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# $Id: Coalescent.pm,v 1.11.4.1 2006/10/02 23:10:23 sendu Exp $
#
# BioPerl module for Bio::PopGen::Simulation::Coalescent
#
# Cared for by Jason Stajich <jason-at-bioperl-dot-org>
#
# Copyright Jason Stajich
#
# You may distribute this module under the same terms as perl itself
# POD documentation - main docs before the code
=head1 NAME
Bio::PopGen::Simulation::Coalescent - A Coalescent simulation factory
=head1 SYNOPSIS
use Bio::PopGen::Simulation::Coalescent;
my @taxonnames = qw(SpeciesA SpeciesB SpeciesC SpeciesD);
my $sim1 = Bio::PopGen::Simulation::Coalescent->new(-samples => \@taxonnames);
my $tree = $sim1->next_tree;
# add 20 mutations randomly to the tree
$sim1->add_Mutations($tree,20);
# or for anonymous samples
my $sim2 = Bio::PopGen::Simulation::Coalescent->new( -sample_size => 6,
-maxcount => 50);
my $tree2 = $sim2->next_tree;
# add 20 mutations randomly to the tree
$sim2->add_Mutations($tree2,20);
=head1 DESCRIPTION
Builds a random tree every time next_tree is called or up to -maxcount
times with branch lengths and provides the ability to randomly add
mutations onto the tree with a probabilty proportional to the branch
lengths.
This algorithm is based on the make_tree algorithm from Richard Hudson 1990.
Hudson, R. R. 1990. Gene genealogies and the coalescent
process. Pp. 1-44 in D. Futuyma and J. Antonovics, eds. Oxford
surveys in evolutionary biology. Vol. 7. Oxford University
Press, New York.
This module was previously named Bio::Tree::RandomTree
=head1 FEEDBACK
=head2 Mailing Lists
User feedback is an integral part of the evolution of this and other
Bioperl modules. Send your comments and suggestions preferably to
the Bioperl mailing list. Your participation is much appreciated.
bioperl-l@bioperl.org - General discussion
http://bioperl.org/wiki/Mailing_lists - About the mailing lists
=head2 Reporting Bugs
Report bugs to the Bioperl bug tracking system to help us keep track
of the bugs and their resolution. Bug reports can be submitted via
the web:
=head1 AUTHOR - Jason Stajich, Matthew Hahn
Email jason-at-bioperl-dot-org
Email matthew-dot-hahn-at-duke-dot-edu
=head1 APPENDIX
The rest of the documentation details each of the object methods.
Internal methods are usually preceded with a _
=cut
# Let the code begin...
use vars qw($PRECISION_DIGITS);
use strict;
$PRECISION_DIGITS = 3; # Precision for the branchlength
use base qw(Bio::Root::Root Bio::Factory::TreeFactoryI);
=head2 new
Title : new
Usage : my $obj = new Bio::PopGen::Simulation::Coalescent();
Function: Builds a new Bio::PopGen::Simulation::Coalescent object
Returns : an instance of Bio::PopGen::Simulation::Coalescent
Args : -samples => arrayref of sample names
OR
-sample_size=> number of samples (samps will get a systematic name)
-maxcount => [optional] maximum number of trees to provide
=cut
sub new{
my ($class,@args) = @_;
my $self = $class->SUPER::new(@args);
$self->{'_treecounter'} = 0;
$self->{'_maxcount'} = 0;
my ($maxcount, $samps,$samplesize ) = $self->_rearrange([qw(MAXCOUNT
SAMPLES
SAMPLE_SIZE)],
@args);
my @samples;
if( ! defined $samps ) {
if( ! defined $samplesize || $samplesize <= 0 ) {
$self->throw("Must specify a valid samplesize if parameter -SAMPLE is not specified (sampsize is $samplesize)");
}
foreach ( 1..$samplesize ) { push @samples, "Samp$_"; }
} else {
if( ref($samps) !~ /ARRAY/i ) {
$self->throw("Must specify a valid ARRAY reference to the parameter -SAMPLES, did you forget a leading '\\'?");
}
@samples = @$samps;
}
$self->samples(\@samples);
$self->sample_size(scalar @samples);
defined $maxcount && $self->maxcount($maxcount);
return $self;
}
=head2 next_tree
Title : next_tree
Usage : my $tree = $factory->next_tree
Function: Returns a random tree based on the initialized number of nodes
NOTE: if maxcount is not specified on initialization or
set to a valid integer, subsequent calls to next_tree will
continue to return random trees and never return undef
Returns : Bio::Tree::TreeI object
Args : none
=cut
sub next_tree{
my ($self) = @_;
# If maxcount is set to something non-zero then next tree will
# continue to return valid trees until maxcount is reached
# otherwise will always return trees
return undef if( $self->maxcount &&
$self->{'_treecounter'}++ >= $self->maxcount );
my $size = $self->sample_size;
my $in;
my @tree = ();
my @list = ();
for($in=0;$in < 2*$size -1; $in++ ) {
push @tree, { 'nodenum' => "Node$in" };
}
# in C we would have 2 arrays
# an array of nodes (tree)
# and array of pointers to these nodes (list)
# and we just shuffle the list items to do the
# tree topology generation
# instead in perl, we will have a list of hashes (nodes) called @tree
# and a list of integers representing the indexes in tree called @list
for($in=0;$in < $size;$in++) {
$tree[$in]->{'time'} = 0;
$tree[$in]->{'desc1'} = undef;
