Class | Bio::ContingencyTable
In: | lib/bio/util/contingency_table.rb
Parent: | Object

bio/util/contingency_table.rb - Statistical contingency table analysis for aligned sequences

Author: | Trevor Wennblom <trevor@corevx.com>
Copyright: | Copyright (c) 2005-2007 Midwinter Laboratories, LLC (midwinterlabs.com)
License: | The Ruby License

# Description

The Bio::ContingencyTable class provides basic statistical contingency table analysis for two positions within aligned sequences.

When ContingencyTable is instantiated the set of characters in the aligned sequences may be passed to it as an array. This is important since it uses these characters to create the table‘s rows and columns. If this array is not passed it will use it‘s default of an amino acid and nucleotide alphabet in lowercase along with the clustal spacer ’-’.

To get data from the table the most used functions will be chi_square and contingency_coefficient:

      ctable = Bio::ContingencyTable.new()
      ctable['a']['t'] += 1
      # .. put more values into the table
      puts ctable.chi_square
      puts ctable.contingency_coefficient  # between 0.0 and 1.0

The contingency_coefficient represents the degree of correlation of change between two sequence positions in a multiple-sequence alignment. 0.0 indicates no correlation, 1.0 is the maximum correlation.

# Further Reading

- en.wikipedia.org/wiki/Contingency_table
- www.physics.csbsju.edu/stats/exact.details.html
- Numerical Recipes in C by Press, Flannery, Teukolsky, and Vetterling

# Usage

What follows is an example of ContingencyTable in typical usage analyzing results from a clustal alignment.

      require 'bio'

      seqs = {}
      max_length = 0
      Bio::ClustalW::Report.new( IO.read('sample.aln') ).to_a.each do |entry|
        data = entry.data.strip
        seqs[entry.definition] = data.downcase
        max_length = data.size if max_length == 0
        raise "Aligned sequences must be the same length!" unless data.size == max_length
      end

      VERBOSE = true
      puts "i\tj\tchi_square\tcontingency_coefficient" if VERBOSE
      correlations = {}

      0.upto(max_length - 1) do |i|
        (i+1).upto(max_length - 1) do |j|
          ctable = Bio::ContingencyTable.new()
          seqs.each_value { |seq| ctable.table[ seq[i].chr ][ seq[j].chr ] += 1 }

          chi_square = ctable.chi_square
          contingency_coefficient = ctable.contingency_coefficient
          puts [(i+1), (j+1), chi_square, contingency_coefficient].join("\t") if VERBOSE

          correlations["#{i+1},#{j+1}"] = contingency_coefficient
          correlations["#{j+1},#{i+1}"] = contingency_coefficient  # Both ways are accurate
        end
      end

      require 'yaml'
      File.new('results.yml', 'a+') { |f| f.puts correlations.to_yaml }

# Tutorial

ContingencyTable returns the statistical significance of change between two positions in an alignment. If you would like to see how every possible combination of positions in your alignment compares to one another you must set this up yourself. Hopefully the provided examples will help you get started without too much trouble.

      def lite_example(sequences, max_length, characters)

        %w{i j chi_square contingency_coefficient}.each { |x| print x.ljust(12) }
        puts

        0.upto(max_length - 1) do |i|
          (i+1).upto(max_length - 1) do |j|
            ctable = Bio::ContingencyTable.new( characters )
            sequences.each do |seq|
              i_char = seq[i].chr
              j_char = seq[j].chr
              ctable.table[i_char][j_char] += 1
            end
            chi_square = ctable.chi_square
            contingency_coefficient = ctable.contingency_coefficient
            [(i+1), (j+1), chi_square, contingency_coefficient].each { |x| print x.to_s.ljust(12) }
            puts
          end
        end

      end

      allowed_letters = Array.new
      allowed_letters = 'abcdefghijk'.split('')

      seqs = Array.new
      seqs << 'abcde'
      seqs << 'abcde'
      seqs << 'aacje'
      seqs << 'aacae'

      length_of_every_sequence = seqs[0].size  # 5 letters long

      lite_example(seqs, length_of_every_sequence, allowed_letters)

