Differences

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--- what_can_do_hipathia_for_you [2016/12/20 05:22] – ccubuk
+++ what_can_do_hipathia_for_you [2017/05/24 14:33] (current) – external edit 127.0.0.1
@@ Line 1: / Line 1: @@
-====== What can do HiPathia for you ======
+====== 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 knock outs or over-expressions of one or several genes or the effect of a drug in metabolic module genes.
+  * **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.
@@ Line 13: / Line 13: @@
 ===== Activity =====
-Metabolizer-Activity allows you to compute a activity (overall flux) each metabolic module and each one of your data samples.
+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 module is changed and how
+  * 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]].
-In order to check how to use these options please see [[differential_signaling|Differential signaling]].
 ===== Knockout =====
-Metabolizer-Knockout allows you to modify the expression value of a set of genes and then check the effect that this change has at the level of module activity. In this way, you can simulate knock outs (KO) or over-expressions of one or several genes or the effect of drug(s) in the activity of metabolic modules.
+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.
+  * 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 manipulation 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 simulation.
+  * 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 below by selecting single/multiple genes or drug(s) on interactive pathway panel. Metabolizer also allows to auto-KOs. Selecting auto KOs calculates effect of simple KOs using all module genes by one by.
+  * 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/Linear_discriminant_analysis|LDA]], [[https://en.wikipedia.org/wiki/Support_vector_machine|SVM]], [[https://en.wikipedia.org/wiki/Random_forest|Random Forest]].
+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]].
+In order to check how to use these options please see [[prediction|Prediction]].