MAINT: Moving pictures updates (#404)

- DADA2 1.10
- Misc grammar

Author: ChrisKeefe

source/tutorials/moving-pictures.rst

@@ -246,7 +246,7 @@ QIIME 2's diversity analyses are available through the ``q2-diversity`` plugin,
   * unweighted UniFrac distance (a qualitative measure of community dissimilarity that incorporates phylogenetic relationships between the features)
   * weighted UniFrac distance (a quantitative measure of community dissimilarity that incorporates phylogenetic relationships between the features)
 
-An important 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.
+An important 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. Choose 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-phylogenetic`` command?
@@ -256,13 +256,13 @@ An important parameter that needs to be provided to this script is ``--p-samplin
    qiime diversity core-metrics-phylogenetic \
      --i-phylogeny rooted-tree.qza \
      --i-table table.qza \
-     --p-sampling-depth 1109 \
+     --p-sampling-depth 1103 \
      --m-metadata-file sample-metadata.tsv \
      --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-phylogenetic`` analyses and anything that uses these results.
+Here we set the ``--p-sampling-depth`` parameter to 1103. This value was chosen based on the number of sequences in the ``L3S313`` 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 samples that have fewer sequences. This will allow us to retain most of our samples. The three samples that have fewer sequences will be dropped from the ``core-metrics-phylogenetic`` analyses and anything that uses these results. It is worth noting that all three of these samples are "right palm" samples. Losing a disproportionate number of samples from one metadata category is not ideal. However, we are dropping a small enough number of samples here that this felt like the best compromise between total sequences analyzed and number of samples retained.
 
-.. 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.
+.. note:: The sampling depth of 1103 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.
 
 .. note:: In many Illumina runs you'll observe a few samples that have very low sequence counts. You will typically want to exclude those from the analysis by choosing a larger value for the sampling depth at this stage.
 
@@ -290,7 +290,7 @@ We'll first test for associations between categorical metadata columns and alpha
 
 In this data set, no continuous sample metadata columns (e.g., ``DaysSinceExperimentStart``) are correlated with alpha diversity, so we won't test for those associations here. If you're interested in performing those tests (for this data set, or for others), you can use the ``qiime diversity alpha-correlation`` command.
 
-Next we'll analyze sample composition in the context of categorical metadata using PERMANOVA (first described in `Anderson (2001)`_) using the ``beta-group-significance`` command. The following commands will test whether distances between samples within a group, such as samples from the same body site (e.g., gut), are more similar to each other then they are to samples from the other groups (e.g., tongue, left palm, and right palm). If you call this command with the ``--p-pairwise`` parameter, as we'll do here, it will also perform pairwise tests that will allow you to determine which specific pairs of groups (e.g., tongue and gut) differ from one another, if any. This command can be slow to run, especially when passing ``--p-pairwise``, since it is based on permutation tests. So, unlike the previous commands, we'll run this on specific columns of metadata that we're interested in exploring, rather than all metadata columns that it's applicable to. Here we'll apply this to our unweighted UniFrac distances, using two sample metadata columns, as follows.
+Next we'll analyze sample composition in the context of categorical metadata using PERMANOVA (first described in `Anderson (2001)`_) using the ``beta-group-significance`` command. The following commands will test whether distances between samples within a group, such as samples from the same body site (e.g., gut), are more similar to each other then they are to samples from the other groups (e.g., tongue, left palm, and right palm). If you call this command with the ``--p-pairwise`` parameter, as we'll do here, it will also perform pairwise tests that will allow you to determine which specific pairs of groups (e.g., tongue and gut) differ from one another, if any. This command can be slow to run, especially when passing ``--p-pairwise``, since it is based on permutation tests. So, unlike the previous commands, we'll run ``beta-group-significance`` on specific columns of metadata that we're interested in exploring, rather than all metadata columns to which it is applicable. Here we'll apply this to our unweighted UniFrac distances, using two sample metadata columns, as follows.
 
 .. command-block::