Producing the following results:

      i           j           chi_square  contingency_coefficient
      1           2           0.0         0.0
      1           3           0.0         0.0
      1           4           0.0         0.0
      1           5           0.0         0.0
      2           3           0.0         0.0
      2           4           4.0         0.707106781186548
      2           5           0.0         0.0
      3           4           0.0         0.0
      3           5           0.0         0.0
      4           5           0.0         0.0

The position i=2 and j=4 has a high contingency coefficient indicating that the changes at these positions are related. Note that i and j are arbitrary, this could be represented as i=4 and j=2 since they both refer to position two and position four in the alignment. Here are some more examples:

      seqs = Array.new
      seqs << 'abcde'
      seqs << 'abcde'
      seqs << 'aacje'
      seqs << 'aacae'
      seqs << 'akcfe'
      seqs << 'akcfe'

      length_of_every_sequence = seqs[0].size  # 5 letters long

      lite_example(seqs, length_of_every_sequence, allowed_letters)

Results:

      i           j           chi_square  contingency_coefficient
      1           2           0.0         0.0
      1           3           0.0         0.0
      1           4           0.0         0.0
      1           5           0.0         0.0
      2           3           0.0         0.0
      2           4           12.0        0.816496580927726
      2           5           0.0         0.0
      3           4           0.0         0.0
      3           5           0.0         0.0
      4           5           0.0         0.0

Here we can see that the strength of the correlation of change has increased when more data is added with correlated changes at the same positions.

      seqs = Array.new
      seqs << 'abcde'
      seqs << 'abcde'
      seqs << 'kacje'  # changed first letter
      seqs << 'aacae'
      seqs << 'akcfa'  # changed last letter
      seqs << 'akcfe'

      length_of_every_sequence = seqs[0].size  # 5 letters long

      lite_example(seqs, length_of_every_sequence, allowed_letters)

Results:

      i           j           chi_square  contingency_coefficient
      1           2           2.4         0.534522483824849
      1           3           0.0         0.0
      1           4           6.0         0.707106781186548
      1           5           0.24        0.196116135138184
      2           3           0.0         0.0
      2           4           12.0        0.816496580927726
      2           5           2.4         0.534522483824849
      3           4           0.0         0.0
      3           5           0.0         0.0
      4           5           2.4         0.534522483824849

With random changes it becomes more difficult to identify correlated changes, yet positions two and four still have the highest correlation as indicated by the contingency coefficient. The best way to improve the accuracy of your results, as is often the case with statistics, is to increase the sample size.

# A Note on Efficiency

ContingencyTable is slow. It involves many calculations for even a seemingly small five-string data set. Even worse, it‘s very dependent on matrix traversal, and this is done with two dimensional hashes which dashes any hope of decent speed.

Finally, half of the matrix is redundant and positions could be summed with their companion position to reduce calculations. For example the positions (5,2) and (2,5) could both have their values added together and just stored in (2,5) while (5,2) could be an illegal position. Also, positions (1,1), (2,2), (3,3), etc. will never be used.

The purpose of this package is flexibility and education. The code is short and to the point in aims of achieving that purpose. If the BioRuby project moves towards C extensions in the future a professional caliber version will likely be created.

### Methods

chi_square chi_square_element column_sum column_sum_all contingency_coefficient expected new row_sum row_sum_all table_sum_all

### Attributes

characters | [R] |
table | [RW] | Since we‘re making this math-notation friendly here is the layout of @table: @table[row][column] @table[i][j] @table[y][x]

### Public Class methods

new(characters_in_sequences = nil)

Create a ContingencyTable that has characters_in_sequence.size rows and characters_in_sequence.size columns for each row

Arguments

- `characters_in_sequences`: (optional) The allowable characters that will be present in the aligned sequences.

