diff filter_by_index_gamma @ 0:6411ca16916e default tip

initial commit

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/filter_by_index_gamma	Wed Mar 25 13:23:29 2015 -0600
@@ -0,0 +1,492 @@
+#!/usr/bin/env perl
+
+use strict;
+use warnings;
+use DB_File;
+use Parse::BooleanLogic;
+use Math::CDF qw(pgamma qgamma); # relevance score -> gamma p-value
+use PDL qw(pdl);
+use PDL::Stats::Distr qw(mme_gamma); # gamma dist parameter estimates
+use vars qw($parser %cached_sentences %sentence_index);
+
+my $quiet = 0;
+if(@ARGV and $ARGV[0] =~ /^-q/){
+  $quiet = 1;
+  shift @ARGV;
+}
+
+@ARGV == 5 or die "Usage: $0 [-q(uiet)] <index filename base> <db name> <hgvs_annotated.txt> <output.txt> <query>\nWhere query has the format \"this or that\", \"this and that\", etc.\n";
+
+my $signal_p = 0.95; # signal is top 5% of scores
+my $index_filename_base = shift @ARGV;
+my $db_name = shift @ARGV;
+my $hgvs_file = shift @ARGV;
+my $out_file = shift @ARGV;
+my $orig_query = shift @ARGV;
+
+$parser = new Parse::BooleanLogic(operators => ['and', 'or']);
+my $query_tree = $parser->as_array($orig_query, error_cb => sub {die "Could not parse query: @_\n"});
+# For simplicity, turn the tree into a base set of or statements (which means expanding "A and (B or C)" into "A and B or A and C") a.k.a. "sum of products/minterms"
+my @query_terms = flatten_query($query_tree);
+
+my $df_index_filename = $index_filename_base."df_index";
+my %df_index;
+my $df_index_handle = tie %df_index, "DB_File", $df_index_filename, O_RDONLY, 0400, $DB_BTREE 
+			or die "Cannot open $df_index_filename: $!\n";
+my $gene_record_count = $df_index{"__DOC_COUNT__"};
+
+my $sentence_index_filename = $index_filename_base."sentence_index";
+my $sentence_index_handle = tie %sentence_index, "DB_File", $sentence_index_filename, O_RDONLY, 0400, $DB_HASH
+			or die "Cannot open $sentence_index_filename: $!\n";
+
+# Get the list of gene symbols we'll need
+open(HGVS, $hgvs_file)
+  or die "Cannot open $hgvs_file for reading: $!\n";
+my $header = <HGVS>;
+chomp $header;
+my @header_columns = split /\t/, $header;
+my ($gene_name_column, $chr_column, $from_column, $to_column);
+for(my $i = 0; $i <= $#header_columns; $i++){
+  if($header_columns[$i] eq "Gene Name"){
+    $gene_name_column = $i;
+  }
+  elsif($header_columns[$i] eq "Chr"){
+    $chr_column = $i;
+  }
+  elsif($header_columns[$i] eq "DNA From"){
+    $from_column = $i;
+  }
+  elsif($header_columns[$i] eq "DNA To"){
+    $to_column = $i;
+  }
+}
+my $blank_query = not @query_terms;
+# Special case of empty query means print all info for variant ranges listed in the input HGVS file (assuming the DB was indexed to include chr:pos keys)
+if($blank_query){
+  #print STDERR "Running blank query\n";
+  if(not defined $chr_column){
+    die "Could not find 'Chr' column in the input header, aborting\n";
+  }
+  if(not defined $from_column){
+    die "Could not find 'DNA From' column in the input header, aborting\n";
+  }
+  if(not defined $to_column){
+    die "Could not find 'DNA To' column in the input header, aborting\n";
+  }
+  # Build the list of locations that will need to be searched in the index
+
+  open(OUT, ">$out_file")
+    or die "Cannot open $out_file for writing: $!\n";
+  print OUT $header, "\t$db_name Text Matches\n";
+
+  while(<HGVS>){
+    chomp;
+    my @F = split /\t/, $_, -1;
+    my @pos_data;
+    for my $pos ($F[$from_column]..$F[$to_column]){ # for each position in the range
+      my $pos_match_data = fetch_sentence("$F[$chr_column]:$pos", -1); # fetch all data for this position
+      push @pos_data, "*$F[$chr_column]:$pos* ".$pos_match_data if defined $pos_match_data;
+    }
+    print OUT join("\t", @F, join(" // ", @pos_data)),"\n";
+  }
+  close(OUT);
+  exit;
+}
+elsif(not defined $gene_name_column){
+  die "Could not find 'Gene Name' column in the input header, aborting\n"; 
+}
+#print STDERR "Query terms: " , scalar(@query_terms), "\n";
+my %gene_to_query_match_ranges;
+# Determine the set of genes that might match the query, based on the word index
+for my $query_term (@query_terms){
+  #print STDERR "Query term $query_term\n";
+  my %doc_hits; # how many needed words match the document? 
