Raw content of Bio::EnsEMBL::ExternalData::Haplotype::Select
<![CDATA[# module Bio::EnsEMBL::ExternalData::Haplotype::Select.pm
# cared by Pedro M. Gomez-Fabre <pgf18872@gsk.com>
#
#
=head1 NAME
Bio::PopGen::Haplotype::Select - Select htSNP from a haplotype set
=head1 SYNOPSIS
use Bio::PopGen::Haplotype::Select;
my $obj = Select->new($hap,$snp,$pop);
=head1 DESCRIPTION
Bio::PopGen::Haplotype::Select
Select the minimal set of SNP that contains the full information about
the haplotype without redundancies.
Take as input the followin values:
=over 4
=item - the haplotype block (array of array).
=item - the snp id (array).
=item - family information and frequency (array of array).
=back
The final haplotype is generated in a numerical format and the SNP's sets can
be retrieve from the module.
B<considerations:>
- If you force to include a family with indetermination, the SNP's with
indetermination will be removed from the analysis, so consider before
to place your data set what do you really want to do.
- If two families have the same information (identical haplotype), one of them
will be removed and the removed files will be stored classify as removed.
- Only are accepted for calculation A, C, G, T and - (as deletion) and their
combinations. Any other value as n or ? will be considered as degenerations due
to lack of information.
=head1 RATIONALE
On a haplotype set is expected that some of the SNP and their variations
contribute in the same way to the haplotype. Eliminating redundancies will
produce a minimal set of SNP's that can be used as input for a taging
selection process. On the process SNP's with the same variation are clustered
on the same group.
The idea is that because the tagging haplotype process is exponential. All
redundant information we could eliminate on the tagging process will help to
find a quick result.
=head1 CONSTRUCTORS
my $obj = Select->new($hap,$snp,$pop);
# where $hap, $snp and $pop are in the format:
my $hap = [
'acgt',
'agtc',
'cgtc'
];
my $snp = [qw/s1 s2 s3 s4/];
my $pop = [
[qw/ uno 0.20/],
[qw/ dos 0.20/],
[qw/ tres 0.15/],
];
=head1 OBJECT METHODS
See Below for more detailed summaries.
=head1 FEEDBACK
=head2 Mailing Lists
User feedback is an integral part of the evolution of this and other
Bioperl modules. Send your comments and suggestions preferably to
the Bioperl mailing list. Your participation is much appreciated.
bioperl-l@bioperl.org - General discussion
http://bioperl.org/MailList.shtml - About the mailing lists
=head2 Reporting Bugs
Report bugs to the Bioperl bug tracking system to help us keep track
of the bugs and their resolution. Bug reports can be submitted via
the web:
http://bugzilla.bioperl.org/
=head1 AUTHOR - Pedro M. Gomez-Fabre
Email pedro.fabre-at-gen.gu.se
=head1 APPENDIX
See at the end of the POD.
=cut
# Let the code begin...
package Bio::EnsEMBL::ExternalData::Haplotype::Select;
# package Select;
use vars qw(@ISA);
use strict;
use Data::Dumper;
use Storable qw(dclone);
=head2 new
Title : new
Function: constructor of the class.
Returns : self hash
Args : input haplotype (array of array)
snp_ids (array)
pop_freq (array of array)
Status : public
=cut
#------------------------
sub new {
#------------------------
my ($class, @args) = @_;
my $self = {
'input_block' => $args[0],
'snp_ids' => $args[1],
'pop_freq' => $args[2]};
# if the input is not well formed complained and exit.
_check_input($self);
_do_it($self);
bless $self, $class;
return $self;
}
=head2 _check_input
Title : _check_input
Usage : _check_input($self)
Function: check for errors on the input
Returns : self hash
Args : self
Status : internal
=cut
#------------------------
sub _check_input{
#------------------------
my $self = shift;
_haplotype_length_error($self);
_population_error($self);
}
=head2 _haplotype_length_error
Title : _haplotype_length_error
Usage : _haplotype_length_error($self)
Function: check if the haplotype length is the same that the one on the
SNP id list. If not break and exit
Returns : self hash
Args : self
Status : internal
=cut
#------------------------
sub _haplotype_length_error{
#------------------------
my $self = shift;
my $input_block = $self->{'input_block'};
my $snp_ids = $self->{'snp_ids'};
#############################
# define error list
#############################
my $different_haplotype_length = 0;
##############################
# get parameters used to find
# the errors
##############################
my $snp_number = scalar @$snp_ids;
my $number_of_families = scalar @$input_block;
my $h = 0; # haplotype position
############################
# haplotype length
#
# if the length differs from the number of ids
############################
for ($h=0; $h<$#$input_block+1 ; $h++){
if (length $input_block->[$h] != $snp_number){
$different_haplotype_length = 1;
last;
}
}
# haploytypes does not have the same length
if ($different_haplotype_length){
die ("The number of snp ids is $snp_number and ".
