Make a figure visualizing diversity (18S) and flux over time. See the figure in slide 7 here. For this, you can either merge the alpha-diversity results from the multiple runs, or re-calculate them from the merged table (use rarefied table if alpha-diversity metric is 'observed features'):
Command line:
qiime diversity alpha \
--i-table merged_table.qza \
--p-metric shannon \
--o-alpha-diversity merged_alpha_shannon.qza
qiime diversity alpha \
--i-table merged_table.qza \
--p-metric observed_features \
--o-alpha-diversity merged_alpha_observed_features.qza
Python:
adiv_vector = Artifact.load('merged_alpha_shannon.qza')
df_adiv = adiv_vector.view(pd.Series)
df_adiv_md = pd.merge(df_md[df_md['ctd_bottle_no'] != 'not applicable'], df_adiv, left_index=True, right_index=True)
sns.boxplot(data=df_adiv_md, y='shannon_entropy', x='line_id', hue='depth_category')
plt.xticks(rotation=45)
plt.savefig('shannon_entropy_boxplot_line_depth.pdf', bbox_inches='tight');
(similar code for observed features)
To overlay the flux data, you can add an additional plot to the same axes.
Make a figure visualizing diversity (18S) and flux over time. See the figure in slide 7 here. For this, you can either merge the alpha-diversity results from the multiple runs, or re-calculate them from the merged table (use rarefied table if alpha-diversity metric is 'observed features'):
Command line:
Python:
(similar code for observed features)
To overlay the flux data, you can add an additional plot to the same axes.