+  my $contiguous = 1; #by default multiword queries must be contiguous
+  # Unless it's an AND query
+  if($query_term =~ s/ and / /g){
+    $contiguous = 0;
+  }
+
+  my @words = split /\s+/, $query_term; # can be multi-word term like "mental retardation"
+  for(my $i = 0; $i <= $#words; $i++){
+    my $word = mc($words[$i]); # can be a stem word, like hypoton 
+    #print STDERR "Checking word $word...";
+    if($i == 0){
+      my $first_word_docs = get_doc_offsets($df_index_handle, $word); # get all words' docs off this stem
+      #print STDERR scalar(keys %$first_word_docs), " documents found\n";
+      for my $doc (keys %$first_word_docs){
+        $doc_hits{$doc} = $first_word_docs->{$doc}; # populate initial hit list that'll be whittled down in subsequent outer loops of multiword phrase members
+      }
+      next;
+    }
+    my @candidate_docs = keys %doc_hits;
+    last if not @candidate_docs; # short circuit searches guaranteed to fail
+
+    # each additional word must directly follow an existing match
+    my $word_doc_offsets_ref = get_doc_offsets($df_index_handle, $word); # get all words' docs off this stem
+    #print STDERR scalar(keys %$word_doc_offsets_ref), " documents found\n";
+    for my $doc (@candidate_docs){
+      my $num_matches = 0;
+      if(not exists $word_doc_offsets_ref->{$doc}){ # required word missing, eliminate doc from consideration
+        delete $doc_hits{$doc};
+        next;
+      }
+      # see if any of the instances of the additional words directly follow the last word we successfully matched
+      my $so_far_matches_ref = $doc_hits{$doc};
+      my $next_word_matches_ref = $word_doc_offsets_ref->{$doc};
+      for (my $j=0; $j <= $#{$so_far_matches_ref}; $j++){
+        my $existing_match_extended = 0;
+        next unless defined $so_far_matches_ref->[$j]->[2]; # every once in a while there is no article id parsed
+        for (my $k=0; $k <= $#{$next_word_matches_ref}; $k++){
+          # Same article?
+          next unless defined $next_word_matches_ref->[$k]->[2] and $next_word_matches_ref->[$k]->[2] eq $so_far_matches_ref->[$j]->[2];
+          if(not $contiguous){
+            $so_far_matches_ref->[$j]->[4] .= " AND ".$next_word_matches_ref->[$k]->[4]; # update the matched term to include the extension too 
+            if(ref $so_far_matches_ref->[$j]->[3] ne "ARRAY"){ # match does not yet span multiple sentences
+              last if $next_word_matches_ref->[$k]->[3] == $so_far_matches_ref->[$j]->[3]; # same sentence
+              $so_far_matches_ref->[$j]->[3] = [$so_far_matches_ref->[$j]->[3], $next_word_matches_ref->[$k]->[3]]; # change from scalar to array (of sentence numbers)
+            }
+            elsif(not grep {$_ eq $next_word_matches_ref->[$k]->[3]} @{$so_far_matches_ref->[$j]->[3]}){
+              push @{$so_far_matches_ref->[$j]->[3]}, $next_word_matches_ref->[$k]->[3]; # add top spanning sentences list of not already there
+            }
+          }
+          # else contiguous word occurences required. 
+          # Same sentence?
