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Discretization of time series gene expression microarray data
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3.4 years ago
Andrea • 40
Sweden

Supposed that we are trying to infer the underlying gene expression network from time series gene expression microarray data. Data from Microarrays are used and our tool is the Bayesian network toolbox in Matlab. There are algorithms such as REVEAL that cannt handle continuous values , so we want to discretize our dataset before analyzing. A natural choise is to use three differnt steps: one for down regulated, one for uncharged and one for down regulated. Αnd here is my question: Is it any specific criterion that we have to consider ?

Just separating the values like this way doesnt seem me right. Any help pleaze??

  1. x<-1=-1 for down regulated
  2. x between -1 and 1 = 0 for uncharged
  3. x>1=1 for up regulated

1.0000 -1.1000 -1.2000 -1.2900 0.1000 1.2600 -0.0200

    0.8400   -1.1800   -0.7900   -0.9400   -0.0400    0.6900    0.5300
    1.0000   -0.4200   -2.0000   -1.1800   -0.6400    1.1900    0.1000
    0.5700   -1.4700   -1.6400   -1.2500    0.4800    1.0500   -0.1800
   -0.0900   -1.8400   -1.9000   -2.2700   -1.6400    1.1700    0.4900

1     1     1     1     1     1     0     1    -1    -1    -1     0
 0     0     1     1     1     1     0     0    -1     0     0     0
 1     1     1     1     1     1     1     1     0    -1    -1     0
 1     1     1     1     1     0     0     0    -1    -1    -1     0
 0     1     1     1     1     1     0     0    -1    -1    -1    -1
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