# $Id: PopStats.pm,v 1.6 2004/01/27 23:13:30 jason Exp $
#
# BioPerl module for Bio::PopGen::PopStats
#
# Cared for by Jason Stajich <jason-at-bioperl.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::PopStats - A collection of methods for calculating
statistics about a population or sets of populations

=head1 SYNOPSIS

  use Bio::PopGen::PopStats;
  my $stats = new Bio::PopGen::PopStats(); # add -haploid => 1 
                                           # to process haploid data

=head1 DESCRIPTION

Calculate various population structure statistics, most notably Wright's Fst.

=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/MailList.shtml  - 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:

  http://bugzilla.bioperl.org/

=head1 AUTHOR - Jason Stajich

Email jason-at-bioperl.org

=head1 CONTRIBUTORS

Matthew Hahn, matthew.hahn-at-duke.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...


package Bio::PopGen::PopStats;
use vars qw(@ISA);
use strict;

# Object preamble - inherits from Bio::Root::Root

use Bio::Root::Root;


@ISA = qw(Bio::Root::Root );

=head2 new

 Title   : new
 Usage   : my $obj = new Bio::PopGen::PopStats();
 Function: Builds a new Bio::PopGen::PopStats object 
 Returns : an instance of Bio::PopGen::PopStats
 Args    : -haploid => 1 (if want to use haploid calculations)


=cut

sub new {
  my($class,@args) = @_;

  my $self = $class->SUPER::new(@args);
  my ($haploid) = $self->_rearrange([qw(HAPLOID)],@args);
  if( $haploid ) { $self->haploid_status(1) }
  return $self;
}


=head2 haploid_status

 Title   : haploid_status
 Usage   : $obj->haploid_status($newval)
 Function: Boolean value for whether or not to do haploid 
           or diploid calculations, where appropriate
 Returns : Boolean
 Args    : on set, new boolean value optional)


=cut

sub haploid_status{
    my $self = shift;
    return $self->{'haploid_status'} = shift if @_;
    return $self->{'haploid_status'};
}


# Implementation provided my Matthew Hahn, massaged by Jason Stajich

=head2 Fst

 Title   : Fst
 Usage   : my $fst = $stats->Fst(\@populations,\@markernames)
 Function: Calculate Wright's Fst based on a set of sub-populations
           and specific markers
 Returns : Fst value (a value between 0 and 1)
 Args    : Arrayref of populations to process
           Arrayref of marker names to process
 Note    : Based on Weir BS, Genetics Data Analysis II, 199?

=cut

#' make emacs happy here
sub Fst {
   my ($self,$populations,$markernames) = @_;

   if( ! defined $populations || 
       ref($populations) !~ /ARRAY/i ) { 
       $self->warn("Must provide a valid arrayref for populations");
       return undef;
   } elsif( ! defined $markernames ||
	    ref($markernames) !~ /ARRAY/i ) {
       $self->warn("Must provide a valid arrayref for marker names");
       return undef;
   }
   my $num_sub_pops          = scalar @$populations;

   if( $num_sub_pops < 2 ) {
       $self->warn("Must provide at least 2 populations for this test, you provided $num_sub_pops");
       return undef;
   }

   # This code assumes that pop 1 contains at least one of all the
   # alleles - need to do some more work to insure that the complete 
   # set of alleles is seen.
   my $Fst;
   my ($TS_sub1,$TS_sub2);

   foreach my $marker ( @$markernames ) {
       # Get all the alleles from all the genotypes in all subpopulations
       my %allAlleles;
       foreach my $allele ( map { $_->get_Alleles() } 
			    map { $_->get_Genotypes($marker) } @$populations ){
	   $allAlleles{$allele}++;
       }
       my @alleles = keys %allAlleles;

       foreach my $allele_name ( @alleles ) {
	   my $avg_samp_size         = 0; # n-bar
	   my $avg_allele_freq       = 0; # p-tilda-A-dot

	   my $total_samples_squared = 0; # 
	   my $sum_heterozygote      = 0;

	   my @marker_freqs;

	   # Walk through each population, get the calculated allele frequencies
	   # for the marker, do some bookkeeping


	   foreach my $pop ( @$populations ) {
	       my $s = $pop->get_number_individuals($marker);

