QIIME2 VI: Diversity metrics and estimations
The first step in hypothesis testing in microbial ecology is typically to look at within- (alpha) and between-sample (beta) diversity. We can calculate diversity metrics, apply appropriate statistical tests, and visualize the data using the q2-diversity plugin.
- Author(s):
- Release: 0.1
- License: MIT
- UniqueID: 300b5a7d-f763-486a-a8ea-87ea84571f19
QIIME2 workflows
Available workflows
- III-V Downstream analyses: III) reconstruct a taxonomy for diversity analysis, IV) rarefaction analysis, V) taxonomic analysis.
- VI: Computation of diversity metrics and estimations
Analogous to the procedures described in the Parkinson’s Mouse Tutorial: https://docs.qiime2.org/2024.5/tutorials/pd-mice/
Inputs
The two workflows have two inputs in common
- Feature table: Count data
- Metadata: Metadata table
and the following extra inputs
III-V
- Representative sequences: Representative (ASV) sequences
- Minimum depth: Lower limit of the sampling depth for the alpha rarefaction analysis
- Maximum depth: Upper limit of the sampling depth for the alpha rarefaction analysis
- SEPP fragment insertion reference: used for the reconstruction of the phylogenetic tree
- Taxonomic classifier: The classifier to assign taxonomic information to the ASVs
VI:
Sampling depth: For the metric calculation (should be based on the rarefaction analysis done in IV) Target metadata parameter: that should be used for beta diversity calculations Rooted Tree: for instance the tree computed in III
Processing
III-V
- Phylogenetic tree generation using
qiime2 fragment-insertion sepp
- Alpha rarefaction analysis using
qiime2 diversity alpha-rarefaction
- Taxonomic classification using
qiime2 feature-classifier classify-sklearn
and compute barplot and tabular output
VI:
- compute alpha and beta diversity metrics using
qiime2 diversity core-metrics-phylogenetic
- organize these metrics in 4 collections:
- Distance matrix collection (weighted and unweighted unifrac, jaccard and bray curtis)
- PCoA collection (same as the distance matrices)
- Emperor plot collection (same as the distance matrices)
- Richness and evenness collection (rarefied table, faith pd vector observed features vector, shannon vector, evenness vector)
- get visualization for alpha diversity:
- Pielou's eveness
- Observed features
- Shannons diversity index
- get visualization for beta diversity
- Jaccard distance matrix
- Bray curtis distance matrix
- Unifrac distance metrix
Outputs
III-V:
- Phylogenetic tree
- Rarefaction curve
- Taxonomic classification (as qza, barplot and table)
VI:
- Four collections containing: distance matrix, PCoA, Emperor plots, Richness and evenness
- Visualization for alpha diversity: Pielou's eveness, Observed features, Shannons diversity index
- Visualization for beta diversity: Jaccard, Bray curtis, Unifrac