class Bio::ContingencyTable
bio/util/contingency_table.rb  Statistical contingency table analysis for aligned sequences
 Author

Trevor Wennblom <trevor@corevx.com>
 Copyright

Copyright © 20052007 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 multiplesequence alignment. 0.0 indicates no correlation, 1.0 is the maximum correlation.
Further Reading¶ ↑

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 fivestring 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.
Attributes
Public Class Methods
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
# File lib/bio/util/contingency_table.rb 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
Report the chi square of the entire table
Arguments

none
 Returns

Float
chi square value
# File lib/bio/util/contingency_table.rb 332 def chi_square 333 total = 0 334 @characters.each do i # Loop through every row in the ContingencyTable 335 @characters.each do j # Loop through every column in the ContingencyTable 336 total += chi_square_element(i, j) 337 end 338 end 339 total 340 end
Report the chisquare relation of two elements in the table
Arguments

i
: row 
j
: column
 Returns

Float
chisquare of an intersection
# File lib/bio/util/contingency_table.rb 349 def chi_square_element(i, j) 350 eij = expected(i, j) 351 return 0 if eij == 0 352 ( @table[i][j]  eij )**2 / eij 353 end
Report the sum of all values in a given column
Arguments

j
: Column to sum
 Returns

Integer
sum of column
# File lib/bio/util/contingency_table.rb 280 def column_sum(j) 281 total = 0 282 @table.each { row_key, column total += column[j] } 283 total 284 end
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
# File lib/bio/util/contingency_table.rb 294 def column_sum_all 295 total = 0 296 @characters.each { j total += column_sum(j) } 297 total 298 end
Report the contingency coefficient of the table
Arguments

none
 Returns

Float
contingency_coefficient
of the table
# File lib/bio/util/contingency_table.rb 361 def contingency_coefficient 362 c_s = chi_square 363 Math.sqrt(c_s / (table_sum_all + c_s) ) 364 end
Calculate e, the expected value.
Arguments

i
: row 
j
: column
 Returns

Float
e(sub:ij) = (r(sub:i)/N) * (c(sub:j))
# File lib/bio/util/contingency_table.rb 322 def expected(i, j) 323 (row_sum(i).to_f / table_sum_all) * column_sum(j) 324 end
Report the sum of all values in a given row
Arguments

i
: Row to sum
 Returns

Integer
sum of row
# File lib/bio/util/contingency_table.rb 268 def row_sum(i) 269 total = 0 270 @table[i].each { k, v total += v } 271 total 272 end
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
# File lib/bio/util/contingency_table.rb 308 def row_sum_all 309 total = 0 310 @characters.each { i total += row_sum(i) } 311 total 312 end