"the lenght of the family (". ($h+1) .") [".
$input_block->[$h]."] is ",
length $input_block->[$h], "\n");
}
}
=head2 _population_error
Title : _population_error
Usage : _population_error($self)
Function: use input_block and pop_freq test if the number of elements
match. If doesn't break and quit.
Returns : self hash
Args : self
Status : internal
=cut
#------------------------
sub _population_error{
#------------------------
my $self = shift;
my $input_block = $self->{'input_block'};
my $pop_freq = $self->{'pop_freq'};
#############################
# define error list
#############################
my $pop_freq_elements_error = 0; # matrix bad formed
##############################
# get parameters used to find
# the errors
##############################
my $number_of_families = scalar @$input_block;
my $pf = 0; # number of elements on population frequency
my $frequency = 0; # population frequency
my $p_f_length = 0;
# check if the pop_freq array is well formed and if the number
# of elements fit with the number of families
#############################
# check population frequency
#
# - population frequency matrix need to be well formed
# - get the frequency
# - calculate number of families on pop_freq
#############################
for ($pf=0; $pf<$#$pop_freq+1; $pf++){
$frequency += $pop_freq->[$pf]->[1];
if ( scalar @{$pop_freq->[$pf]} !=2){
$p_f_length = scalar @{$pop_freq->[$pf]};
$pop_freq_elements_error = 1;
last;
}
}
###########################
## error processing
###########################
# The frequency shouldn't be greater than 1
if ($frequency >1) {
warn ("The frequency for this set is $frequency (greater than 1)\n");
}
# the haplotype matix is not well formed
if ($pop_freq_elements_error){
die ("the frequency matrix is not well formed\n".
"The number of elements for family ".($pf+1)." is ".
"$p_f_length and should be 2 for family @{$pop_freq->[$pf]}\n".
"Format should be:\n".
"haplotype_id\t frequency\n"
);
}
# the size does not fit on pop_freq array
# with the one in haplotype (input_block)
if ($pf != $number_of_families) {
die ("The number of families on population array ($pf) ".
"does not fit with the number of families on the ".
"haplotype array ($number_of_families)\n");
}
}
=head2 _do_it
Title : _do_it
Usage : _do_it($self)
Function: Process the input generating the results.
Returns : self hash
Args : self
Status : internal
=cut
#------------------------
sub _do_it{
#------------------------
my $self = shift;
# first we are goinf to define here all variables we are going to use
$self->{'w_hap'} = [];
$self->{'w_pop_freq'} = dclone ( $self->{pop_freq} );
$self->{'deg_fam'} = {};
$self->{'snp_type'} = {}; # type of snp on the set. see below
$self->{'alleles_number'} = 0; # number of variations (biallelic,...)
$self->{'snp_type_code'} = [];
$self->{'ht_type'} = []; # store the snp type used on the htSet
$self->{'snp_info'} = []; # resume of all snp information
$self->{'split_hap'} = [];
$self->{'snp_and_code'} = [];
# we classify the SNP under snp_type
$self->{snp_type}->{useful_snp} = dclone ( $self->{snp_ids} );
$self->{snp_type}->{deg_snp} = []; # deg snp
$self->{snp_type}->{silent_snp} = []; # not a real snp
# split the haplotype
_split_haplo ($self);
# first we convert to upper case the haplotype
# to make A the same as a for comparison
_to_upper_case( $self->{w_hap} );