+          next unless $next_word_matches_ref->[$k]->[3] == $so_far_matches_ref->[$j]->[3];
+
+          my $space_between_match_words = $next_word_matches_ref->[$k]->[0] - $so_far_matches_ref->[$j]->[1];
+          if($space_between_match_words <= 2){
+            $existing_match_extended = 1;
+            $so_far_matches_ref->[$j]->[1] = $next_word_matches_ref->[$k]->[1]; # move the match cursor to include the new extending word
+            $so_far_matches_ref->[$j]->[4] .= " ".$next_word_matches_ref->[$k]->[4]; # update the matched term to include the extension too
+            last;
+          }
+          elsif($space_between_match_words > 2){ # more than two typographical symbols between words, consider non-continuous
+            last;  # since the offsets are in order, any further k would only yield a larger spacing, so shortcircuit
+          }
+        }
+        if(not $existing_match_extended){
+          splice(@$so_far_matches_ref, $j, 1);
+          $j--;
+        }
+        else{
+          $num_matches++;
+        }
+      }
+      if(not $num_matches){
+        delete $doc_hits{$doc};
+      }
+    }
+  }
+  # the only keys that get to this point should be those that match all terms
+  for my $doc (keys %doc_hits){
+    $gene_to_query_match_ranges{$doc} = [] if not exists $gene_to_query_match_ranges{$doc};
+    push @{$gene_to_query_match_ranges{$doc}}, [$query_term, @{$doc_hits{$doc}}];
+  }
+}
+
+my @matched_genes = keys %gene_to_query_match_ranges;
+#print STDERR "Found ", scalar(@matched_genes), "/$gene_record_count records in cached iHOP matching the query\n" unless $quiet;
+my %query_gene_counts;
+my %ntf;
+for my $gene (keys %gene_to_query_match_ranges){
+  my $max_doc_word_count = $df_index{"__DOC_MAX_WC_$gene"};
+  for my $count_record (@{$gene_to_query_match_ranges{$gene}}){
+    my ($query_term, @query_term_match_ranges_in_this_gene) = @$count_record;
+    # next if $query_term eq $gene; # slightly controversial? exclude references to genes from the score if the gene is the record being talked about (obviously it will be highly scored)
+    # allows us to find first degree interactors (i.e. points for "A interacts with B", in the record describing A) without creating crazy high score for doc describing gene B if B was in the original query without any phenotype query terms
+    $query_gene_counts{$query_term}++;
+
+    $ntf{$gene} = {} unless exists $ntf{$gene};
+    # atypical use of log in order to weigh heavy use of a common term less than occasional use of a rare term
+    $ntf{$gene}->{$query_term} = log($#query_term_match_ranges_in_this_gene+2)/log($max_doc_word_count+1); 
+  }
+  #print STDERR "Doc max word count is $max_doc_word_count for $gene, ntf keys = ", keys %{$ntf{$gene}}, "\n";
+}
+
+my %idf;
+for my $query_term (@query_terms){ # convert %idf values from documents-with-the-query-term-count to actual IDF
+  next unless exists $query_gene_counts{$query_term}; # query not in the document collection
+  $idf{$query_term} = log($gene_record_count/$query_gene_counts{$query_term});
+  #print STDERR "$query_term IDF is $idf{$query_term}\n";
+}
+
+# Create a relevance score using a normalized term frequency - inverse document frequency summation
+my %relevance_score;
+my %matched_query_terms;
+for my $gene_symbol (keys %gene_to_query_match_ranges){
+  my $relevance_score = 0;
+  # Hmm, take average, sum or max of TF-IDFs? 
+  my $max_query_score = 0;
+  my @matched_query_terms;
+  my $query_score = 0;
+  for (my $i = 0; $i <= $#query_terms; $i++){
+    my $query_term = $query_terms[$i];
+    next unless exists $idf{$query_term};
+    next unless exists $ntf{$gene_symbol}->{$query_term};
+    $query_score += $ntf{$gene_symbol}->{$query_term}*$idf{$query_term};
+    push @matched_query_terms, $query_term;
+    $query_score *= 1-$i/scalar(@query_terms)/2 if scalar(@query_terms) > 2;# adjust the query score so the first terms are weighted more heavily if a bunch of terms are being searched
+    $max_query_score = $query_score if $query_score > $max_query_score;
+    $relevance_score += $query_score;
+  }
+  # this square root trick will not affect the score of a single term query, but will penalize a high total score that is comprised of a bunch of low value individual term scores)
+  $relevance_score{$gene_symbol} = sqrt($relevance_score*$max_query_score); 
+  #print STDERR "Relevance score for $gene_symbol is $relevance_score{$gene_symbol}\n";
+  $matched_query_terms{$gene_symbol} = \@matched_query_terms;
+}
+ 
+# Characterize relevance score as a gamma statistical distribution and convert to probability
+my $max_relevance_score = 0;
+for my $relevance_score (values %relevance_score){
+  $max_relevance_score = $relevance_score if $relevance_score > $max_relevance_score;
+}
+# Remove top end scores as signal, characterize the rest as noise.
