HappyDoc Generated Documentation Class: Matrix

mabslib / Matrix.py / Matrix 

represents a binding site matrix

Methods   
  drawWeights 
drawWeights ( self )

draws the matrix

  positiveWeights 
positiveWeights ( self )

Update weights for higher order markov background.

Update weights only for positive probability. Background is taken care elsewhere

  setBG 
setBG ( self,  seq )

Sets the 0-order background frequences from and example sequence.

  getName 
getName ( self )

Gives the name of this matrix

  draw 
draw ( self )

draws the matrix

  setBGfreq 
setBGfreq (
        self,
        a,
        c,
        g,
        t,
        )

Sets 0-order background.

The parameters are frequences of a,c,g and t in the background sequence

  __init__ 
__init__ ( self,  filename )

reads matrix from file

  setPseudoCount 
setPseudoCount ( self,  pcount=1.0 )

Sets the amount of pseudocount

  initWeights 
initWeights ( self )

Helper to initialize the matrix weights for 0- or higher order background models

  setMarkovBackground 
setMarkovBackground ( self,  bg )

Set a markov Background.

Markov background of k-order is represented as counts of (k+1)-grams in the background sequence

  match 
match ( self,  sequence )

matches matrix on sequence DOES NOT CURRENTLY WORK

  getTFBSbyAbsolute 
getTFBSbyAbsolute (
        self,
        sequence,
        cutoff,
        )

Returns the hits that are better than cutoff

  trivialWeights 
trivialWeights ( self )

Update weights according to 0-order background

  __len__ 
__len__ ( self )

Return the number of columns in this matrix

  getTFBSbyRatio 
getTFBSbyRatio (
        self,
        sequence,
        minscore_percent=0.1,
        )

returns the hits, which are better then log2(minscore_percent*maxprod)

These are possible Transcription Factor Binding Sites. The formula is equal to log2(minscore_percent)+maxscore


This document was automatically generated Thu Feb 19 15:16:17 2004 by HappyDoc version 3.0.a1