Package Bio :: Package motifs :: Module matrix :: Class PositionSpecificScoringMatrix

Class PositionSpecificScoringMatrix

       object --+        
                |        
             dict --+    
                    |    
GenericPositionMatrix --+
                        |
                       PositionSpecificScoringMatrix

Instance Methods

[hide private]

calculate(self, sequence)
returns the PWM score for a given sequence for all positions.

source code

search(self, sequence, threshold=0.0, both=True)
a generator function, returning found hits in a given sequence with the pwm score higher than the threshold

source code

mean(self, background=None)
Expected value of the score of a motif.

source code

std(self, background=None)
Standard deviation of the score of a motif.

source code

dist_pearson(self, other)
return the similarity score based on pearson correlation for the given motif against self.

source code

dist_pearson_at(self, other, offset)

source code

distribution(self, background=None, precision=1000)
calculate the distribution of the scores at the given precision.

source code

Inherited from GenericPositionMatrix: __getitem__, __init__, __str__, reverse_complement

Inherited from dict: __cmp__, __contains__, __delitem__, __eq__, __ge__, __getattribute__, __gt__, __hash__, __iter__, __le__, __len__, __lt__, __ne__, __new__, __repr__, __setitem__, clear, copy, fromkeys, get, has_key, items, iteritems, iterkeys, itervalues, keys, pop, popitem, setdefault, update, values

Inherited from object: __delattr__, __reduce__, __reduce_ex__, __setattr__

Properties

[hide private]

max
Maximal possible score for this motif.

min
Minimal possible score for this motif.

Inherited from GenericPositionMatrix: anticonsensus, consensus, degenerate_consensus

Inherited from object: __class__

Method Details

[hide private]

calculate(self, sequence)

source code


returns the PWM score for a given sequence for all positions.

- the sequence can only be a DNA sequence
- the search is performed only on one strand
- if the sequence and the motif have the same length, a single
  number is returned
- otherwise, the result is a one-dimensional list or numpy array

dist_pearson(self, other)

source code


return the similarity score based on pearson correlation for the given motif against self.

We use the Pearson's correlation of the respective probabilities.

Property Details

[hide private]

max

Maximal possible score for this motif.

returns the score computed for the consensus sequence.

Get Method:: unreachable.max(self) - Maximal possible score for this motif.

min

Minimal possible score for this motif.

returns the score computed for the anticonsensus sequence.

Get Method:: unreachable.min(self) - Minimal possible score for this motif.