MAINT: Tutorials use core-metrics-phylogenetic command (#205)

source/tutorials/atacama-soils.rst

@@ -131,7 +131,7 @@ Questions to guide data analysis
 
 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`` command?
+#. 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.
 

source/tutorials/fmt.rst

@@ -117,7 +117,7 @@ Next, we'll generate a summary of the merged ``FeatureTable[Frequency]`` artifac
      --m-sample-metadata-file sample-metadata.tsv
 
 .. question::
-   Based on the information in ``table.qzv``, what value will you choose for the ``--p-sampling-depth`` parameter when you run ``qiime diversity core-metrics``?
+   Based on the information in ``table.qzv``, what value will you choose for the ``--p-sampling-depth`` parameter when you run ``qiime diversity core-metrics-phylogenetic``?
 
 .. question::
    Generate summaries of the tables for the individual runs of ``qiime dada2 denoise-single``. How many features were defined in the first run? How many features were defined in the second run? How do these numbers compare to total number of features after merging?

source/tutorials/moving-pictures.rst

@@ -223,7 +223,7 @@ The FastTree program creates an unrooted tree, so in the final step in this sect
 Alpha and beta diversity analysis
 ---------------------------------
 
-QIIME 2's diversity analyses are available through the ``q2-diversity`` plugin, which supports computing alpha and beta diversity metrics, applying related statistical tests, and generating interactive visualizations. We'll first apply the ``core-metrics`` method, which rarefies a ``FeatureTable[Frequency]`` to a user-specified depth, and then computes a series of alpha and beta diversity metrics. The metrics computed by default are:
+QIIME 2's diversity analyses are available through the ``q2-diversity`` plugin, which supports computing alpha and beta diversity metrics, applying related statistical tests, and generating interactive visualizations. We'll first apply the ``core-metrics-phylogenetic`` method, which rarefies a ``FeatureTable[Frequency]`` to a user-specified depth, and then computes a series of alpha and beta diversity metrics. The metrics computed by default are:
 
 * Alpha diversity
 
@@ -242,17 +242,17 @@ QIIME 2's diversity analyses are available through the ``q2-diversity`` plugin,
 The only parameter that needs to be provided to this script is ``--p-sampling-depth``, which is the even sampling (i.e. rarefaction) depth. Because most diversity metrics are sensitive to different sampling depths across different samples, this script will randomly subsample the counts from each sample to the value provided for this parameter. For example, if you provide ``--p-sampling-depth 500``, this step will subsample the counts in each sample without replacement so that each sample in the resulting table has a total count of 500. If the total count for any sample(s) are smaller than this value, those samples will be dropped from the diversity analysis. Choosing this value is tricky. We recommend making your choice by reviewing the information presented in the ``table.qzv`` file that was created above and choosing a value that is as high as possible (so you retain more sequences per sample) while excluding as few samples as possible.
 
 .. question::
-   View the ``table.qzv`` QIIME 2 artifact, and in particular the *Interactive Sample Detail* tab in that visualization. What value would you choose to pass for ``--p-sampling-depth``? How many samples will be excluded from your analysis based on this choice? How many total sequences will you be analyzing in the ``core-metrics`` command?
+   View the ``table.qzv`` QIIME 2 artifact, and in particular the *Interactive Sample Detail* tab in that visualization. What value would you choose to pass for ``--p-sampling-depth``? How many samples will be excluded from your analysis based on this choice? How many total sequences will you be analyzing in the ``core-metrics-phylogenetic`` command?
 
 .. command-block::
 
-   qiime diversity core-metrics \
+   qiime diversity core-metrics-phylogenetic \
      --i-phylogeny rooted-tree.qza \
      --i-table table.qza \
      --p-sampling-depth 1109 \
      --output-dir core-metrics-results
 
-Here we set the ``--p-sampling-depth`` parameter to 1109. This value was chosen based on the number of sequences in the ``L3S341`` sample because it's close to the number of sequences in the next few samples that have higher sequence counts, and because it is considerably higher (relatively) than the number of sequences in the one sample that has fewer sequences. This will allow us to retain most of our samples. The one sample that has fewer sequences will be dropped from the ``core-metrics`` analyses and anything that uses these results.
+Here we set the ``--p-sampling-depth`` parameter to 1109. This value was chosen based on the number of sequences in the ``L3S341`` sample because it's close to the number of sequences in the next few samples that have higher sequence counts, and because it is considerably higher (relatively) than the number of sequences in the one sample that has fewer sequences. This will allow us to retain most of our samples. The one sample that has fewer sequences will be dropped from the ``core-metrics-phylogenetic`` analyses and anything that uses these results.
 
 .. note:: The sampling depth of 1109 was chosen based on the DADA2 feature table summary. If you are using a Deblur feature table rather than a DADA2 feature table, you might want to choose a different even sampling depth. Apply the logic from the previous paragraph to help you choose an even sampling depth.
 
@@ -305,7 +305,7 @@ Next we'll analyze sample composition in the context of discrete metadata using
 
 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.
 
-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. PCoA is run as part of the ``core-metrics`` command, so we can generate these plots for unweighted UniFrac and Bray-Curtis as follows. The ``--p-custom-axis`` parameter that we pass here is very useful for exploring temporal data. The resulting plot will contain axes for principal coordinate 1, principal coordinate 2, and days since the experiment start. This is useful for exploring how the samples change over time.
+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. PCoA is run as part of the ``core-metrics-phylogenetic`` command, so we can generate these plots for unweighted UniFrac and Bray-Curtis as follows. The ``--p-custom-axis`` parameter that we pass here is very useful for exploring temporal data. The resulting plot will contain axes for principal coordinate 1, principal coordinate 2, and days since the experiment start. This is useful for exploring how the samples change over time.
 
 .. command-block::