introgression.stats-methods | R Documentation |
A generic function to estimate archaic admixture.
## S4 method for signature 'GENOME' introgression.stats(object, subsites=FALSE, do.D=TRUE, do.df=TRUE, keep.site.info=TRUE, block.size=FALSE, do.RNDmin=FALSE, l.smooth=FALSE)
object |
An object of class |
subsites |
|
do.D |
Pattersons D and Martin's f statistic |
do.df |
d-fraction |
keep.site.info |
keep site specific values ( |
block.size |
the block size for jackknife |
do.RNDmin |
RNDmin (Rosenzweig, 2016) |
l.smooth |
laplace smoothing for Bd-fraction |
To perform the D and f statistic one needs to define 3 populations via the function set.populations
,
where the third population represent the archaic population. In addition, an outgroup is required and have to be
set via the function set.outgroup
. Here, only SNPs where the outgroup is monomorphic are considered.
f is the fraction of the genome that is admixed [2].
Returned value is a modified object of class "GENOME"
———————————————————
The following slots will be modified in the "GENOME"
object
———————————————————
Slot | Reference | Description | |
1. | D | [1;eq. 2] | Pattersons D statistic |
2. | f | [2] | f statistic |
3. | df | [3] | Bd-fraction |
4. | RNDmin | [x] | RNDmin |
5. | D.z | [x] | z values (jackknife) |
6. | D.pval | [x] | P values (jackknife) |
7. | df.z | [3] | z values (jackknife) |
8. | df.pval | [3] | P values (jackknife) |
[1] Durand, E. Y., Patterson, N. J., Reich, D., & Slatkin, M. (2011). Testing for ancient admixture between closely related populations.
Molecular Biology and Evolution, 28(8), 2239–2252. doi:10.1093/molbev/msr048
[2] Simon H Martin, Kanchon K Dasmahapatra, Nicola J Nadeau, et al. (2013). Genome-wide evidence for speciation with gene flow in Heliconius butterflies.
Genome Res. doi:10.1101/gr.159426.113
[3] Bastian Pfeifer and Durrell D. Kapan (2019). Estimates of introgression as a function of pairwise distances.
BMC Bioinformatics. https://doi.org/10.1186/s12859-019-2747-z
# GENOME.class <- readData("\home\Alignments") # GENOME.class <- set.populations(GENOME.class,list(1:3,4:8,9:12)) # GENOME.class <- set.outgroup(GENOME.class,13) # GENOME.class <- introgression.stats(GENOME.class, do.D=TRUE) # show the result: # GENOME.class@D # GENOME.class@f # GENOME.class <- introgression.stats(GENOME.class, do.df=TRUE) # show the result: # GENOME.class@df
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