Remove potential bias caused by cloned genotypes in genind or genclone object.

This function removes any duplicated multilocus genotypes from any specified population strata.

Usage

clonecorrect(pop, strata = 1, combine = FALSE, keep = 1)

Arguments

pop: a genind, genclone, or snpclone object
strata: a hierarchical formula or numeric vector. This will define the columns of the data frame in the strata slot to use.
combine: logical. When set to TRUE, the strata will be combined to create a new population for the clone-corrected genind or genclone object.
keep: integer. When combine is set to FALSE, you can use this flag to choose the levels of your population strata. For example: if your clone correction strata is set to "Pop", "Subpop", and "Year", and you want to analyze your populations with respect to year, you can set keep = c(1,3).

Value

a clone corrected genclone,

snpclone, or genind object.

Details

This function will clone correct based on the stratification provided. To clone correct indiscriminately of population structure, set strata = NA.

Author

Zhian N. Kamvar

Examples

# LOAD A. euteiches data set
data(Aeut)

# Redefine it as a genclone object
Aeut <- as.genclone(Aeut)
strata(Aeut) <- other(Aeut)$population_hierarchy[-1]

# Check the number of multilocus genotypes
mlg(Aeut)
#> #############################
#> # Number of Individuals:  187 
#> # Number of MLG:  119 
#> #############################
#> [1] 119
popNames(Aeut)
#> [1] "Athena"     "Mt. Vernon"

# Clone correct at the population level.
Aeut.pop <- clonecorrect(Aeut, strata =  ~Pop)
mlg(Aeut.pop)
#> #############################
#> # Number of Individuals:  120 
#> # Number of MLG:  119 
#> #############################
#> [1] 119
popNames(Aeut.pop)
#> [1] "Athena"     "Mt. Vernon"

# \dontrun{
# Clone correct at the subpopulation level with respect to population and
# combine.
Aeut.subpop <- clonecorrect(Aeut, strata = ~Pop/Subpop, combine=TRUE)
mlg(Aeut.subpop)
#> #############################
#> # Number of Individuals:  141 
#> # Number of MLG:  119 
#> #############################
#> [1] 119
popNames(Aeut.subpop)
#>  [1] "Athena_1"     "Athena_2"     "Athena_3"     "Athena_4"     "Athena_5"    
#>  [6] "Athena_6"     "Athena_7"     "Athena_8"     "Athena_9"     "Athena_10"   
#> [11] "Mt. Vernon_1" "Mt. Vernon_2" "Mt. Vernon_3" "Mt. Vernon_4" "Mt. Vernon_5"
#> [16] "Mt. Vernon_6" "Mt. Vernon_7" "Mt. Vernon_8"

# Do the same, but set to the population level.
Aeut.subpop2 <- clonecorrect(Aeut, strata = ~Pop/Subpop, keep=1)
mlg(Aeut.subpop2)
#> #############################
#> # Number of Individuals:  141 
#> # Number of MLG:  119 
#> #############################
#> [1] 119
popNames(Aeut.subpop2)
#> [1] "Athena"     "Mt. Vernon"

# LOAD H3N2 dataset
data(H3N2)

strata(H3N2) <- other(H3N2)$x

# Extract only the individuals located in China
country <- clonecorrect(H3N2, strata =  ~country)

# How many isolates did we have from China before clone correction?
sum(strata(H3N2, ~country) == "China") # 155
#> [1] 155

# How many unique isolates from China after clone correction?
sum(strata(country, ~country) == "China") # 79
#> [1] 79

# Something a little more complicated. (This could take a few minutes on
# slower computers)

# setting the hierarchy to be Country > Year > Month  
c.y.m <- clonecorrect(H3N2, strata =  ~year/month/country)

# How many isolates in the original data set?
nInd(H3N2) # 1903
#> [1] 1903

# How many after we clone corrected for country, year, and month?
nInd(c.y.m) # 1190
#> [1] 1190
# }