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  • Mian Overview
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  • Tool Parameters and Filters
  • Boxplots
  • Barplot (Composition)
  • Donut (Composition)
  • Scatterplot (Correlation)
  • Heatmap (Correlation)
  • Heatmap (Composition)
  • Correlation Network
  • Rarefaction Curves
  • Taxonomic Tree View
  • Table
  • Alpha Diversity
  • Beta Diversity
  • NMDS
  • PCoA
  • Boruta (Feature Selection)
  • Elastic Net Classification
  • Elastic Net Regression
  • Fisher Exact Test
  • Differential Selection
  • Correlations Selection
  • Linear Regressor
  • Linear Classifier
  • Random Forest Classifier
  • Deep Learning
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  • Used For
  • Visualization Parameters
  • Interactive Elements
  • Additional Features

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NMDS

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Last updated 5 years ago

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Performs non-metric multidimensional scaling at the selected taxonomic level NMDS tries to represent the original data in a 2D reduced dimensional space. This analysis utilizes the

Used For

  • Visualizing the similarity between different samples (collapsing information into two components)

Visualization 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.

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

Distance Metric

Choose from one or multiple ways to calculate the distance between samples

Interactive Elements

  • Hover over each point to determine its sample ID and its NMDS component values

  • Zoom and pan

Additional Features

  • Save Snapshot: Save the visualization to the experiment notebook

  • Download: Downloads the visualization as a PNG file

  • Share: Creates a shareable link that allows you to share the visualization with others

scikit-learn package