+# Iterative estimation of gamma parameters and removing data within range where CDF>99%
+my $noise_data = pdl(values %relevance_score);
+my ($shape, $scale) = $noise_data->mme_gamma();
+#print STDERR "Initial gamma distribution estimates: $shape, $scale (max observation $max_relevance_score)\n";
+my $signal_cutoff = qgamma($signal_p, $shape, 1/$scale);
+my @noise_data;
+for my $gene_symbol (keys %relevance_score){
+  my $score = $relevance_score{$gene_symbol};
+  push @noise_data, $score if $score < $signal_cutoff;
+}
+$noise_data = pdl(@noise_data);
+($shape, $scale) = $noise_data->mme_gamma();
+#print STDERR "Revised gamma distribution estimates (noise estimate at $signal_cutoff (CDF $signal_p)): $shape, $scale\n";
+# Convert scores to probabilities
+for my $gene_symbol (keys %relevance_score){
+  $relevance_score{$gene_symbol} = 1-pgamma($relevance_score{$gene_symbol}, $shape, 1/$scale);
+}
+
+#TODO: create summary stats for each query term so the user gets an idea of each's contribution?
+
+my %pubmed_matches;
+for my $gene_symbol (keys %gene_to_query_match_ranges){
+  my $query_match_ranges_ref = $gene_to_query_match_ranges{$gene_symbol};
+  my %matching_sentences;
+  for my $count_record (@$query_match_ranges_ref){
+    my ($query_term, @query_term_match_ranges_in_this_gene) = @$count_record;  
+    for my $occ_info (@query_term_match_ranges_in_this_gene){ 
+      my $id = $occ_info->[2];
+      my $sentence_number = $occ_info->[3];
+      my $query_match_word = $occ_info->[4];
+      # Fetch the preparsed sentence from the sentence index based on id and sentence number
+      # Will automatically *HIGHLIGHT* the query terms fetched in the sentence over the course of this script
+      if(ref $sentence_number eq "ARRAY"){ # match spans multiple sentences
+        for my $s (@$sentence_number){
+          for my $word (split / AND /, $query_match_word){
+            #print STDERR "Highlighting $word in $id #$s for query term $query_term (multisentence match)\n";
+            $matching_sentences{fetch_sentence_key($id, $s, $word)}++;
+          }
+        }
+      }
+      else{ # single sentence match
+        #print STDERR "Highlighting $query_match_word in $id #$sentence_number for query term $query_term\n";
+        $matching_sentences{fetch_sentence_key($id, $sentence_number, $query_match_word)}++;
+      }
+    }
+  }
+  $gene_symbol =~ s/_/\//; # didn't have a forward slash in a gene name for disk caching purposes
+  if(keys %matching_sentences){
+    $pubmed_matches{$gene_symbol} = [] unless exists $pubmed_matches{$gene_symbol};
+    for my $new_match_ref (keys %matching_sentences){
+      push @{$pubmed_matches{$gene_symbol}}, $new_match_ref unless grep {$_ eq $new_match_ref} @{$pubmed_matches{$gene_symbol}}; # only put in new sentences, no need to dup
+    }
+  }
+}
+
+$orig_query =~ s/\s+/ /; # normalized whitespace
+$orig_query =~ s/ and / and /i; # lc()
+my @orig_query_terms = split /\s+or\s+/, $orig_query;
+
+open(OUT, ">$out_file")
+  or die "Cannot open $out_file for writing: $!\n";
+my $new_header = $header;
