus the posterior for a given refpop will change slightly
depending on which populations are in includepopnames
/refpopnames
.
The structure of the output from calc_logprob
and/or geneplot
is a data frame, with one row per individual.
The first two columns are "id" and "pop", as in the input data.
The next column (col3) is "status" which is "complete" or "impute" depending on whether the individual had data for all loci, or had some loci missing.
The next column (col4) is "nloci" which is how many loci the individual has data for
The next columns are the final/imputed loggenotype probabilities for the individual with respect to each of the reference populations. They are named in the form "Pop1", "Pop2" etc. corresponding to the names in the refpopnames input.
Then the final columns are the "raw" loggenotype probabilities for the same pops. These are named in the form "Pop1.raw", Pop2.raw", etc. again corresponding to the names in refpopnames.
For individuals with full data at all loci, i.e. no missing data, these two sets of columns will be the same, and give the individual's loggenotype probabilities with respect to each of the reference populations.
For individuals with missing data at some loci then the raw values are the loggenotype probabilities calculated based on the loci that *are* present in the data, and the final/imputed columns, at the start of the results data frame, are the imputed loggenotype probabilities for the full set of loci i.e. the final LGPs for the missingdata individuals are comparable to the final LGPs for the completedata individuals.
— Additional attributes of the results object —————————
At the end of calc_logprob
the details of the algorithm used to calculate
the results are attached as attributes to the results object.
If your call to calc_logprob
or geneplot
is e.g.
Pop1_vs_Pop2_results < calc.logprob.func(dat, c("Pop1","Pop2"), locnames=whaleLocnames)
then you would find out the attributes using attributes(Pop1_vs_Pop2_results)$saddlepoint
etc.
Other attributes attached to the results object are:
attributes(results)$min.loci
– the minimum number of loci to require
for any individual to be assigned, so any individual with fewer loci
will be excluded from analysis
attributes(results)$n.too.few
– the number of individuals that have
been excluded from the analysis because they had too few loci
attributes(results)$percent.missing
– the percentage of individuals
that have been excluded, out of all those in the samples listed in
allpopnames
attributes(results)$qmat
– the values of the plotted quantiles
for the populations, with the % labels of the quantiles as the column names
e.g. if quantiles=c(0.05,0.99) was the input to chart.func then
qmat will be of the form
5%  99%  
Pop1  xx  xx 
Pop2  xx  xx 
attributes(results)$allele_freqs
– the posterior estimates of the allele
frequencies for the populations, as a list, where each element of the
list corresponds to one locus (and the list elements are named with
the loci names), and at a single locus the allele
frequencies are given as a matrix with the allele type names as the
columns and the reference populations as the rows
e.g. one locus example
$TR3G2
150  158  168  172  176  180  
Pop1  0.125  0.125  12.125  26.125  22.125  12.125 
Pop2  1.125  2.125  13.125  29.125  21.125  10.125 
These are allele COUNT estimates, NOT PROPORTION estimates, so they do not need to add up to 1.
attributes(results)$allpopnames
– a vector of refpopnames, followed by include.pops names
i.e. allpopnames < c(refpopnames, include.pops)
attributes(results)$refpopnames
– vector of reference population names
attributes(results)$include.pops
– vector of included pop names for assignment
attributes(results)$saddlepoint
– TRUE/FALSE for whether saddlepoint was used
attributes(results)$leave.one.out
– TRUE/FALSE for whether leave.one.out was used
attributes(results)$logten
– TRUE/FALSE for whether log_10 was used (TRUE) or
log_e was used (FALSE)
attributes(results)$prior
– "Rannala"/"Baudouin", for whether Rannala
and Mountain or Baudouin and Lebrun prior was used (see McMillan & Fewster, 2017 Biometrics)
LogGenotypeProbability calculations based on the method of Rannala and Mountain (1997) as implemented in GeneClass2, updated to allow for individuals with missing data and to enable accurate calculations of quantiles of the LogGenotypeProbability distributions of the reference populations. See McMillan and Fewster (2017) for details.
