# Fisher Exact Test

Applies the Fisher Exact presence/absence test to a contingency table built based on the two selected pairwise categorical metadata values\
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This test relies on the fisher.test function in the R stats package [here](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/fisher.test.html)

![](https://2045057293-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M32ms-dWLPjFImgSINF%2F-M3AJIQ0Z4h584wNKpip%2F-M3AKREoCiQS7gKBZlx5%2Fimage.png?alt=media\&token=37d56e4a-97a3-47d9-be11-d65c0e5f0d4a)



### Used For

* Selecting OTUs or taxonomic groups ("OTU signature") that have a significant difference between two or more sample groups according to a presence/absence Fisher Exact test

### Feature Selection Parameters

#### Taxonomic Level

The taxonomic level to aggregate the OTUs at. The OTUs will be grouped together (by summing the OTU values) at the selected taxonomic level before the analysis is applied.

#### Categorical Variable

Create comparative sample groups based on categorical variables uploaded in the metadata file.&#x20;

Optionally create a categorical variable from a quantitative variable by using the Quantile Range feature on the Projects home page.&#x20;

#### Pairwise Comparison Variable

The two unique values for the selected Categorical Variable field that will be used to build a contingency table.

#### Min Presence Threshold

In the Fisher Exact test, this parameter controls what constitutes whether a taxonomic item is 'present' in a given sample.&#x20;

By default, this is zero which means that a taxonomic item is present for a sample if it has a non-zero value for the sample. If this value was 10, this means that a taxonomic item must have a count of at least 10 for it to be considered present.

#### P-Value Threshold

Only OTUs or taxonomic groups whose resultant p-value was less than this threshold will be displayed

#### Fix Training Set Between Changes

Indicate whether the training set should remain the same every time a parameter is changed and the model is retrained.

#### Dataset Training Proportion

Define the proportion of the data that should be randomly picked to form a training dataset.&#x20;

{% hint style="info" %}
You can set this value to be 1.0 if you don't plan on evaluating with a test dataset.
{% endhint %}

####

### Interactive Elements

* Link back to boxplots

### Additional Features

* **Save Snapshot**: Save the results to the experiment notebook
* **Download**: Downloads the results as a CSV file
* **Share**: Creates a shareable link that allows you to share the results with others
