sweeps.stats-methods: Selective Sweeps
In PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses

Description Usage Arguments Details Value References Examples

This module calculates some statistics to detect selective sweeps.

## S4 method for signature 'GENOME'
sweeps.stats(object,new.populations=FALSE,subsites=FALSE,
 freq.table=FALSE, FST=FALSE)
## S4 method for signature 'GENOME'
get.sweeps(object)

`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"`: 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`
`freq.table`	the frequency counts for the CLR test. `"list"`
`FST`	use FST values instead of the minor allele frequencies

The freq.table contains the global sets of frequency counts. It can be produced with the module detail.stats. The values in the slot GENOME.class@region.stats@minor.allele.frequencies can be used to create this global set. (use the R function table) freq.table is a list of length n.pops.

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

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The following slots will be modified in the "GENOME" object
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`CL`	Composite Likelihood of SNPs
`CLR`	Nielsen's CLR test

Cai JJ (2008) PGEToolbox: A Matlab toolbox for population genetics and evolution Journal of Heredity Jul-Aug;99(4):438-40.doi:10.1093/jhered/esm127

Nielson, R. (2005). Genomic scans for selective sweeps using SNP data Genome Res. 2005 15: 1566-1575

# Reading one alignment stored in the folder Aln
# GENOME.class <- readData("\home\Aln")
#
# CL
# GENOME.class <- sweeps.stats(GENOME.class)
# GENOME.class@CL
#
# CLR
# create global set
# GENOME.class  <- detail.stats(GENOME.class)
# freq <- GENOME.class@region.stats@minor.allele.freqs[[1]]
# freq.table <- list()
# freq.table[[1]] <- table(freq)
# define the region of interest
# GENOME.class.split <- splitting.data(GENOME.class, positions= ...)
# calculate CLR
# GENOME.class.split <- sweeps.stats(GENOME.class.split, freq.table=freq.table)
# GENOME.class@CLR