neutrality.stats-methods: Neutrality Statistics
In PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses

Description Usage Arguments Value References Examples

This generic function calculates some neutrality statistics.

## S4 method for signature 'GENOME'
neutrality.stats(object,new.populations=FALSE,new.outgroup=FALSE,
subsites=FALSE,detail=FALSE, FAST=FALSE, do.R2=FALSE)
## S4 method for signature 'GENOME'
get.neutrality(object,theta=FALSE,stats=TRUE)

`object`	an object of class `"GENOME"`
`new.populations`	list of populations. default:`FALSE`
`new.outgroup`	vector of outgroup sequences. default:`FALSE`
`subsites`	`"transitions"`: SNPs that are transitions. `"transversions"`: SNPs that are transversions. `"syn"`: synonymous sites. `"nonsyn"`: non-synonymous 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. default:`FALSE`
`detail`	default:`FALSE`, `TRUE` for some detailed statistics. Note: slows down calculations!
`FAST`	Fast computation. only works if there is no outgroup defined.
`do.R2`	Ramos-Onsins' & Rozas' R2
`stats`	show the results of each statistic. default:`TRUE`
`theta`	show the theta values. default:`FALSE`

The return value is a modified object of class "GENOME"

———————————————————
The following slots will be modified in the "GENOME" object
———————————————————

	Slot	Reference	Description
1.	`n.segregating.sites`		Total number of segregating sites
2.	`Tajima.D`	[1]	Tajima's' D statistic 1989
3.	`Fu.Li.F`	[3]	Fu & Li's' F* statistic 1993
4.	`Fu.Li.D`	[3]	Fu & Li's D* statistic 1993
5.	`Fay.Wu.H`	[6]	Fay & Wu's H statistic 2000
6.	`Zeng.E`	[7]	Zeng's E statistic 2006
7.	`Strobeck.S`	[5]	Strobeck's S statistic 1987 (if detail==`TRUE`)
8.	`Fu.F_S`	[4]	Fu's F$_S$ statistic 1997 (if detail==`TRUE`)
9.	`Rozas.R_2`	[2]	Ramos-Onsins' & Rozas' $R_2$ statistic 2002
10.	`theta_Tajima`	[1]
11.	`theta_Watterson`
12.	`theta_Fu.Li`	[3]
13.	`theta_Achaz.Watterson`
14.	`theta_Achaz.Tajima`
15.	`theta_Fay.Wu`	[6]
16.	`theta_Zeng`	[7]

[1] Tajima, F.(1989) Statistical Method for Testing the Neutral Mutation Hypothesis by DNA Polymorphism. Genetics, 123(3): 585-595.

[2] Ramos-Onsins, S.E. and J.Rozas (2002). Statistical Properties of New Neutrality Tests Against Population Growth. Mol.Biol.Evol.19(12),2092-2100

[3] Fu, Y.X. and W.H.Li (1993). Statistical Tests of Neutrality of Mutations. Genetics 133(3),693-709

[4] Fu, Y.-X.(1997). Statistical Tests of Neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147(2),915-925.

[5] Strobeck, C. (1987). Average number of nucleotide differences in a sample from a single subpopulation: a test for population subdivision. Genetics 117, 149-153

[6] Fay, J.C. and C.-I. Wu (2000). Hitchhiking under positive Darwinian selection. Genetics 155 (3),1405-1413

[7] Zeng, K., Y.-X. Fu, S. Shi, and C.-I. Wu (2006). Statistical tests for detecting positive selection by utilizing high-frequency variants. Genetics 174, 1431-1439

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