coala primiary focuses on simulation of data, it also offers to calculate summary statsitcs from real data. This is particularly useful when comparing the statistics of real and simulated data.
Rather than offering functions to import data directly, coala can convert the internal formats of other R packages into its own format. Currently, the
PopGenome package is supported, but we plan to support
pegas in the future.
PopGenome provides functions for reading various data formats, including
fasta. Please refer to its documentation for detailed instructions. As an example, we will read sequence data from a short fasta file that is included in coala:
suppressPackageStartupMessages(library(PopGenome)) fasta <- system.file("example_fasta_files", package = "coala") data_pg <- readData(fasta, progress_bar_switch = FALSE) data_pg <- set.outgroup(data_pg, c("Individual_Out-1", "Individual_Out-2")) individuals <- list(paste0("Individual_1-", 1:5), paste0("Individual_2-", 1:5)) data_pg <- set.populations(data_pg, individuals)
Here the sequences originate from two population and an outgroup. The outgroup is required for most summary statistics.
We can now convert
data_pg using the
Next, we calculate summary statistics using
model <- coal_model(c(5, 5, 2), 1, 25) + feat_mutation(5) + feat_outgroup(3) + sumstat_sfs(population = 1) stats <- calc_sumstats_from_data(model, segsites) stats$sfs
Alternatively, it is also possible to pass the
data_pg object directly to
Please refer to the help pages for
calc_sumstats_from_data for additional information.