McMillan, L. and Fewster, R. "Visualizations for genetic assignment analyses using the saddlepoint approximation method" (2017) Biometrics. Rannala, B., and Mountain, J. L. (1997). Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences 94, 9197–9201. Piry, S., Alapetite, A., Cornuet, J.M., Paetkau, D., Baudouin, L., and Estoup, A. (2004). GENECLASS2: A software for genetic assignment and firstgeneration migrant detection. Journal of Heredity 95, 536–539.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94  ## Example dataset created directly within R (usually you would read in data from a file instead):
ratLocnames < c("D10Rat20","D11Mgh5","D15Rat77","D16Rat81","D18Rat96",
"D19Mit2","D20Rat46","D2Rat234","D5Rat83","D7Rat13")
ratData < rbind(
c("Ki001","Kai",96,128,246,280,234,250,155,165,226,232,219,231,149,149,101,127,174,176,164,182),
c("Ki002","Kai",122,126,246,276,238,238,155,165,226,232,223,231,187,187,107,121,174,174,164,164),
c("Ki003","Kai",122,122,276,280,234,234,157,165,244,244,231,231,187,187,107,107,174,174,164,182),
c("Ki004","Kai",130,130,276,280,238,238,157,165,0,0,223,231,187,187,101,111,168,176,184,184),
c("Ki009","Kai",122,122,276,276,234,236,165,165,240,244,229,231,187,187,89,101,174,176,164,164),
c("Ki010","Kai",122,122,278,280,236,236,155,165,236,244,219,231,185,187,101,101,168,174,164,164),
c("Ki011","Kai",120,128,280,282,236,238,155,165,226,236,223,231,149,149,99,101,174,174,164,164),
c("Bi01","Brok",96,126,280,280,236,250,165,165,232,246,231,231,185,187,89,89,170,176,154,164),
c("Bi02","Brok",96,126,280,280,250,262,155,155,232,232,231,233,149,185,127,127,174,174,164,166),
c("Bi03","Brok",96,126,280,280,258,262,165,165,232,232,231,231,185,187,89,127,174,174,164,164),
c("Bi04","Brok",96,126,280,280,238,262,155,155,232,232,231,233,149,185,127,127,174,174,164,164),
c("Bi05","Brok",96,122,280,280,250,258,155,155,226,244,231,231,187,187,107,127,174,176,164,164),
c("Bi06","Brok",96,96,280,280,238,262,155,155,232,232,231,231,187,187,123,127,174,174,164,164),
c("Bi11","Brok",96,96,278,280,234,250,165,165,226,240,231,231,149,187,89,99,170,170,154,164),
c("Bi12","Brok",96,96,276,280,234,250,165,165,240,240,231,231,187,187,89,99,170,174,154,164),
c("Bi13","Brok",96,126,276,276,246,250,165,165,226,244,231,231,149,187,99,99,174,174,164,164),
c("Bi14","Brok",96,126,276,276,262,262,155,165,226,244,231,231,149,187,89,107,170,174,154,164),
c("Ki092","Main",122,126,280,282,234,238,165,165,236,240,231,231,149,187,95,95,0,0,164,164),
c("Ki093","Main",122,126,282,282,238,238,165,165,236,240,231,231,149,187,95,107,166,174,164,182),
c("Ki094","Main",122,126,280,282,238,238,165,165,226,240,231,231,173,187,95,127,174,176,154,182),
c("Ki095","Main",120,126,280,280,234,236,155,165,244,246,231,231,161,187,123,127,174,174,154,154),
c("Ki097","Main",122,126,280,280,236,236,163,165,236,242,219,231,149,161,107,115,166,174,164,166),
c("Ki098","Main",96,122,276,280,236,238,155,165,242,244,233,233,149,187,99,107,174,174,164,164),
c("Ki100","Main",122,122,280,280,234,234,155,165,236,236,219,235,0,0,107,107,174,176,164,164),
c("Ki101","Main",122,126,276,280,234,238,155,155,236,244,229,231,0,0,101,101,0,0,164,182),
c("Ki102","Main",122,126,0,0,0,0,155,163,0,0,229,231,0,0,107,107,0,0,0,0),
c("Ki103","Main",122,122,280,280,234,236,163,165,0,0,231,233,0,0,99,107,0,0,164,184),
c("Ki104","Main",96,126,276,280,236,238,157,165,230,246,231,231,149,187,107,107,0,0,164,164),