#######################################################
# check if any SNP has indetermination. If any SNP has
# indetermination this value will be removed.
#######################################################
_remove_deg ( $self );
#######################################################
# depending of the families you use some SNPs can be
# silent. This silent SNP's are not used on the
# creation of tags and has to be skipped from the
# analysis.
#######################################################
_rem_silent_snp ( $self );
#######################################################
# for the remaining SNP's we have to check if two
# families have the same value. If this is true, the families
# will produce the same result and therefore we will not find
# any pattern. So, the redundant families need to be take
# away from the analysis. But also considered for a further
# run.
#
# When we talk about a normal haplotype blocks this situation
# makes no sense but if we remove one of the snp because the
# degeneration two families can became the same.
# these families may be analised on a second round
#######################################################
_find_deg_fam ( $self );
#################################################################
# if the family list length is different to the lenght of the w_hap
# we can tell that tow columns have been considered as the same one
# and therefore we have to start to remove the values.
# remove all columns with degeneration
#
# For this calculation we don't use the frequency of the families.
# All information on the families are the same, This selection makes
# sense when you have different frequency.
#
# Note: on this version we don't classify the haplotype by frequency
# but if you need to do it. This is the place to do it!!!!
#################################################################
my @tmp =keys %{$self->{deg_fam}}; # just count the families
# if the size of the list is different to the size of the degenerated
# family. There is degeneration. And the redundancies will be
# removed.
if($#tmp != $#{$self->{ w_hap } } ){
_keep_these_fam($self->{w_hap}, \@tmp);
_keep_these_fam($self->{w_pop_freq}, \@tmp);
}
$self->{snp_type}->{silent_snp};
#################################################################
# the steps made before about removing snp and cluster families
# are just needed pre-process the haplotype before.
#
# Now is when the fun starts.
#
#
# once we have the this minimal matrix, we have to calculate the
# max multipliticy for the values. The max number of alleles found
# on the set. A normal haplotype is biallelic but we can not
# reject multiple variations.
##################################################################
_alleles_number ( $self );
##################################################################
# Now we have to convert the haplotype into number
#
# A C C - T
# C A G G C
# A C C C T
# C G G G C
#
# one haplotype like this transformed into number produce this result
#
# 0 0 0 0 0
# 1 1 1 1 1
# 0 0 0 2 0
# 1 2 1 1 1
#
##################################################################
_convert_to_numbers( $self );
###################################################################
# The next step is to calculate the type of the SNP.
# This process is made based on the position of the SNP, the value
# and its multiplicity.
###################################################################
_snp_type_code( $self );
###################################################################
# now we have all information we need to calculate the haplotype
# tagging SNP htSNP
###################################################################
_htSNP( $self );
###################################################################
# patch:
#
# all SNP have a code. but if the SNP is not used this code must
# be zero in case of silent SNP. This looks not to informative
# because all the information is already there. But this method
# compile the full set.
###################################################################
_snp_and_code_summary( $self );
}
=head2 input_block
Title : input_block
Usage : $obj->input_block()
Function: returns input block
Returns : reference to array of array
Args : none
Status : public
=cut
#------------------------
sub input_block{
#------------------------
my $self = shift;
return $self->{input_block};
}
=head2 snp_ids
Title : snp_ids
Usage : $obj->snp_ids()
Function: returns snp ids
Returns : reference to array
Args : none
Status : public
=cut
#------------------------
sub snp_ids{
#------------------------
my $self = shift;
return $self->{snp_ids};
}
=head2 hap_length
Title : hap_length
Usage : $obj->hap_length()
Function: get numbers of SNP on the haplotype
Returns : scalar
Args : none
Status : public
=cut
#------------------------
sub hap_length{
#------------------------
my $self = shift;
return scalar @{$self->{'snp_ids'}};
}
=head2 pop_freq
Title : pop_freq
Usage : $obj->pop_freq()
Function: returns population frequency
Returns : reference to array
Args : none
Status : public
=cut
#------------------------
sub pop_freq{
#------------------------
my $self = shift;
return $self->{pop_freq}
}
=head2 deg_snp
Title : deg_snp
Usage : $obj->deg_snp()
Function: returns snp_removes due to indetermination on their values
Returns : reference to array
Args : none
Status : public
=cut
#------------------------
sub deg_snp{
#------------------------
my $self = shift;
return $self->{snp_type}->{deg_snp};
}
=head2 snp_type
Title : snp_type
Usage : $obj->snp_type()
Function: returns hash with SNP type
Returns : reference to hash
Args : none
Status : public
=cut
#------------------------
sub snp_type{
#------------------------
my $self = shift;
return $self->{snp_type};
}
=head2 silent_snp
Title : silent_snp
Usage : $obj->silent_snp()
Function: some SNP's are silent (not contibuting to the haplotype)
and are not considering for this analysis
Returns : reference to a array
Args : none
Status : public
=cut
#------------------------
sub silent_snp{
#------------------------
my $self = shift;
return $self->{snp_type}->{silent_snp};
}
=head2 useful_snp
Title : useful_snp
Usage : $obj->useful_snp()
Function: returns list of SNP's that are can be used as htSNP. Some
of them can produce the same information. But this is
not considered here.
