diff multilocus_genotype.R @ 0:725b160c91f0 draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/multilocus_genotype.R	Thu Oct 25 10:50:53 2018 -0400
@@ -0,0 +1,144 @@
+#!/usr/bin/env Rscript
+
+suppressPackageStartupMessages(library("optparse"))
+suppressPackageStartupMessages(library("vcfR"))
+suppressPackageStartupMessages(library("poppr"))
+suppressPackageStartupMessages(library("adegenet"))
+suppressPackageStartupMessages(library("ape"))
+suppressPackageStartupMessages(library("ggplot2"))
+suppressPackageStartupMessages(library("knitr"))
+
+option_list <- list(
+    make_option(c("--input_vcf"), action="store", dest="input_vcf", help="VCF input file")
+    make_option(c("--input_pop_info"), action="store", dest="input_pop_info", help="Population information input file")
+)
+
+parser <- OptionParser(usage="%prog [options] file", option_list=option_list);
+args <- parse_args(parser, positional_arguments=TRUE);
+opt <- args$options;
+
+get_file_path = function(file_name) {
+    file_path = paste("output_plots_dir", file_name, sep="/");
+    return(file_path);
+}
+
+#extract Provesti's distance from the distance matrix
+provesti_distance <- function(distance, selection) {
+  eval(parse(text=paste("as.matrix(distance)[", selection, "]")));
+}
+
+#Read in VCF input file.
+vcf <- read.vcfR(opts$input_vcf);
+
+# Convert VCF file into formats compatiable with the Poppr package.
+gind <- 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);
+# Convert genind to genclone object
+gclo <- as.genclone(gind);
+# Calculate the bitwise distance between individuals,
+# the following is similar to Provesti's distance.
+xdis <- bitwise.dist(gclo);
+# All alleles must match to make a unique multilocus
+# genotype (“original” naive approach). This is the
+# default behavior of poppr.
+mll(gclo) <- "original";
+# The method above does not take the genetic distance
+# into account, but we can use this matrix to collapse
+# MLGs that are under a specified distance threshold.
+# To determine the distance threshold, we will generate
+# a neighbor-joining tree for all samples.
+
+# Start PDF device driver.
+dev.new(width=20, height=30);
+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.
+cols <- c("skyblue2","#C38D9E", '#E8A87C',"darkcyan","#e27d60");
+set.seed(999);
+
+theTree <- gclo %>%
+  aboot(dist=provesti.dist, sample=50, tree="nj", cutoff=50, quiet=TRUE) %>%
+   # Organize branches by clade.
+  ladderize();
+plot.phylo(theTree, tip.color=cols[gclo$pop], label.offset=0.0125, cex=0.7, font=2, lwd=4);
+ # Add a scale bar showing 5% difference.
+add.scale.bar(length=0.05, cex=0.65);
+nodelabels(theTree$node.label, cex=.5, adj=c(1.5, -0.1), frame="n", font=3, xpd=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("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
+# matrix. Here we will use the bitwise distance matrix
+# calculated above.
+
+# Multilocus genotypes (threshold of 1%).
+mlg.filter(gclo, distance= xdis) <- 0.01;
+m <- mlg.table(gclo, background=TRUE, color=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("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.
+gclo_filtered <- filter_stats(gclo, distance=xdis, plot=TRUE);
+# Turn off device driver to flush output.
+dev.off();
+
+# Create table of MLGs.
+id <- mlg.id(gclo);
+df <- data.frame(matrix((id), byrow=T));
+
+# Start PDF device driver.
+dev.new(width=20, height=30);
+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);
+# 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")
+pdf(file=file_path, width=20, height=30, bg="white");
+p <- last_plot();
+p + geom_smooth() + xlim(0, 100) + theme_bw();
+
+# From the collapsed MLGs, we can calculate genotypic
+# richness, diversity and eveness.
+kable(poppr(gclo));
+kable(diversity_ci(gclo, n=100L, raw=FALSE ));
+
+# Now we can correct the original data for clones using
+# clonecorrect. This step will reduce the dataset to
+# only have 1 representative genotype per multilocus
+# lineages (MLL).
+gclo_cor <- clonecorrect(gclo, strata=NA);
+
+# Lastly, we can use a discriminant analysis of principal
+# components to cluster genetically related individuals.
+# This multivariate statistical approach partions the
+# sample into a between-group and within- group component,
+# in an effort to maximize discrimination between groups.
+# Data is first transformed using a principal components
+# analysis (PCA) and subsequently clusters are identified
+# using discriminant analysis (DA).More information can be
+# found here.
+dapc.coral <- dapc(gclo_cor, var.contrib=TRUE, scale=FALSE, n.pca=62, n.da=nPop(gclo_cor)-1);
+scatter(dapc.coral, cell=0, pch=18:23, cstar=0, lwd=2, lty=2, legend=TRUE, cleg=0.75, clabel=TRUE, col=cols);
+# Turn off device driver to flush output.
+dev.off();
+