A function that takes paths to an eigenstrat trio (ind, snp and geno file) and returns the pairwise mismatch rate for all pairs on a thinned set of SNPs. Options include choosing thinning parameter, subsetting by population names, and filtering out SNPs for which deamination is possible.
Usage
processEigenstrat(
indfile,
genofile,
snpfile,
filter_length = NULL,
pop_pattern = NULL,
filter_deam = FALSE,
outfile = NULL,
chromosomes = NULL,
verbose = TRUE,
byPop = FALSE,
minMerge = 2
)Arguments
- indfile
path to eigenstrat ind file
- genofile
path to eigenstrat geno file.
- snpfile
path to eigenstrat snp file.
- filter_length
the minimum distance between sites to be compared (to reduce the effect of LD).
- pop_pattern
a character vector of population names to filter the ind file if only some populations are to compared.
- filter_deam
a TRUE/FALSE for if C->T and G->A sites should be ignored.
- outfile
(OPTIONAL) a path and filename to which we can save the output of the function as a TSV, if NULL, no back up saved. If no outfile, then a tibble is returned.
- chromosomes
the chromosome to filter the data on.
- verbose
controls printing of messages to console
- byPop
Boolean variable identifying if all pairs of individuals should be compared (FALSE), or just pairs of individuals in the same populations (TRUE)
- minMerge
(integer) the minimum group size (smaller groups are merged).
Examples
# Use internal files to the package as an example
indfile <- system.file("extdata", "example.ind.txt", package="BREADR")
genofile <- system.file("extdata", "example.geno.txt", package="BREADR")
snpfile <- system.file("extdata", "example.snp.txt", package="BREADR")
processEigenstrat(
indfile, genofile, snpfile,
filter_length=1e5,
pop_pattern=NULL,
filter_deam=FALSE
)
#> Reading in SNP data.
#> Analysing chromosomes:
#> 1
#> Starting to read in genotype data.
#>
|
| | 0%
|
|============== | 20%
|
|============================ | 40%
|
|========================================== | 60%
|
|======================================================== | 80%
|
|======================================================================| 100% Complete.
#>
#> Starting to compare genotypes and calculate PMR.
#>
|
|===== | 7%
|
|========= | 13%
|
|============== | 20%
|
|=================== | 27%
|
|======================= | 33%
|
|============================ | 40%
|
|================================= | 47%
|
|===================================== | 53%
|
|========================================== | 60%
|
|=============================================== | 67%
|
|=================================================== | 73%
|
|======================================================== | 80%
|
|============================================================= | 87%
|
|================================================================= | 93%
|
|======================================================================| 100%
#> Complete.
#>
#> # A tibble: 15 × 4
#> pair nsnps mismatch pmr
#> <chr> <dbl> <dbl> <dbl>
#> 1 Ind1 - Ind2 2 1 0.5
#> 2 Ind1 - Ind3 14 2 0.143
#> 3 Ind1 - Ind4 14 3 0.214
#> 4 Ind1 - Ind5 1 0 0
#> 5 Ind1 - Ind6 4 0 0
#> 6 Ind2 - Ind3 13 3 0.231
#> 7 Ind2 - Ind4 11 2 0.182
#> 8 Ind2 - Ind5 0 0 NA
#> 9 Ind2 - Ind6 4 2 0.5
#> 10 Ind3 - Ind4 35 5 0.143
#> 11 Ind3 - Ind5 10 0 0
#> 12 Ind3 - Ind6 19 2 0.105
#> 13 Ind4 - Ind5 10 1 0.1
#> 14 Ind4 - Ind6 20 7 0.35
#> 15 Ind5 - Ind6 2 1 0.5