+$new_header .= "\t$db_name p-value (log normalized TF-IDF score, gamma dist)\t$db_name Matching Terms ($orig_query)\t$db_name Text Matches";
+print OUT $new_header, "\n";
+
+# Check if any of the variants in the annotated HGVS table are in genes from the OMIM match list
+while(<HGVS>){
+  chomp;
+  my @F = split /\t/, $_, -1;
+  # order the ids from highest number of sentence matches to lowest, from highest ranked term to least
+  my (%id2match_count, %id2sentences);
+  my @matched_genes;
+  my $relevance_score_final = 1;
+  my @matched_query_terms;
+  for my $gene_name (split /\s*;\s*/, $F[$gene_name_column]){
+    next unless exists $pubmed_matches{$gene_name};
+    push @matched_genes, $gene_name;
+    for my $sentence_ref (@{$pubmed_matches{$gene_name}}){ # 0 == always fetch the title which is stored in sentence index 0
+      my $pubmed_record = fetch_sentence($sentence_ref);
+      $id2match_count{$pubmed_record->[0]}++; # key = id
+      if(not exists $id2sentences{$pubmed_record->[0]}){
+        $id2sentences{$pubmed_record->[0]} = {};
+        my $title_record = fetch_sentence(fetch_sentence_key($pubmed_record->[0], 0, ""));
+        next unless $title_record->[0];
+        print STDERR "No $index_filename_base sentence number for ", $title_record->[0], "\n" if not defined $title_record->[1];
+        print STDERR "No $index_filename_base sentence text for ", $title_record->[0], " sentence #", $title_record->[1], "\n" if not defined $title_record->[2];
+        $id2sentences{$title_record->[0]}->{$title_record->[2]} = $title_record->[1];
+      }
+      # Only print sentences that match a query term other than the gene name for the record key, if that gene name is part of the query
+      my $non_self_query_ref = 0;
+      while($pubmed_record->[2] =~ /\*(.+?)\*/g){
+        if($1 ne $gene_name){
+          $non_self_query_ref = 1;
+          last;
+        }
+      }
+      #print STDERR "rejected $gene_name self-only sentence ",$pubmed_record->[2],"\n" unless $non_self_query_ref;
+      next unless $non_self_query_ref;
+      $id2sentences{$pubmed_record->[0]}->{$pubmed_record->[2]} = $pubmed_record->[1]; # value = sentence order within pubmed text
+    }
+    $relevance_score_final *= $relevance_score{$gene_name};
+    push @matched_query_terms, @{$matched_query_terms{$gene_name}};
+  }
+
+  # If we get here, there were matches
+  my @ordered_ids = sort {$id2match_count{$b} <=> $id2match_count{$a}} keys %id2match_count;
+
+  # print sentences in each id in order, with ellipsis if not contiguous
+  my %h;
+  print OUT join("\t", @F, ($relevance_score_final != 1 ? $relevance_score_final : ""), (@matched_query_terms ? join("; ", sort grep {not $h{$_}++} @matched_query_terms) : "")), "\t"; 
+  my $first_record = 1;
+  for my $id (@ordered_ids){
+    my $sentence2order = $id2sentences{$id};
+    my @ordered_sentences = sort {$sentence2order->{$a} <=> $sentence2order->{$b}} keys %$sentence2order;
+    next if scalar(@ordered_sentences) == 1; # due to self-gene only referencing filter above, we may have no matching sentences in a record.  Skip in this case.
