win.ia: Calculate windows of the index of association for genlight objects.

Description

Genlight objects can contain millions of loci. Since it does not make much sense to calculate the index of association over that many loci, this function will scan windows across the loci positions and calculate the index of association.

Usage

win.ia(
  x,
  window = 100L,
  min.snps = 3L,
  threads = 1L,
  quiet = FALSE,
  name_window = TRUE,
  chromosome_buffer = TRUE
)

Value

A value of the standardized index of association for all windows in each chromosome.

Arguments

x: a genlight or snpclone object.
window: an integer specifying the size of the window.
min.snps: an integer specifying the minimum number of snps allowed per window. If a window does not meet this criteria, the value will return as NA.
threads: The maximum number of parallel threads to be used within this function. Defaults to 1 thread, in which the function will run serially. A value of 0 will attempt to use as many threads as there are available cores/CPUs. In most cases this is ideal for speed. Note: this option is passed to bitwise.ia() and does not parallelize the windowization process.
quiet: if FALSE (default), a progress bar will be printed to the screen.
name_window: if TRUE (default), the result vector will be named with the terminal position of the window. In the case where several chromosomes are represented, the position will be appended using a period/full stop.
chromosome_buffer: DEPRECATED if TRUE (default), buffers will be placed between adjacent chromosomal positions to prevent windows from spanning two chromosomes.

Author

Zhian N. Kamvar, Jonah C. Brooks

Examples

Run this code


# with structured snps assuming 1e4 positions
set.seed(999)
x <- glSim(n.ind = 10, n.snp.nonstruc = 5e2, n.snp.struc = 5e2, ploidy = 2)
position(x) <- sort(sample(1e4, 1e3))
res <- win.ia(x, window = 300L) # Calculate for windows of size 300
plot(res, type = "l")

if (FALSE) {

# unstructured snps
set.seed(999)
x <- glSim(n.ind = 10, n.snp.nonstruc = 1e3, ploidy = 2)
position(x) <- sort(sample(1e4, 1e3))
res <- win.ia(x, window = 300L) # Calculate for windows of size 300
plot(res, type = "l")

# Accounting for chromosome coordinates
set.seed(999)
x <- glSim(n.ind = 10, n.snp.nonstruc = 5e2, n.snp.struc = 5e2, ploidy = 2)
position(x) <- as.vector(vapply(1:10, function(x) sort(sample(1e3, 100)), integer(100)))
chromosome(x) <- rep(1:10, each = 100)
res <- win.ia(x, window = 100L)
plot(res, type = "l")

# Converting chromosomal coordinates to tidy data
library("dplyr")
library("tidyr")
res_tidy <- res %>% 
  tibble(rd = ., chromosome = names(.)) %>% # create two column data frame
  separate(chromosome, into = c("chromosome", "position")) %>% # get the position info
  mutate(position = as.integer(position)) %>% # force position as integers
  mutate(chromosome = factor(chromosome, unique(chromosome))) # force order chromosomes
res_tidy

# Plotting with ggplot2
library("ggplot2")
ggplot(res_tidy, aes(x = position, y = rd, color = chromosome)) +
  geom_line() +
  facet_wrap(~chromosome, nrow = 1) +
  ylab(expression(bar(r)[d])) +
  xlab("terminal position of sliding window") +
  labs(caption = "window size: 100bp") + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  theme(legend.position = "top")

}