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in-silico_knockout:workflow_ko

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in-silico_knockout:workflow_ko [2016/12/23 02:54] ccubukin-silico_knockout:workflow_ko [2017/05/24 14:33] (current) – external edit 127.0.0.1
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 Uploading only 1 sapmle does not require design data and disables auto knockout. Uploading only 1 sapmle does not require design data and disables auto knockout.
 +Fold change of the module activities between conditions will be shown as results of this analysis.
  
 __**Using dataset (multiple samples):**__ __**Using dataset (multiple samples):**__
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 Using multiple samples lets user to calculate class probability (before and after KO/OE ) of selected  sample. Probabilities are calculated using Random Forest machine learning algorithm. For unbiased learning  Using multiple samples lets user to calculate class probability (before and after KO/OE ) of selected  sample. Probabilities are calculated using Random Forest machine learning algorithm. For unbiased learning 
 sample sizes between classes might be balanced and sample size should be good enough. Once user upload design data, statistics of the design data used as criteria to let user do probability based analysis. sample sizes between classes might be balanced and sample size should be good enough. Once user upload design data, statistics of the design data used as criteria to let user do probability based analysis.
-This statistics and warnings appears after uploading design file. If the sample sizes are imbalanced the design file will be used only to select sample for KO/OE.+This statistics and warnings appears after uploading design file. If the sample sizes are imbalanced the design file will be used only to select sample for KO/OE and only fold change of the module activities between conditions will be shown as results of this analysis.
  
  
in-silico_knockout/workflow_ko.1482461673.txt.gz · Last modified: 2017/05/24 14:33 (external edit)