| 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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