F_ST.stats-methods: Fixation Index
In PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses

Description Usage Arguments Details Value References See Also Examples

A generic function to calculate some F-statistics and nucleotide/haplotype diversities.

## S4 method for signature 'GENOME'
F_ST.stats(
object,
new.populations=FALSE,
subsites=FALSE,
detail=TRUE,
mode="ALL",
only.haplotype.counts=FALSE,
FAST=FALSE
)

## S4 method for signature 'GENOME'
get.diversity(object,between=FALSE)
## S4 method for signature 'GENOME'
get.F_ST(object,mode=FALSE,pairwise=FALSE)

`object`	An object of class `"GENOME"`
`new.populations`	list of populations. default:`FALSE`
`subsites`	`"transitions"`: SNPs that are transitions. `"transversions"`: SNPs that are transversions. `"syn"`: synonymous sites. `"nonsyn"`: nonsynonymous sites. `"exon"`: SNPs in exon regions. `"intron"`: SNPs in intron regions. `"coding"`: SNPs in coding regions (CDS). `"utr"`: SNPs in UTR regions. `"gene"`: SNPs in genes. `"intergenic"` : SNPs in intergenic regions.
`detail`	detail statistics. Note: slower!
`between`	`TRUE`: show between-diversities. `FALSE`: show within-diversities
`mode`	mode=`"haplotype"` or mode=`"nucleotide"`
`only.haplotype.counts`	only calculate the haplotype counts
`FAST`	if `TRUE` only calculate a subset of statistics. see details!
`pairwise`	show paiwise comparisons. default:`FALSE`

If FAST is switched on, this module only calculates nuc.diversity.within, hap.diversity.within, haplotype.F_ST, nucleotide.F_ST and pi.
Note:
1) The nucleotide diversities have to be devided by the size of region considered (e.g. GENOME@n.sites) to give diversities per site.
2) When missing or unknown nucleotides are included (include.unknown=TRUE) those sites are completely deleted in case of haplotype based statistics.
3) The function detail.stats(...,site.FST=TRUE) will compute SNP specific FST values which are then stored in the slot GENOME.class@region.stats@site.FST.
4) We recommend to use mode="nucleotide" in case you have many unknowns included in your dataset.

	Slot	Reference	Description
1.	`haplotype.F_ST`	[1]	Fixation Index based on haplotype frequencies
2.	`nucleotide.F_ST`	[1]	Fixation Index based on minor.allele frequencies
3.	`Nei.G_ST`	[2]	Nei's Fixation Index
4.	`Hudson.G_ST`	[3]	see reference ...
5.	`Hudson.H_ST`	[3]	see reference ...
6.	`Hudson.K_ST`	[3]	see reference ...
7.	`nuc.diversity.within`	[1,5]	Nucleotide diversity (within the population)
8.	`hap.diversity.within`	[1]	Haplotype diversity (within the population)
9.	`Pi`	[4]	Nei's diversity (within the population)
10.	`hap.F_ST.vs.all`	[1]	Fixation Index for each population against all other individuals (haplotype)
11.	`nuc.F_ST.vs.all`	[1]	Fixation Index for each population against tall other individuals (nucleotide)
12.	`hap.diversity.between`	[1]	Haplotype diversities between populations
13.	`nuc.diversity.between`	[1,5]	Nucleotide diversities between populations
14.	`nuc.F_ST.pairwise`	[1]	Fixation Index for every pair of populations (nucleotide)
15.	`hap.F_ST.pairwise`	[1]	Fixation Index for every pair of populations (haplotype)
16.	`Nei.G_ST.pairwise`	[2]	Fixation Index for every pair of populations (Nei)
17.	`region.stats`		an object of class "region.stats" for detailed statistics

[1] Hudson, R. R., M. Slatkin, and W.P. Maddison (1992). Estimating levels of gene flow from DNA sequence data. Gentics 13(2),583-589

[2] Nei, M. (1973). Analysis of gene diversity in subdivided populations. Proc.Natl. Acad. Sci. USA 70: 3321-3323

[3] Hudson, R. R., Boos, D.D. and N. L. Kaplan (1992). A statistical test for detecting population subdivison. Mol. Biol. Evol. 9: 138-151.

[4] Nei, M. (1987). Molecular Evolutionary Genetics. Columbia Univ. Press, New York.

[5] Wakeley, J. (1996).The Variance of Pairwise Nucleotide Differences in Two Populations with Migration. THEORETICAL POPULATION BIOLOGY. 49, 39-57.

# methods?F_ST.stats.2 #F_ST.stats.2

# GENOME.class <- readData("\home\Alignments")
# GENOME.class
# GENOME.class <- F_ST.stats(GENOME.class)
# GENOME.class <- F_ST.stats(GENOME.class,list(1:4,5:10),subsites="syn")
# GENOME.class <- F_ST.stats(GENOME.class,list(c("seq1","seq5","seq3"),c("seq2","seq8")))
# show the result:
# get.F_ST(GENOME.class)
# get.F_ST(GENOME.class, pairwise=TRUE)
# get.diversity(GENOME.class, between=TRUE)
# GENOME.class@Pi --> population specific view
# GENOME.class@region.stats