poppr (version 2.6.1)

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,
  chromosome_buffer = TRUE)

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. A value of 0 (default) will attempt to use as many threads as there are available cores/CPUs. In most cases this is ideal. A value of 1 will force the function to run serially, which may increase stability on some systems. Other values may be specified, but should be used with caution.

quiet

if FALSE, a progress bar will be printed to the screen.

chromosome_buffer

if TRUE (default), buffers will be placed between adjacent chromosomal positions to prevent windows from spanning two chromosomes.

Value

Index of association representing the samples in this genlight object.

See Also

genlight, snpclone, samp.ia, ia, bitwise.dist

Examples

# NOT RUN {
# 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")

# }
# NOT RUN {
# 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")
res_tidy <- res %>% 
  data_frame(rd = ., chromosome = names(.)) %>% # create two column data frame
  filter(chromosome != "") %>%                  # filter out null chromosomes
  group_by(chromosome) %>%                      # group data by chromosome
  mutate(window = row_number()) %>%             # windows by chromosome
  ungroup(chromosome) %>%                       # ungroup and reorder
  mutate(chromosome = factor(chromosome, unique(chromosome))) 
res_tidy

# Plotting with ggplot2
library("ggplot2")
ggplot(res_tidy, aes(x = window, y = rd, color = chromosome)) +
  geom_line() +
  facet_wrap(~chromosome, nrow = 1) +
  ylab(expression(bar(r)[d])) +
  xlab("window (100bp)") +
  theme(legend.position = "bottom")

# }
# NOT RUN {
# }