# HG changeset patch
# User greg
# Date 1540488827 14400
# Node ID 1bc815d9c8c5b9d7a80cb64b99d48920b5af2ece
# Parent  86aaadf36a4f17ccec2ed43fb09d46a6873dc4d4
Uploaded

diff -r 86aaadf36a4f -r 1bc815d9c8c5 multilocus_genotype.R
--- a/multilocus_genotype.R	Thu Oct 25 11:10:17 2018 -0400
+++ b/multilocus_genotype.R	Thu Oct 25 13:33:47 2018 -0400
@@ -22,21 +22,21 @@
     return(file_path);
 }
 
-#extract Provesti's distance from the distance matrix
+# Extract Provesti's distance from the distance matrix.
 provesti_distance <- function(distance, selection) {
   eval(parse(text=paste("as.matrix(distance)[", selection, "]")));
 }
 
-#Read in VCF input file.
+# Read in VCF input file.
 vcf <- read.vcfR(opt$input_vcf);
 
 # Convert VCF file into formats compatiable with the Poppr package.
-gind <- vcfR2genind(vcf);
+genind <- vcfR2genind(vcf);
 # Add population information to the genind object.
 poptab <- read.table(opt$input_pop_info, check.names=FALSE, header=T, na.strings = c("", "NA"));
-gind@pop <- as.factor(poptab$region);
+genind@pop <- as.factor(poptab$region);
 # Convert genind to genclone object
-gclo <- as.genclone(gind);
+gclo <- as.genclone(genind);
 # Calculate the bitwise distance between individuals,
 # the following is similar to Provesti's distance.
 xdis <- bitwise.dist(gclo);
@@ -52,7 +52,7 @@
 
 # Start PDF device driver.
 dev.new(width=20, height=30);
-file_path = get_file_path("phylogeny_tree.pdf")
+file_path = get_file_path("phylogeny_tree.pdf");
 pdf(file=file_path, width=20, height=30, bg="white");
 # Create a phylogeny of samples based on distance matrices
 # colors.
@@ -72,7 +72,7 @@
 
 # Start PDF device driver.
 dev.new(width=20, height=30);
-file_path = get_file_path("dissimiliarity_distance_matrix.pdf")
+file_path = get_file_path("dissimiliarity_distance_matrix.pdf");
 pdf(file=file_path, width=20, height=30, bg="white");
 # Use of mlg.filter() will create a dissimiliarity distance
 # matrix from the data and then filter based off of that
@@ -87,7 +87,7 @@
 
 # Start PDF device driver.
 dev.new(width=20, height=30);
-file_path = get_file_path("filter_stats.pdf")
+file_path = get_file_path("filter_stats.pdf");
 pdf(file=file_path, width=20, height=30, bg="white");
 # Different clustering methods for tie breakers used by
 # mlg.filter, default is farthest neighbor.
@@ -101,18 +101,18 @@
 
 # Start PDF device driver.
 dev.new(width=20, height=30);
-file_path = get_file_path("genotype_accumulation_curve.pdf")
+file_path = get_file_path("genotype_accumulation_curve.pdf");
 pdf(file=file_path, width=20, height=30, bg="white");
 # We can use the genotype_curve() function to create a
 # genotype accumulation curve to determine the minimum
 # number of loci to identify unique MLGs.
-gac <- genotype_curve(gind, sample=5, quiet=TRUE);
+gac <- genotype_curve(genind, sample=5, quiet=TRUE);
 # Turn off device driver to flush output.
 dev.off();
 
 # Start PDF device driver.
 dev.new(width=20, height=30);
-file_path = get_file_path("genotype_accumulation_curve_for_gind.pdf")
+file_path = get_file_path("genotype_accumulation_curve_for_genind.pdf");
 pdf(file=file_path, width=20, height=30, bg="white");
 p <- last_plot();
 p + geom_smooth() + xlim(0, 100) + theme_bw();