PopGenReport (version 2.2.2)

popgenreport: This is the main function of the package. It analyses an object of class genind and then creates a report containing the results of the analysis. There are several routines that can be optionally included in the analysis and there are multip output options including a PDF with the report, R-code and an object (fname.results) containing all of the results, which can be used for further analyses.

Description

This function is used to analyse population genetic data. The main idea is to provide a framework for analysing microsatellite genetic data using a mix of existing and new functions. The function works on an object of class genind. There are several ways to convert data into a genind object using existing functions provided by the adegenet package ( import2genind, df2genind,read.fstat, read.structure, read.genetix ,read.genepop) or refer to read.genetable how to import data from an EXCEL (csv) document. The function performs a number of different genetic analyses (e.g. counts of indivuals and alleles across sub-populations, tests for heterozygosity and Hardy-Weinberg Equilibrium, differentiation statistics Fst, G'st, Jost's D, and genetic distance between individuals and populations), with users having the option to select which analysis routines are included in the report. To select a routine, the user simply turns on a switch e.g. mk.map=TRUE returns a map with the sampling location for each individual (if coordinates are provided). Coordinates need to specified within the genind object. As a standard genind object does not require spatial coordinates, we extended it by using the other slot in the genind object. The easiest way to provide spatial coordinates is to use the read.genetable function and use the lat, long or x, y arguments for WGS1984 projected data or mercator projected data respectively. To calculate distances the data are internally reprojected using the Mercator function in package dismo), which is the projection used by google maps. Or you can add data manually to your genind object using the mentioned (e.g. genindobject@other$latlong <- yourlatlong data or genindobject@other$xy <- your_xy_data). If you have your data in a different projection you need to reproject them into either WGS1984 or the google maps Mercator projection. If you use a different projection distance calculation may be wrong and probably the map will not be correct. See the manual for an example how to project and add spatial coordinates to your genetic data. Names for alleles ([e-mail]) are truncated if longer than six characters. If truncated Captial letters linked by a hyphen are added to guarentee they are unique. You can rename them by providing new names by accessing the [e-mail] slot prior to running popgenreport. Note that the popgenreport function can take a long time to run if the options mk.complete, mk.gd.kosman, or mk.gd.smouse are set to TRUE. For example, running popgenreport with mk.complete=TRUE on a dataset with 500 individuals with 36 loci will take 14 to 15 minutes on a PC with a 3.5 Ghz processor and nearly 3 hours for a dataset with ~3200 individuals.

Usage

popgenreport(cats = NULL, mk.counts = TRUE, mk.map=FALSE, maptype="satellite", 
mapdotcolor ="blue", mapdotsize=1, mapdotalpha=0.4, mapdottype=19 , mapzoom=NULL,
mk.locihz = FALSE, mk.hwe = FALSE, mk.fst = FALSE, mk.gd.smouse = FALSE, 
mk.gd.kosman = FALSE, mk.pcoa=FALSE, mk.spautocor=FALSE, mk.allele.dist = FALSE,
mk.null.all=FALSE, mk.allel.rich=FALSE,mk.differ.stats=FALSE, mk.custom=FALSE,
fname = "PopGenReport", foldername="results", path.pgr=NULL, mk.Rcode = FALSE, 
mk.complete=FALSE, mk.pdf=TRUE)

Value

The function returns an object (e.g. res) that has all of the results produced by this function in it. The structure of the object can be accessed via str(res). The main slots in this object (if you ran a full report) are: dataoverview, PopHet, Alleledist, Fst, HsHtdifferentiate, HWEresults, subgroups, GDKosman, GDSmouse Additional ouput is provided in the form of a PDF (if mk.pdf=TRUE),which will be saved to the specified subfolder (via foldername) in your current working directory, and maps and figures which will be placed in this folder as well. This folder will be generated automatically in your current working directory. If you do not specify a working directory via path.pgr then the temporary working directory of R will be used (tempdir()). If mk.Rcode=T is set, an R file named fname.R will be saved to your specified subfolder.

References

Kosman E., Leonard K.J. 2005. Similarity coefficients for molecular markers in studies of genetic relationships between individuals for haploid, diploid, and polyploidy species. Molecular Ecology 14:415-424 Peakall R., Smouse P. 2012. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research - an update. Bioinformatics 28:2537-2539

See Also

adegenet, pegas, mmod

Examples

#not run:
#data(bilby) # a generated data set
#res <- popgenreport(bilby, mk.counts=TRUE, mk.map=TRUE, mk.pdf=FALSE)
#check results via res or use created tables in the results folder.

### RUN ONLY with a working Latex version installed
# res <- popgenreport(bilby, mk.counts=TRUE, mk.map=TRUE, mk.pdf=TRUE, path.pgr="c:/temp")
# for a full report in a single pdf set mk.complete to TRUE
# res <- popgenreport(bilby, mk.complete=TRUE)