$tree[$in]->{'desc2'} = undef;
push @list, $in;
}
my $t=0;
# generate times for the nodes
for($in = $size; $in > 1; $in-- ) {
$t+= -2.0 * log(1 - $self->random(1)) / ( $in * ($in-1) );
$tree[2 * $size - $in]->{'time'} =$t;
}
# topology generation
for ($in = $size; $in > 1; $in-- ) {
my $pick = int $self->random($in);
my $nodeindex = $list[$pick];
my $swap = 2 * $size - $in;
$tree[$swap]->{'desc1'} = $nodeindex;
$list[$pick] = $list[$in-1];
$pick = int rand($in - 1);
$nodeindex = $list[$pick];
$tree[$swap]->{'desc2'} = $nodeindex;
$list[$pick] = $swap;
}
# Let's convert the hashes into nodes
my @nodes = ();
foreach my $n ( @tree ) {
push @nodes,
new Bio::Tree::AlleleNode(-id => $n->{'nodenum'},
-branch_length => $n->{'time'});
}
my $ct = 0;
foreach my $node ( @nodes ) {
my $n = $tree[$ct++];
if( defined $n->{'desc1'} ) {
$node->add_Descendent($nodes[$n->{'desc1'}]);
}
if( defined $n->{'desc2'} ) {
$node->add_Descendent($nodes[$n->{'desc2'}]);
}
}
my $T = Bio::Tree::Tree->new(-root => pop @nodes );
return $T;
}
=head2 add_Mutations
Title : add_Mutations
Usage : $factory->add_Mutations($tree, $mutcount);
Function: Adds mutations to a tree via a random process weighted by
branch length (it is a poisson distribution
as part of a coalescent process)
Returns : none
Args : $tree - Bio::Tree::TreeI
$nummut - number of mutations
$precision - optional # of digits for precision
=cut
sub add_Mutations{
my ($self,$tree, $nummut,$precision) = @_;
$precision ||= $PRECISION_DIGITS;
$precision = 10**$precision;
my @branches;
my @lens;
my $branchlen = 0;
my $last = 0;
my @nodes = $tree->get_nodes();
my $i = 0;
# Jason's somewhat simplistics way of doing a poission
# distribution for a fixed number of mutations
# build an array and put the node number in a slot
# representing the branch to put a mutation on
# but weight the number of slots per branch by the
# length of the branch ( ancestor's time - node time)
foreach my $node ( @nodes ) {
if( $node->ancestor ) {
my $len = int ( ($node->ancestor->branch_length -
$node->branch_length) * $precision);
if ( $len > 0 ) {
for( my $j =0;$j < $len;$j++) {
push @branches, $i;
}
$last += $len;
}
$branchlen += $len;
}
if( ! $node->isa('Bio::Tree::AlleleNode') ) {
bless $node, 'Bio::Tree::AlleleNode'; # rebless it to the right node
}
# This let's us reset the stored genotypes so we can keep reusing the
# same tree topology, but throw down mutations multiple times
$node->reset_Genotypes;
$i++;
}
# sanity check
$self->throw("branch len is $branchlen arraylen is $last")
unless ( $branchlen == $last );
my @mutations;
for( my $j = 0; $j < $nummut; $j++) {
my $index = int(rand($branchlen));
my $branch = $branches[$index];
# We're using an infinite sites model so every new
# mutation is a new site
my $g = new Bio::PopGen::Genotype(-marker_name => "Mutation$j",
-alleles => [1]);
$nodes[$branch]->add_Genotype($g);
push @mutations, "Mutation$j";
# Let's add this mutation to all the children (push it down
# the branches to the tips)
foreach my $child ( $nodes[$branch]->get_all_Descendents ) {
$child->add_Genotype($g);
}
}
# Insure that everyone who doesn't have the mutation
# has the ancestral state, which is '0'
foreach my $node ( @nodes ) {
foreach my $m ( @mutations ) {
if( ! $node->has_Marker($m) ) {
my $emptyg = new Bio::PopGen::Genotype(-marker_name => $m,
-alleles => [0]);
$node->add_Genotype($emptyg);
}
}
}
}
=head2 maxcount
Title : maxcount
Usage : $obj->maxcount($newval)
Function:
Returns : Maxcount value
Args : newvalue (optional)
=cut
sub maxcount{
my ($self,$value) = @_;
if( defined $value) {
if( $value =~ /^(\d+)/ ) {
$self->{'maxcount'} = $1;
} else {
$self->warn("Must specify a valid Positive integer to maxcount");
$self->{'maxcount'} = 0;
}
}
return $self->{'_maxcount'};
}
=head2 samples
Title : samples
Usage : $obj->samples($newval)
Function:
Example :
Returns : value of samples
Args : newvalue (optional)
=cut
sub samples{
my ($self,$value) = @_;
if( defined $value) {
if( ref($value) !~ /ARRAY/i ) {
$self->warn("Must specify a valid array ref to the method 'samples'");
$value = [];
}
$self->{'samples'} = $value;
}
return $self->{'samples'};
}
=head2 sample_size
Title : sample_size
Usage : $obj->sample_size($newval)
Function:
Example :
Returns : value of sample_size
Args : newvalue (optional)
=cut
sub sample_size{
my ($self,$value) = @_;
if( defined $value) {
$self->{'sample_size'} = $value;
}
return $self->{'sample_size'};
}
=head2 random
Title : random
Usage : my $rfloat = $node->random($size)
Function: Generates a random number between 0 and $size
This is abstracted so that someone can override and provide their
own special RNG. This is expected to be a uniform RNG.
Returns : Floating point random
Args : $maximum size for random number (defaults to 1)
=cut
sub random{
my ($self,$max) = @_;
return rand($max);
}
1;