OriGen (version 1.4.3)

FitOriGenModelFindUnknowns: Fit the OriGen model and place unknown individuals

Description

This function fits the OriGen model and places individuals of unknown origins.

Usage

FitOriGenModelFindUnknowns(DataArray,SampleCoordinates,
	UnknownData,MaxGridLength=20,RhoParameter=10)

Arguments

DataArray

An array giving the number of major/minor SNPs (defined as the most occuring in the dataset) grouped by sample sites for each SNP. The dimension of this array is [2,SampleSites,NumberSNPs].

SampleCoordinates

This is an array which gives the longitude and latitude of each of the found sample sites. The dimension of this array is [SampleSites,2], where the second dimension represents longitude and latitude respectively.

UnknownData

An array showing the unknown individuals genetic data. The dimension of this array is [NumberUnknowns,NumberSNPs].

MaxGridLength

An integer giving the maximum number of boxes to fill the longer side of the region. Note that computation time increases quadratically as this number increases, but this number also should be high enough to separate different sample sites otherwise they

RhoParameter

This is a real precision parameter weighting the amount of smoothing. A higher value flattens out the surface while a lower value allows for more fluctuations. The default value of 10 was used in our analysis and should prove a good starting point. To

Value

• List with the following components:
• UnknownGrids
  An array giving the probability that an unknown individual comes from the given location. The dimension of this array is [NumberLongitudeDivisions, NumberLatitudeDivisions, NumberUnknowns], where either NumberLongitudeDivisions or NumberLatitudeDivisions is equal to MaxGridLength(an input to this function) and the other is scaled so that the geodesic distance between points horizontally and vertically is equal.
• DataArray
  An array giving the number of major/minor SNPs (defined as the most occuring in the dataset) grouped by sample sites for each SNP. The dimension of this array is [2,SampleSites,NumberSNPs].
• NumberSNPs
  This shows the integer number of SNPs found.
• GridLength
  An array giving the number of longitudinal and latitudinal divisions. The dimension of this array is [2], where the first number is longitude and the second is latitude.
• RhoParameter
  A real value showing the inputted RhoParameter value.
• SampleSites
  This shows the integer number of sample sites found.
• MaxGridLength
  An integer giving the maximum number of boxes to fill the longer side of the region. Note that computation time increases quadratically as this number increases, but this number also should be high enough to separate different sample sites otherwise they will be binned together as a single site. This number was part of the inputs.
• SampleCoordinates
  This is an array which gives the longitude and latitude of each of the found sample sites. The dimension of this array is [SampleSites,2], where the second dimension represents longitude and latitude respectively.
• NumberUnknowns
  This is an integer value showing the number of unknowns found in the UnknownPEDFile.
• UnknownData
  An array showing the unknown individuals genetic data. The dimension of this array is [NumberUnknowns,NumberSNPs].
• GridCoordinates
  An array showing the corresponding coordinates for each longitude and latitude division. The dimension of this array is [2,MaxGridLength], with longitude coordinates coming first and latitude second. Note that one of these rows may not be filled entirely. The associated output GridLength should be used to find the lengths of the two rows. Rows not filled in entirely will contain zeroes at the end.

References

Ranola J, Novembre J, Lange K (2014) Fast Spatial Ancestry via Flexible Allele Frequency Surfaces. Bioinformatics, in press.

See Also

ConvertUnknownPEDData for converting two Plink PED files (known and unknown)into a format appropriate for analysis,

FitOriGenModelFindUnknowns for fitting allele surfaces to the converted data and finding the locations of the given unknown individuals,

PlotUnknownHeatMap for a quick way to plot the resulting unknown heat map surfaces from FitOriGenModelFindUnknowns,;

FitAdmixedModelFindUnknowns for fitting allele surfaces to the converted data and finding the locations of the given unknown individuals who may be admixed,

PlotAdmixedSurface for a quick way to plot the resulting admixture surfaces from FitAdmixedFindUnknowns,

Examples

#this example not run because it takes slightly longer than 5 secs
#note - type example(FunctionName, run.dontrun=TRUE) to run the example where FunctionName is
#the name of the function

#Data generation
SampleSites=10
NumberSNPs=5
TestData=array(sample(2*(1:30),2*SampleSites*NumberSNPs,
	replace=TRUE),dim=c(2,SampleSites,NumberSNPs))
#Europe is about -9 to 38 and 34 to 60
TestCoordinates=array(0,dim=c(SampleSites,2))
TestCoordinates[,1]=runif(SampleSites,-9,38)
TestCoordinates[,2]=runif(SampleSites,34,60)

#This code simulates the number of major alleles the unknown individuals have.
NumberUnknowns=2
TestUnknowns=array(sample(0:2,NumberUnknowns*NumberSNPs,
	replace=TRUE),dim=c(NumberUnknowns,NumberSNPs))

#Fitting the model
#MaxGridLength is the maximum number of boxes allowed to span the region in either direction
#RhoParameter is a tuning constant
trials4=FitOriGenModelFindUnknowns(TestData,TestCoordinates,
	TestUnknowns,MaxGridLength=20,RhoParameter=10)
str(trials4)

#Plotting the unknown heat map
PlotUnknownHeatMap(trials4,UnknownNumber=1,MaskWater=TRUE)