Returns : reference to a array
Args : none
Status : public
=cut
#------------------------
sub useful_snp{
#------------------------
my $self = shift;
return $self->{snp_type}->{useful_snp};
}
=head2 ht_type
Title : ht_type
Usage : $obj->ht_type()
Function: every useful SNP has a numeric code dependending of its
value and position. For a better description see
description of the module.
Returns : reference to a array
Args : none
Status : public
=cut
#------------------------
sub ht_type{
#------------------------
my $self = shift;
return $self->{ht_type};
}
=head2 ht_set
Title : ht_set
Usage : $obj->ht_set()
Function: returns the minimal haplotype in numerical format. This
haplotype contains the maximal information about the
haplotype variations but with no redundancies. It's the
minimal set that describes the haplotype.
Returns : reference to an array of arrays
Args : none
Status : public
=cut
#------------------------
sub ht_set{
#------------------------
my $self = shift;
return $self->{w_hap};
}
=head2 snp_type_code
Title : snp_type_code
Usage : $obj->snp_type_code()
Function: returns the numeric code of the SNPs that need to be
tagged that correspond to the SNP's considered in ht_set.
Returns : reference to an array
Args : none
Status : public
=cut
#------------------------
sub snp_type_code{
#------------------------
my $self = shift;
return $self->{snp_type_code};
}
=head2 snp_and_code
Title : snp_and_code
Usage : $obj->snp_and_code()
Function: Returns the full list of SNP's and the code associate to
them. If the SNP belongs to the group useful_snp it keep
this code. If the SNP is silent the code is 0. And if the
SNP is degenerated the code is -1.
Returns : reference to an array of array
Args : none
Status : public
=cut
#------------------------
sub snp_and_code{
#------------------------
my $self = shift;
return $self->{'snp_and_code'};
}
=head2 deg_fam
Title : deg_fam
Usage : $obj->deg_fam()
Function: Returns the a list with the degenerated haplotype.
Sometimes due to degeneration some haplotypes looks
the same and if we don't remove them it won't find
any tag.
Returns : reference to a hash of array
Args : none
Status : public
=cut
#------------------------
sub deg_fam{
#------------------------
my $self = shift;
return $self->{'deg_fam'};
}
=head2 split_hap
Title : split_hap
Usage : $obj->split_hap()
Function: simple representation of the haplotype base by base
Same information that input haplotype but base based.
