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datamatrix_methods [2010/09/28 20:13]
mbleda
datamatrix_methods [2010/09/28 20:14] (current)
mbleda
Line 6: Line 6:
   * **Replicate handling** \\ Usually the data matrix contains replicated measures. For further analysis these replicated measurements have to be transformed into single measures. Here we present a module for merge replicates where the final value could be the average or the median.   * **Replicate handling** \\ Usually the data matrix contains replicated measures. For further analysis these replicated measurements have to be transformed into single measures. Here we present a module for merge replicates where the final value could be the average or the median.
  
-  * **Management of missing values** \\ * Filter missing values if the percentage of existing values is below a Minimum (%)+  * **Management of missing values** \\ 
 +     * Filter missing values if the percentage of existing values is below a Minimum (%)
      * Impute missing values and fill them with 0, row average, row median, or KNN impute method. KNN impute is a standard missing value imputation method that takes advantage of the correlation structure in microarray data by selecting genes with expression profiles similar to the gene of interest to impute missing values. The KNN method is relatively sensitive when K-value is in the range of 10-20 neighbours.      * Impute missing values and fill them with 0, row average, row median, or KNN impute method. KNN impute is a standard missing value imputation method that takes advantage of the correlation structure in microarray data by selecting genes with expression profiles similar to the gene of interest to impute missing values. The KNN method is relatively sensitive when K-value is in the range of 10-20 neighbours.
  
datamatrix_methods.txt · Last modified: 2010/09/28 20:14 by mbleda
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