c("Ki105","Main",122,126,276,280,238,250,157,165,226,244,217,231,0,0,111,121,174,174,164,164),
c("R01","Erad10",128,128,280,288,234,244,155,165,242,244,231,231,149,149,107,107,174,174,164,166),
c("R02","Erad10",128,130,276,288,238,244,155,155,228,244,223,231,149,149,101,111,174,174,164,166),
c("R03","Erad10",128,130,276,288,238,244,155,155,244,244,223,231,149,187,107,111,174,176,164,166))
ratData < as.data.frame(ratData, stringsAsFactors=FALSE)
names(ratData) < c("id","pop","D10Rat20.a1","D10Rat20.a2","D11Mgh5.a1","D11Mgh5.a2",
"D15Rat77.a1","D15Rat77.a2","D16Rat81.a1","D16Rat81.a2",
"D18Rat96.a1","D18Rat96.a2","D19Mit2.a1","D19Mit2.a2",
"D20Rat46.a1","D20Rat46.a2","D2Rat234.a1","D2Rat234.a2",
"D5Rat83.a1","D5Rat83.a2","D7Rat13.a1","D7Rat13.a2")
## Run GenePlot for 2 reference populations:
geneplot(dat=ratData,refpopnames=c("Kai","Main"),locnames=ratLocnames,
prior="Baudouin", leave_one_out=TRUE,
colvec=c("darkorchid4","steelblue"), shapevec=c("Circle","Square"),
axis_labels=c("Log10 genotype probability for Kaikoura Island",
"Log10 Genotype probability for Mainland"))
## Run GenePlot for 2 reference populations, and include an extra group of
## individuals who are going to be compared to the 2 reference populations to
## see which reference population they are most similar to (note that we
## specify an additional colour and shape for the new individuals, but the
## axis labels stay the same because they correspond to the two reference
## populations):
results < geneplot(dat=ratData,refpopnames=c("Kai","Main"),locnames=ratLocnames,
includepopnames=c("Erad10"), prior="Baudouin", leave_one_out=TRUE,
colvec=c("darkorchid4","steelblue","chartreuse4"),
shapevec=c("Circle","Square","TriangleUp"),
axis_labels=c("Log10 genotype probability for Kaikoura Island",
"Log10 Genotype probability for Mainland"))
## Barplot:
plot_logprob(results, plot_bars=TRUE,
colvec=c("darkorchid4","steelblue","chartreuse4"))
## Rename Kai and Main as a single population and compare that population to Brok:
ratData2 < ratData
ratData2$pop[which(ratData2$pop %in% c("Kai","Main"))] < "KaiMain"
geneplot(dat=ratData2,refpopnames=c("KaiMain","Brok"),locnames=ratLocnames,
prior="Rannala", leave_one_out=TRUE,
colvec=c("forestgreen","darkgoldenrod"),
shapevec=c("TriangleDown","Diamond"),
axis_labels=c("Log10 genotype probability for Kaikoura and Mainland",
"Log10 Genotype probability for Broken Islands"))
## Example code for reading in a Genepopformat file
## Typical code to run locally on your home machine:
## genepopDat < read_genepop_format("/home/data/genepop_format_example.gen",digits_per_allele=3)
## Working code to demonstrate the example, using a file in the package:
genepopDat < read_genepop_format(system.file("extdata",
"genepop_format_example.gen", package = "geneplot"),digits_per_allele=3)
## Extract the loci names (that were read in from the top of the file):
locnames < genepopDat$locnames
## Separate out the data:
dat < genepopDat$pop_data
## You could then run GenePlot on the data and locnames.
## Note that by default, data read in from Genepop format will have populations
## called Pop1, Pop2 etc. unless the individuals in that pop have nonunique
## names, in which case they will be given the ID of the first individual
## in that pop as their pop name and will be given autogenerated unique IDs.
geneplot(dat, refpopnames=c("Mahu","Taik"), includepopnames=c("Flat"), locnames=genepopDat$locnames)

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