Returns : reference to an array of array
Args : none
Status : public
=cut
#------------------------
sub split_hap{
#------------------------
my $self = shift;
return $self->{'split_hap'};
}
=head2 _split_haplo
=head2 _split_haplo
Title : _split_haplo
Usage : _split_haplo($self)
Function: Take a haplotype and split it into bases
Returns : self
Args : none
Status : internal
=cut
#------------------------
sub _split_haplo {
#------------------------
my $self = shift;
my $in = $self->{'input_block'};
my $out = $self->{'w_hap'};
# split every haplotype and store the result into $out
foreach (@$in){
push @$out, [split (//,$_)];
}
$self->{'split_hap'} = dclone ($out);
}
# internal method to convert the haplotype to uppercase
=head2 _to_upper_case
Title : _to_upper_case
Usage : _to_upper_case()
Function: make SNP or in-dels Upper case
Returns : self
Args : an AoA ref
Status : private
=cut
#------------------------
sub _to_upper_case {
#------------------------
my ($arr) =@_;
foreach my $aref (@$arr){
foreach my $value (@{@$aref} ){
$value = uc $value;
}
}
}
=head2 _remove_deg
Title : _remove_deg
Usage : _remove_deg()
Function: when have a indetermination or strange value this SNP
is removed
Returns : haplotype family set and degeneration list
Args : ref to an AoA and a ref to an array
Status : internal
=cut
#------------------------
sub _remove_deg {
#------------------------
my $self = shift;
my $hap = $self->{w_hap};
my $snp = $self->{snp_type}->{useful_snp};
my $deg_snp = $self->{snp_type}->{deg_snp};
my $rem = []; # take the position of the array to be removed
# first we work on the columns we have void values
$rem = _find_indet($hap,$rem); # find degenerated columns
if (@$rem){
# remove column on haplotype
_remove_col($hap,$rem); # remove list
# now remove the values from SNP id
_remove_snp_id($snp,$deg_snp,$rem); # remove list
}
}
=head2 _rem_silent_snp
Title : _rem_silent_snp
Usage : _rem_silent_snp()
Function: there is the remote possibilty that one SNP won't be a
real SNP on this situation we have to remove this SNP,
otherwise the program won't find any tag
Returns : nonthing
Args : ref to an AoA and a ref to an array
Status : internal
=cut
#------------------------
sub _rem_silent_snp {
#------------------------
my $self = shift;
my $hap = $self->{w_hap};
my $snp = $self->{snp_type}->{useful_snp};
my $silent_snp = $self->{snp_type}->{silent_snp};
my $rem = []; # store the positions to be removed
#find columns with no variation on the SNP, Real snp?
$rem = _find_silent_snps($hap);
if (@$rem){
# remove column on haplotype
_remove_col($hap,$rem);
# remove the values from SNP id
_remove_snp_id($snp,$silent_snp,$rem);
}
}
=head2 _find_silent_snps
Title : _find_silent_snps
Usage :
Function: list of snps that are not SNPs. All values for that
SNPs on the set is the same one. Look stupid but can
happend and if this happend you will not find any tag
Returns : nothing
Args :
Status :
=cut
#------------------------
sub _find_silent_snps{
#------------------------
my ($arr)=@_;
my $list =[]; # no snp list;
# determine the number of snp by the length of the first row.
# we assume that the matrix is squared.
my $colsn= @{$arr->[0]};
for (my $i=0;$i<$colsn;$i++){
my $different =0; # check degeneration
for my $r (1..$#$arr){
if($arr->[0][$i] ne $arr->[$r][$i]){
$different =1;
last;
}
}
if(!$different){
push (@$list, $i);
}
}
return $list;
}
=head2 _find_indet
Title : _find_indet
Usage :
Function: find column (SNP) with invalid or degenerated values
and store this values into the second parameter suplied.
Returns : nothing
Args : ref to AoA and ref to an array
Status : internal
=cut
#------------------------
sub _find_indet{
#------------------------
my ($arr, $list)=@_;
foreach my $i(0..$#$arr){
foreach my $j(0..$#{$arr->[$i]}){
unless ($arr->[$i][$j] =~ /[ACTG-]/){
if ($#$list<0){
push(@$list,$j);
}
else{
my $found =0; # check if already exist the value
foreach my $k(0..$#$list){
$found =1 if ($list->[$k] eq $j);
last if ($found);
}
if(!$found){
push(@$list,$j);
}
}
}
}
}
@$list = sort { $a <=> $b} @$list;
