Data from: Interpreting the FLOCK algorithm from a statistical perspective

We show that the algorithm in the program FLOCK (Duchesne &
Turgeon 2009) can be interpreted as an estimation procedure based on a
model essentially identical to the STRUCTURE (Pritchard et al. 2000) model
with no admixture and non-correlated allele frequency priors. Rather than
using MCMC, the FLOCK algorithm searches for the maximum-a-posteriori
estimate of this STRUCTURE model via a simulated annealing algorithm with
a rapid cooling schedule (namely, the exponent on the objective function
--> ∞). We demonstrate the similarities between the two programs in
a two step approach. First, to enable rapid batch processing of many
simulated data sets, we modified the source code of STRUCTURE to use the
FLOCK algorithm, producing the program FLOCKTURE. With simulated data we
confirmed that results obtained with FLOCK and FLOCKTURE are very similar
(though ockture is some 200 times faster). Second, we simulated multiple
large data sets under varying levels of population differentiation for
both microsatellite and SNP genotypes. We analyzed them with FLOCKTURE and
STRUCTURE and assessed each program on its ability to cluster individuals
to their correct subpopulation. We show that FLOCKTURE yields results
similar to STRUCTURE albeit with greater variability from run to run.
FLOCKTURE did perform better than STRUCTURE when genotypes were composed
of SNPs and differentiation was moderate (FST = 0.022 - 0.032). When
differentiation was low, STRUCTURE outperformed FLOCKTURE for both marker
types. On large data sets like those we simulated, it appears that
FLOCK's reliance on inference rules regarding its “plateau record”
are not helpful. Interpreting FLOCK's algorithm as a special case of
the model in STRUCTURE should aid in understanding the program's
output and behavior.