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Summary

Bio::PopGen::Simulation::Coalescent - A Coalescent simulation factory

Package variables

Included modules

Inherit

Bio::Factory::TreeFactoryI Bio::Root::Root

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);

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

Methods

Methods description

 Title   : new
 Usage   : my $obj = Bio::PopGen::Simulation::Coalescent->new();
 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

 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

 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

 Title   : maxcount
 Usage   : $obj->maxcount($newval)
 Function: 
 Returns : Maxcount value
 Args    : newvalue (optional)

 Title   : samples
 Usage   : $obj->samples($newval)
 Function: 
 Example : 
 Returns : value of samples
 Args    : newvalue (optional)

 Title   : sample_size
 Usage   : $obj->sample_size($newval)
 Function: 
 Example : 
 Returns : value of sample_size
 Args    : newvalue (optional)

 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)

Methods code

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;

}

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 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, 
	   Bio::Tree::AlleleNode->new(-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;

}

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 $g = Bio::PopGen::Genotype->new(-marker_name  => "Mutation$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 = Bio::PopGen::Genotype->new(-marker_name => $m,
						      -alleles     => [0]);
	       $node->add_Genotype($emptyg);
	   }
       }
   }

}

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'};

}

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'};

}

sub sample_size {

   my ($self,$value) = @_;
   if( defined $value) {
      $self->{'sample_size'} = $value;
    }
    return $self->{'sample_size'};

}

sub random {

   my ($self,$max) = @_;
   return rand($max);
}

1;

}

General documentation

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with code and data examples if at all possible.

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The rest of the documentation details each of the object methods.
Internal methods are usually preceded with a _