> For the complete documentation index, see [llms.txt](https://tbj128.gitbook.io/mian/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://tbj128.gitbook.io/mian/correlations-selection.md).

# Correlations Selection

Calculates the correlation between each taxonomic group/OTU and the selected numerical metadata variable. Taxonomic groups/OTUs that correlate statistically with the numerical metadata will be selected.

As an example, this is ideal for identifying taxonomic groups/OTUs that correlate with gene expression data.

![](/files/-M3AN7Bu5R0vt88RkM91)

### Used For

* Selecting OTUs or taxonomic groups ("OTU signature"), gene expression, or quantitative metadata that significantly correlate with other OTUs or taxonomic groups, gene expression, alpha diversity, or quantitative metadata
* Answers questions such as:
  * What genes are upregulated in response to disease severity?
  * What histological features are correlated with Staphylococcus expansion?
  * What OTUs are correlated with T-cell infiltration?

### 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.

#### Select...

Specify which variable you want to select for:

* OTUs or taxonomic groups ("OTU signature")
* Gene expression
* Quantitative metadata

#### By Correlating Against...

Specify which variable you want to correlate the selected variable against:

* OTUs or taxonomic groups (you must pick the specific OTU or taxonomic group)
* Gene expression (you must pick the specific gene)
* Alpha Diversity
* Quantitative metadata (you must pick the specific metadata)

#### 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.

{% 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 scatterplots

### 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


---

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