====== What can do Metabolizer for you ======


Metabolizer integrates three different pathway tools:
  * **Activity** allows you to see how module activity changes in different conditions.
  * **Knockout** allows you to simulate knockouts or over-expressions of one or several genes or the effect of drugs in metabolic module genes.
  * **Prediction** allows you to train a prediction model and test it with different data.

Metabolizer is able to integrate RNA-Seq and microarray data to produce an accurate result.



===== Activity =====

Metabolizer-Activity allows you to compute activity (overall flux) of each metabolic module and each one of your data samples. 
Therefore, it allows you to **compare** the activity values:
  * Between two groups, in order to see which modules are changed and how

In order to check how to use these options please see [[differential_signaling|Activity]].


===== Knockout =====
Metabolizer-Knockout allows you to modify the expression value of a set of genes and then check the effect of this change at the level of module activity. In this way, you can simulate effect of knockouts (KO) and over-expressions of one or several genes or the effect of drug(s) in the activity of metabolic modules.
The simulations can be done:
  * Using single sample: This option calculates fold-change of module activity between after and before KO/OE.
  * Using multiple samples: This option uses one selected sample to calculate effect of in-silico intervention of gene expression on module activity. Rest of the samples are used to train a prediction model. Metabolizer uses this trained model to calculate class probability of selected sample including before and after intervention.
  * User can use the options above by selecting single/multiple genes or drug(s) on interactive pathway panel. Metabolizer also allows to auto-KOs. Auto-KOs option calculates effect of simple KOs using all module genes by one by.   

In order to check how to use these options please see [[in-silico_knockout:over_expression| Knockout]].
===== Prediction =====

Metabolizer allows you to train, download and test a prediction model for your dataset using different machine learning algorithms. Prediction can be done using following methods; [[https://en.wikipedia.org/wiki/Support_vector_machine|SVM]], [[https://en.wikipedia.org/wiki/Random_forest|Random Forest]].
The model can be trained and test:
  * Using different groups (2 and more groups) of samples.

In order to check how to use these options please see [[prediction|Prediction]].