+    if($first_record){
+      $first_record = 0;
+    }
+    else{
+      print OUT " // ";
+    }
+    my $title = shift(@ordered_sentences);
+    print OUT "$db_name $id",(defined $title ? " $title": ""),":"; # first sentence is always the record title
+    my $last_ordinal = 0;
+    for my $s (@ordered_sentences){
+      if($last_ordinal and $sentence2order->{$s} != $last_ordinal+1){
+        print OUT "..";
+      }
+      print OUT " ",$s;
+      $last_ordinal = $sentence2order->{$s};
+    }
+  }
+  print OUT "\n";
+}
+
+sub get_doc_offsets{
+  my ($db_handle, $word_stem) = @_;
+  my %doc2offsets;
+
+  my $is_uc = $word_stem =~ /^[A-Z0-9]+$/;
+  my $has_wildcard = $word_stem =~ s/\*$//;
+  my $value = 0;
+  my $cursor_key = $word_stem;
+  # retrieves the first 
+  for(my $status = $db_handle->seq($cursor_key, $value, R_CURSOR);
+      $status == 0;
+      $status = $db_handle->seq($cursor_key, $value, R_NEXT)){
+    if(CORE::index($cursor_key,$word_stem) != 0){
+      last; # outside the records that have the requested stem now
+    }
+    for my $record (split /\n/s, $value){
+      my ($doc, @occ_infos) = split /:/, $record;
+      $doc2offsets{$doc} = [] if not exists $doc2offsets{$doc};
+      for my $occ_info (@occ_infos){
+        my ($term_offset, $id, $sentence_number) = split /,/, $occ_info, -1;
+        # record start and end of word to facilitate partial key consecutive word matching algorithm used in this script
+        push @{$doc2offsets{$doc}}, [$term_offset, $term_offset+length($cursor_key), $id, $sentence_number, $cursor_key]; 
+      }
+    }
+    last if $is_uc and not $has_wildcard; # only exact matches for upper case words like gene names
+  }
+  return \%doc2offsets;
+}
+
+sub mc{
+  if($_[0] =~ /^[A-Z][a-z]+$/){
+    return lc($_[0]); # sentence case normalization to lower case for regular words
+  }
+  else{
+    return $_[0]; # as-is for gene names, etc
+  }
+}
+
+sub fetch_sentence_key{
+  my ($id, $sentence_number, $query_term) = @_;
+
+  $sentence_number = 0 if not defined $sentence_number;
+  return ":$sentence_number" if not $id;
+  my $key = "$id:$sentence_number";
+  if(not exists $cached_sentences{$key}){
+    my @sentences = split /\n/, $sentence_index{$id};
+    $cached_sentences{$key} = $sentences[$sentence_number];
+  }
+  $cached_sentences{$key} =~ s/\b\Q$query_term\E\b(?!\*)/"*".uc($query_term)."*"/ge unless $query_term eq "";
+  #print STDERR "Highlighted $query_term in $cached_sentences{$key}\n" if $query_term =~ /cirrhosis/;
+  return $key;
+}
+
+sub fetch_sentence{
+  if(@_ == 1){ # from cache
+    return [split(/:/, $_[0]), $cached_sentences{$_[0]}];
+  }
+  else{ # if more than one arg, DIRECT FROM index key as first arg, sentence # is second arg
+    return undef if not exists $sentence_index{$_[0]};
+    my @sentences = split /\n/, $sentence_index{$_[0]};
+    if($_[1] < 0){ # all sentences request
+      return join("; ", @sentences);
+    }
+    return $sentences[$_[1]];
+  }
+}
+
+
+# boolean operator tree to flat expanded single depth "or" op query
+sub flatten_query{
+  my $tree = shift @_;
+  my @or_queries;
+
+  # Base case: the tree is just a leaf (denoted by a hash reference). Return value of the operand it represents.
+  if(ref $tree eq "HASH"){
+    return ($tree->{"operand"});
+  }
+
+  elsif(not ref $tree){
+    return $tree;
+  }
+
+  # Otherwise it's an operation array
+  if(ref $tree ne "ARRAY"){
+    die "Could not parse $tree, logic error in the query parser\n";
+  }
+ 
+  # Deal with AND first since it has higher precedence
+  for (my $i = 1; $i < $#{$tree}; $i++){
+    if($tree->[$i] eq "and"){
+      my @expanded_term;
+      my @t1_terms = flatten_query($tree->[$i-1]);
+      my @t2_terms = flatten_query($tree->[$i+1]);
+      #print STDERR "need to expand ", $tree->[$i-1], "(@t1_terms) AND ", $tree->[$i+1], "(@t2_terms)\n";
+      for my $term1 (@t1_terms){
+        for my $term2 (@t2_terms){
+          #print STDERR "Expanding to $term1 and $term2\n";
+          push @expanded_term, "$term1 and $term2";
+        }
+      }
+      splice(@$tree, $i-1, 3, @expanded_term);
+      $i--; # list has been shortened
+    }
+  }
+  # Should be only "OR" ops left
+  # Resolve any OR subtrees
+  for(my $i = 0; $i <= $#{$tree}; $i++){
+    next if $tree->[$i] eq "or";
+    push @or_queries, flatten_query($tree->[$i]); # otherwise recursive parse
+  }
+
+  return @or_queries;
+}