return $list;
}
=head2 _remove_col
Title : _remove_col
Usage :
Function: remove columns contained on the second array from
the first arr
Returns : nothing
Args : array of array reference and array reference
Status : internal
=cut
#------------------------
sub _remove_col{
#------------------------
my ($arr,$rem)=@_;
foreach my $col (reverse @$rem){
splice @$_, $col, 1 for @$arr;
}
}
=head2 _remove_snp_id
Title : _remove_snp_id
Usage :
Function: remove columns contained on the second array from
the first arr
Returns : nothing
Args : array of array reference and array reference
Status : internal
=cut
#------------------------
sub _remove_snp_id{
#------------------------
my ($arr,$removed,$rem_list)=@_;
push @$removed, splice @$arr, $_, 1 foreach reverse @$rem_list;
}
=head2 _find_deg_fam
Title : _find_deg_fam
Usage :
Function: create a list with the degeneration families
Returns : @array
Args : a ref to AoA
Status : public
=cut
#------------------------
sub _find_deg_fam{
#------------------------
my $self = shift;
my $arr = $self->{w_hap}; # the working haplotype
my $list = $self->{'deg_fam'}; # degenerated families
# we have to check all elements
foreach my $i(0..$#$arr){
# is the element has not been used create a key
unless ( _is_on_hash ($list,\$i) ) {
$list->{$i}=[$i];
};
foreach my $j($i+1..$#$arr){
my $comp = compare_arrays($arr->[$i],$arr->[$j]);
if($comp){
# as we have no elements we push this into the list
# check for the first element
my $key = _key_for_value($list,\$i);
push (@{$list->{$key}},$j);
last;
}
}
}
}
#------------------------
sub _key_for_value{
#------------------------
my($hash,$value)=@_;
foreach my $key (keys %$hash){
if( _is_there(\@{$hash->{$key}},$value)){
return $key;
}
}
}
#------------------------
sub _is_on_hash{
#------------------------
my($hash,$value)=@_;
foreach my $key (keys %$hash){
if( _is_there(\@{$hash->{$key}},$value)){
return 1;
}
}
}
#------------------------
sub _is_there{
#------------------------
my($arr,$value)=@_;
foreach my $el (@$arr){
if ($el eq $$value){
return 1;
}
}
}
=head2 _keep_these_fam
Title : _keep_these_fam
Usage :
Function: this is a basic approach, take a LoL and a list,
keep just the columns included on the list
Returns : nothing
Args : an AoA and an array
Status : public
=cut
#------------------------
sub _keep_these_fam{
#------------------------
my ($arr,$list)=@_;
# by now we just take one of the repetitions but you can weight
# the values by frequency
my @outValues=();
foreach my $k (@$list){
push @outValues, $arr->[$k];
}
#make arr to hold the new values
@$arr= @{dclone(\@outValues)};
}
=head2 compare_arrays
Title : compare_arrays
Usage :
Function: take two arrays and compare their values
Returns : 1 if the two values are the same
0 if the values are different
Args : an AoA and an array
Status : public
=cut
#------------------------
sub compare_arrays {
#------------------------
my ($first, $second) = @_;
return 0 unless @$first == @$second;
for (my $i = 0; $i < @$first; $i++) {
return 0 if $first->[$i] ne $second->[$i];
}
return 1;
}
=head2 _convert_to_numbers
Title : _convert_to_numbers
Usage : _convert_to_numbers()
Function: tranform the haplotype into numbers. before to do that
we have to consider the variation on the set.
Returns : nonthing
Args : ref to an AoA and a ref to an array
Status : internal
=cut
#------------------------
sub _convert_to_numbers{
#------------------------
my $self = shift;
my $hap_ref = $self->{w_hap};
my $mm = $self->{alleles_number};
# the first element is considered as zero. The first modification
# is consider as one and so on.
my $length = @{ @$hap_ref[0]}; #length of the haplotype
for (my $c = 0; $c<$length;$c++){
my @al=();
for my $r (0..$#$hap_ref){
push @al,$hap_ref->[$r][$c]
unless _is_there(\@al,\$hap_ref->[$r][$c]);
$hap_ref->[$r][$c] = get_position(\@al,\$hap_ref->[$r][$c]);
}
}
}
#------------------------
=head2 _snp_type_code
#------------------------
Title : _snp_type_code
Usage :
Function:
we have to create the snp type code for each version.