	       $avg_samp_size += $s;
	       $total_samples_squared += $s**2;

	       my $markerobj = $pop->get_Marker($marker);
	       if( ! defined $markerobj ) { 
		   $self->warn("Could not derive Marker for $marker ".
			       "from population ". $pop->name);
		   return undef;
	       }

	       my $freq_homozygotes = 
		   $pop->get_Frequency_Homozygotes($marker,$allele_name);
	       my %af = $markerobj->get_Allele_Frequencies();
	       my $all_freq = ( ($af{$allele_name} || 0));

	       $avg_allele_freq += $s * $all_freq;
	       $sum_heterozygote += (2 * $s)*( $all_freq - $freq_homozygotes);

	       push @marker_freqs, \%af;
	   }
	   my $total_samples =  $avg_samp_size;	# sum of n over i sub-populations
	   $avg_samp_size /= $num_sub_pops;
	   $avg_allele_freq /= $total_samples;

	   # n-sub-c
	   my $adj_samp_size = ( 1/ ($num_sub_pops - 1)) *
	       ( $total_samples - ( $total_samples_squared/$total_samples));

	   my $variance              = 0; # s-squared-sub-A
	   my $sum_variance          = 0;
	   my $i = 0;		# we have cached the marker info
	   foreach my $pop ( @$populations ) {
	       my $s = $pop->get_number_individuals($marker);
	       my %af = %{$marker_freqs[$i++]};
	       $sum_variance += $s * (( ($af{$allele_name} || 0) - 
					$avg_allele_freq)**2);
	   }
	   $variance = ( 1 / (( $num_sub_pops-1)*$avg_samp_size))*$sum_variance;

	   # H-tilda-A-dot
	   my $freq_heterozygote = ($sum_heterozygote / $total_samples);

	   if( $self->haploid_status ) {
	       # Haploid calculations

	       my $T_sub1 = $variance - 
		   ( ( 1/($avg_samp_size-1))*
		     ( ($avg_allele_freq*(1-$avg_allele_freq))-
		       ( (($num_sub_pops-1)/$num_sub_pops)*$variance)));
	       my $T_sub2 = ( (($adj_samp_size-1)/($avg_samp_size-1))*
			      $avg_allele_freq*(1-$avg_allele_freq) ) +
			      ( 1 + ( (($num_sub_pops-1)*
				       ($avg_samp_size-$adj_samp_size))/ 
				      ($avg_samp_size - 1))) * 
				      ($variance/$num_sub_pops);


	       #to get total Fst from all alleles (if more than two) or all
	       #loci (if more than one), we need to calculate $T_sub1 and
	       #$T_sub2 for all alleles for all loci, sum, and then divide
	       #again to get Fst.
	       $TS_sub1 += $T_sub1;
	       $TS_sub2 += $T_sub2;

	   } else { 
	       my $S_sub1 = $variance - ( (1/($avg_samp_size-1))*
					  ( ($avg_allele_freq*
					     (1-$avg_allele_freq)) - 
					    ((($num_sub_pops-1)/$num_sub_pops)*
					     $variance)-0.25*$freq_heterozygote ) );
	       my $S_sub2 = ($avg_allele_freq*(1-$avg_allele_freq)) - 
		   ( ($avg_samp_size/($num_sub_pops*($avg_samp_size-1)))*
		     ( ((($num_sub_pops*($avg_samp_size- $adj_samp_size))/
			 $avg_samp_size)*$avg_allele_freq*
			(1-$avg_allele_freq)) - 
		       ( (1/$avg_samp_size)* (($avg_samp_size-1)+
					      ($num_sub_pops-1)*
					      ($avg_samp_size-
					       $adj_samp_size) )*$variance ) - 
		       ( (($num_sub_pops*($avg_samp_size-$adj_samp_size))/
			  (4*$avg_samp_size*$adj_samp_size))*
			 $freq_heterozygote ) ) );

	       my $S_sub3 = ($adj_samp_size/(2*$avg_samp_size))*
		   $freq_heterozygote;

	       #Again, to get the average over many alleles or many loci,
	       #we will have to run the above for each and then sum the $S
	       #variables and recalculate the F statistics 
	       $TS_sub1 += $S_sub1;
	       $TS_sub2 += $S_sub2;
	   } 
       }
   }
   # $Fst_diploid = $S_sub1/$S_sub2;
   #my $Fit_diploid = 1 - ($S_sub3/$S_sub2);
   #my $Fis_diploid = ($Fit_diploid-$Fst_diploid)/(1-$Fst_diploid);
   $Fst = $TS_sub1 / $TS_sub2;

   return $Fst;
}

1;
