Data from: Selection of Pairings Reaching Evenly Across the Data (SPREAD): a simple algorithm to design maximally informative fully crossed mating experiments
Data files
Aug 20, 2015 version files 27.42 MB
-
all_matings_distance.csv
15.89 KB
-
all_matings_spore_simulations.csv
50.14 KB
-
Figure2_SPREAD.R
611 B
-
Figure3_Table2_SPREAD.R
5.68 KB
-
Figure4_SPREAD.R
12.78 KB
-
README_for_all_matings_spore_simulations.txt
622 B
-
README_for_SPREAD_Rdata.txt
1.38 KB
-
SPREAD_Rdata.zip
27.32 MB
-
strain_mating_type.csv
570 B
-
Table1_SPREAD.R
9.73 KB
Abstract
We present a novel algorithm for the design of crossing experiments. The algorithm identifies a set of individuals (a "crossing-set") from a larger pool of potential crossing-sets by maximizing the diversity of traits of interest, for example, maximizing the range of genetic and geographic distances between individuals included in the crossing-set. To calculate diversity, we use the mean nearest neighbor distance of crosses plotted in trait space. We implement our algorithm on a real dataset of Neurospora crassa strains, using the genetic and geographic distances between potential crosses as a two-dimensional trait space. In simulated mating experiments, crossing-sets selected by our algorithm provide better estimates of underlying parameter values than randomly chosen crossing-sets.