The way the snp type is created is the following:
we take the number value for every SNP and do the
following calculation
let be a SNP set as follow:
0 0
1 1
1 2
and multiplicity 3
on this case the situation is:
sum (value * multiplicity ^ position) for each SNP
0 * 3 ^ 0 + 1 * 3 ^ 1 + 1 * 3 ^ 2 = 12
0 * 3 ^ 0 + 1 * 3 ^ 1 + 2 * 3 ^ 2 = 21
Returns : nothing
Args : $self
Status : private
=cut
#------------------------
sub _snp_type_code{
#------------------------
my $self = shift;
my $hap = $self->{w_hap};
my $arr = $self->{snp_type_code};
my $al = $self->{alleles_number};
my $length = @{ $hap->[0]}; #length of the haplotype
for (my $c=0; $c<$length; $c++){
for my $r (0..$#$hap){
$arr->[$c] += $hap->[$r][$c] * $al ** $r;
}
}
}
#################################################
# return the position of an element in one array
# The element is always present on the array
#################################################
#------------------------
sub get_position{
#------------------------
my($array, $value)=@_;
for my $i(0..$#$array) {
if ($array->[$i] eq $$value){
return $i;
}
}
}
=head2 _alleles_number
Title : _alleles_number
Usage :
Function: calculate the max number of alleles for a haplotype and
if the number. For each SNP the number is stored and the
max number of alleles for a SNP on the set is returned
Returns : max number of alleles (a scalar storing a number)
Args : ref to AoA
Status : public
=cut
#------------------------
sub _alleles_number{
#------------------------
my $self = shift;
my $hap_ref = $self->{w_hap}; # working haplotype
my $length = @{ @$hap_ref[0]}; # length of the haplotype
for (my $c = 0; $c<$length;$c++){
my %alleles=();
for my $r (0..$#$hap_ref){
$alleles{ $hap_ref->[$r][$c] } =1; # new key for every new snp
}
# if the number of alleles for this column is
# greater than before set $m value as allele number
if ($self->{alleles_number} < keys %alleles) {
$self->{alleles_number} = keys %alleles;
}
}
}
=head2 _htSNP
Title : _htSNP
Usage : _htSNP()
Function: calculate the minimal set that contains all information of the
haplotype.
Returns : nonthing
Args : ref to an AoA and a ref to an array
Status : internal
=cut
#------------------------
sub _htSNP{
#------------------------
my $self = shift;
my $hap = $self->{'w_hap'};
my $type = $self->{'snp_type_code'};
my $set = $self->{'ht_type'};
my $out = []; # store the minimal set
my $nc=0; # new column for the output values
# pass for every value of the snp_type_code
for my $c (0..$#$type){
my $exist =0;
# every new value (not present) is pushed into set
if ( ! _is_there( $set,\$type->[$c] ) ){
push @$set, $type->[$c];
$exist =1;
for my $r(0..$#$hap){
#save value of the snp for every SNP
$out->[$r][$nc]= $hap->[$r][$c];
}
}
if ($exist){ $nc++ };
}
@$hap = @{dclone $out};
}
=head2 _snp_and_code_summary
Title : _snp_and_code_summary
Usage : _snp_and_code_summary()
Function: compile on a list all SNP and the code for each. This
information can be also obtained combining snp_type and
snp_type_code but on these results the information about
the rest of SNP's are not compiled as table.
0 will be silent SNPs
-1 are degenerated SNPs
and the rest of positive values are the code for useful SNP
Returns : nonthing
Args : ref to an AoA and a ref to an array
Status : internal
=cut
#------------------------
sub _snp_and_code_summary{
#------------------------
my $self = shift;
my $snp_type_code = $self->{'snp_type_code'};
my $useful_snp = $self->{'snp_type'}->{'useful_snp'};
my $silent_snp = $self->{'snp_type'}->{'silent_snp'};
my $deg_snp = $self->{'snp_type'}->{'deg_snp'};
my $snp_ids = $self->{'snp_ids'};
my $snp_and_code = $self->{'snp_and_code'};
# walk all SNP's and generate code for each
# do a practical thing. Consider all snp silent
foreach my $i (0..$#$snp_ids){
# assign zero to silent
my $value=0;
# active SNPs
foreach my $j (0..$#$useful_snp){
if ($snp_ids->[$i] eq $useful_snp->[$j]){
$value = $snp_type_code->[$j];
last;
}
}
# assign -1 to degenerated
foreach my $j (0..$#$deg_snp){
if ($snp_ids->[$i] eq $deg_snp->[$j]){
$value = -1;
last;
}
}
push @$snp_and_code, [$snp_ids->[$i], $value];
}
}
=head1 APPENDIX
=head2 How the process is working with one example
let's begin with one general example of the code.
