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Univariate feature selection (numeric values)
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.
- 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?
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.
Specify which variable you want to select for:
- OTUs or taxonomic groups ("OTU signature")
- Gene expression
- Quantitative metadata
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)
Only OTUs or taxonomic groups whose resultant p-value was less than this threshold will be displayed
Indicate whether the training set should remain the same every time a parameter is changed and the model is retrained.
Define the proportion of the data that should be randomly picked to form a training dataset.
You can set this value to be 1.0 if you don't plan on evaluating with a test dataset.
- Link back to scatterplots
- 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