Beta diversity
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11-Aug-2018 New paper describes octave plots for visualizing alpha diversity.

12-Jun-2018 New paper shows that one in five taxonomy annotations in SILVA and Greengenes are wrong.

18-Apr-2018 New paper shows that taxonomy prediction accuracy is <50% for V4 sequences.

05-Oct-2017 PeerJ paper shows low accuracy of closed- and open-ref. QIIME OTUs.

22-Sep-2017 New paper shows 97% threshold is wrong, OTUs should be 99% full-length 16S, 100% for V4.

24-Nov-2016
UPARSE tutorial video posted on YouTube. Make OTUs from MiSeq reads.

 

USEARCH v11

Beta diversity


ImageSee also
 
Diversity analysis
  Beta diversity metrics
  beta_div command
  Interpreting diversity metrics
  Recommended alpha and beta metrics
  Comparing alpha diversity between groups
  Statistical significance of diversity differences

Beta diversity compares two sample. Usually, this is done by calculating a number that indicates the similarity or difference between the samples. Often, but not always, the number is in the range zero to one.

The pair-wise comparisons for a set of samples can be presented in a distance matrix.

In usearch, beta diversities are always difference measures, not similarity measures, so increasing values indicate lower similarity and increasing distance. For distance measures D that ranges between zero and one there is always an equivalent similarity measure S defined by S = 1 - D, for example (Jaccard similarity) = 1 - (Jaccard distance). You can easily convert between distance and similarity measures in a spreadsheet program such as Excel.

Samples can be clustered to bring similar samples together, producing a tree, as shown in the figure above. In usearch, this can be done using the beta_div command, which automatically generates sample trees, or by using the cluster_aggd command to cluster a distance matrix generated by beta_div or by third-party software.