Input haplotype:
acgtcca-t
cggtagtgc
cccccgtgc
cgctcgtgc
the first thing to to is to B<split the haplotype> into characters.
a c g t c c a - t
c g g t a g t g c
c c c c c g t g c
c g c t c g t g c
Now we have to B<convert> the haplotype to B<Upercase>. This
will produce the same SNP if we have input a or A.
A C G T C C A - T
C G G T A G T G C
C C C C C G T G C
C G C T C G T G C
The program admit as values any combination of ACTG and - (deletions).
The haplotype is B<converted to number>, considering the first variation
as zero and the alternate value as 1 (see expanded description below).
0 0 0 0 0 0 0 0 0
1 1 0 0 1 1 1 1 1
1 0 1 1 0 1 1 1 1
1 1 1 0 0 1 1 1 1
Once we have the haplotype converted to numbers we have to generate the
snp type information for the haplotype.
=over 4
B<SNP code = SUM ( value * multiplicity ^ position );>
=back
where:
SUM is the sum of the values for the SNP
value is the SNP number code (0 [generally for the mayor allele],
1 [for the minor allele].
position is the position on the block.
For this example the code is:
0 0 0 0 0 0 0 0 0
1 1 0 0 1 1 1 1 1
1 0 1 1 0 1 1 1 1
1 1 1 0 0 1 1 1 1
------------------------------------------------------------------
14 10 12 4 2 14 14 14 14
14 = 0*2^0 + 1*2^1 + 1*2^2 + 1*2^3
12 = 0*2^0 + 1*2^1 + 0*2^2 + 1*2^3
....
Once we have the families classify. We will B<take> just the SNP's B<not
redundant>.
14 10 12 4 2
This information will be B<passed to the tag module> is you want to tag
the htSNP.
Whatever it happens to one SNPs of a class will happen to a SNP of
the same class. Therefore you don't need to scan redundancies
=head2 Working with fuzzy data.
This module is designed to work with fuzzy data. As the source of the
haplotype is diverse. The program assume that some haplotypes can be
generated using different values. If there is any indetermination (? or n)
or any other degenerated value or invalid. The program will take away
This SNP and will leave that for a further analysis.
On a complex situation:
a c g t ? c a c t
a c g t ? c a - t
c g ? t a g ? g c
c a c t c g t g c
c g c t c g t g c
c g g t a g ? g c
a c ? t ? c a c t
On this haplotype everything is happening. We have a multialelic variance.
We have indeterminations. We have deletions and we have even one SNP
which is not a real SNP.
The buiding process will be the same on this situation.
Convert the haplotype to uppercase.
A C G T ? C A C T
A C G T ? C A - T
C G ? T A G ? G C
C A C T C G T G C
C G C T C G T G C
C G G T A G ? G C
A C ? T ? C A C T
All columns that present indeterminations will be removed from the analysis
on this Step.
hapotype after remove columns:
A C T C C T
A C T C - T
C G T G G C
C A T G G C
C G T G G C
C G T G G C
A C T C C T
All changes made on the haplotype matrix, will be also made on the SNP list.
snp_id_1 snp_id_2 snp_id_4 snp_id_6 snp_id_8 snp_id_9
now the SNP that is not one SNP will be removed from the analysis.
SNP with Id snp_id_4 (the one with all T's).
because of the removing. Some of the families will become the same and will
be clustered. A posteriori analysis will diference these families.
but because of the indetermination can not be distinguish.
A C C C T
A C C - T
C G G G C
C A G G C
C G G G C
C G G G C
A C C C T
The result of the mergering will go like:
A C C C T
A C C - T
C G G G C
C A G G C
Once again the changes made on the families and we merge the frequency (I<to be
implemented>)
Before to convert the haplotype into numbers we consider how many variations
we have on the set. On this case the variations are 3.
The control code will use on this situation base three as mutiplicity
0 0 0 0 0
0 0 0 1 0
1 1 1 2 1
1 2 1 2 1
-----------------------------------
36 63 36 75 36
And the minimal set for this combination is
0 0 0
0 0 1
1 1 2
1 2 2
B<NOTE:> this second example is a remote example an on normal conditions. This
conditions makes no sense, but as the haplotypes, can come from many sources
we have to be ready for all kind of combinations.
=cut
1;
]]>