Returns: | `ContingencyTable` object to be filled with values and calculated upon

[Source]

         # File lib/bio/util/contingency_table.rb, line 256
    256:   def initialize(characters_in_sequences = nil)
    257:     @characters = ( characters_in_sequences or %w{a c d e f g h i k l m n p q r s t v w y - x u} )
    258:     tmp = Hash[*@characters.collect { |v| [v, 0] }.flatten]
    259:     @table = Hash[*@characters.collect { |v| [v, tmp.dup] }.flatten]
    260:   end

### Public Instance methods

chi_square()

Report the chi square of the entire table

Arguments

- none

Returns: | `Float` chi square value

[Source]

         # File lib/bio/util/contingency_table.rb, line 332
    332:   def chi_square
    333:     total = 0
    334:     c = @characters
    335:     max = c.size - 1
    336:     @characters.each do |i|    # Loop through every row in the ContingencyTable
    337:       @characters.each do |j|  # Loop through every column in the ContingencyTable
    338:         total += chi_square_element(i, j)
    339:       end
    340:     end
    341:     total
    342:   end

chi_square_element(i, j)

Report the chi-square relation of two elements in the table

Arguments

- `i`: row
- `j`: column

Returns: | `Float` chi-square of an intersection

[Source]

         # File lib/bio/util/contingency_table.rb, line 351
    351:   def chi_square_element(i, j)
    352:     eij = expected(i, j)
    353:     return 0 if eij == 0
    354:     ( @table[i][j] - eij )**2 / eij
    355:   end

column_sum(j)

Report the sum of all values in a given column

Arguments

- `j`: Column to sum

Returns: | `Integer` sum of column

[Source]

         # File lib/bio/util/contingency_table.rb, line 280
    280:   def column_sum(j)
    281:     total = 0
    282:     @table.each { |row_key, column| total += column[j] }
    283:     total
    284:   end

column_sum_all()

Report the sum of all values in all columns.

- This is the same thing as asking for the sum of all values in the table.

Arguments

- none

Returns: | `Integer` sum of all columns

[Source]

         # File lib/bio/util/contingency_table.rb, line 294
    294:   def column_sum_all
    295:     total = 0
    296:     @characters.each { |j| total += column_sum(j) }
    297:     total
    298:   end

contingency_coefficient()

Report the contingency coefficient of the table

Arguments

- none

Returns: | `Float` contingency_coefficient of the table

[Source]

         # File lib/bio/util/contingency_table.rb, line 363
    363:   def contingency_coefficient
    364:     c_s = chi_square
    365:     Math.sqrt(c_s / (table_sum_all + c_s) )
    366:   end

expected(i, j)

Calculate e, the expected value.

Arguments

- `i`: row
- `j`: column

Returns: | `Float` e(sub:ij) = (r(sub:i)/N) * (c(sub:j))

[Source]

         # File lib/bio/util/contingency_table.rb, line 322
    322:   def expected(i, j)
    323:     (row_sum(i).to_f / table_sum_all) * column_sum(j)
    324:   end

row_sum(i)

Report the sum of all values in a given row

Arguments

- `i`: Row to sum

Returns: | `Integer` sum of row

[Source]

         # File lib/bio/util/contingency_table.rb, line 268
    268:   def row_sum(i)
    269:     total = 0
    270:     @table[i].each { |k, v| total += v }
    271:     total
    272:   end

row_sum_all()

Report the sum of all values in all rows.

- This is the same thing as asking for the sum of all values in the table.

Arguments

- none

Returns: | `Integer` sum of all rows

[Source]

         # File lib/bio/util/contingency_table.rb, line 308
    308:   def row_sum_all
    309:     total = 0
    310:     @characters.each { |i| total += row_sum(i) }
    311:     total
    312:   end

table_sum_all()

Alias for row_sum_all

[Validate]