MAINT: update beta-correlation to mantel (#218)

qiime2 · Oct 25, 2017 · 26f6a8f · 26f6a8f
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diff --git a/source/tutorials/atacama-soils.rst b/source/tutorials/atacama-soils.rst
@@ -133,7 +133,7 @@ Use the following questions to guide your further analyses of these data data.
 
 #. What value would you choose to pass for ``--p-sampling-depth``? How many samples will be excluded from your analysis based on this choice? Approximately how many total sequences will you be analyzing in the ``core-metrics-phylogenetic`` command?
 
-#. What sample metadata or combinations of sample metadata are most strongly associated with the differences in microbial composition of the samples? Are these associations stronger with unweighted UniFrac or with Bray-Curtis? Based on what you know about these metrics, what does that difference suggest? For exploring associations between continuous metadata and sample composition, the commands ``qiime diversity beta-correlation`` and ``qiime diversity bioenv`` will be useful. These were not covered in the Moving Pictures tutorial, but you can learn about them by running them with the ``--help`` parameter.
+#. What sample metadata or combinations of sample metadata are most strongly associated with the differences in microbial composition of the samples? Are these associations stronger with unweighted UniFrac or with Bray-Curtis? Based on what you know about these metrics, what does that difference suggest? For exploring associations between continuous metadata and sample composition, the commands ``qiime metadata distance-matrix`` in combination with ``qiime diversity mantel`` and ``qiime diversity bioenv`` will be useful. These were not covered in the Moving Pictures tutorial, but you can learn about them by running them with the ``--help`` parameter.
 
 #. What do you conclude about the associations between continuous sample metadata and the richness and evenness of these samples? For exploring associations between continuous metadata and richness or evenness, the command ``qiime diversity alpha-correlation`` will be useful. This was not covered in the Moving Pictures tutorial, but you can learn about it by running it with the ``--help`` parameter.
 

diff --git a/source/tutorials/moving-pictures.rst b/source/tutorials/moving-pictures.rst
@@ -315,7 +315,7 @@ Next we'll analyze sample composition in the context of discrete metadata using
 .. question::
    Are the associations between subjects and differences in microbial composition statistically significant? How about body sites? What specific pairs of body sites are significantly different from each other?
 
-Again, none of the continuous sample metadata that we have for this data set are correlated with sample composition, so we won't test for those associations here. If you're interested in performing those tests, you can use the ``qiime diversity beta-correlation`` and ``qiime diversity bioenv`` commands.
+Again, none of the continuous sample metadata that we have for this data set are correlated with sample composition, so we won't test for those associations here. If you're interested in performing those tests, you can use the ``qiime metadata distance-matrix`` in combination with ``qiime diversity mantel`` and ``qiime diversity bioenv`` commands.
 
 Finally, ordination is a popular approach for exploring microbial community composition in the context of sample metadata. We can use the `Emperor`_ tool to explore principal coordinates (PCoA) plots in the context of sample metadata. While our ``core-metrics-phylogenetic`` command did already generate some Emperor plots, we want to pass an optional parameter, ``--p-custom-axis``, which is very useful for exploring time series data. The PCoA results that were used in ``core-metrics-phylogeny`` are also available, making it easy to generate new visualizations with Emperor. We will generate Emperor plots for unweighted UniFrac and Bray-Curtis so that the resulting plot will contain axes for principal coordinate 1, principal coordinate 2, and days since the experiment start. We will use that last axis to explore how these samples changed over time.