q2-makarsa

q2-makarsa is a plugin to incorporate some functionality from the SpiecEasi and FlashWeave packages into the QIIME 2 environment together with additional network visualisation.

What is involved

QIIME2

QIIME 2 is a powerful, extensible, and decentralized microbiome analysis package with a focus on data and analysis transparency. QIIME 2 enables researchers to start an analysis with raw DNA sequence data and finish with publication-quality figures and statistical results.

SpiecEasi

SpiecEasi (Sparse InversE Covariance estimation for Ecological Association and Statistical Inference) is an R based package which allows the user to infer microbial ecological networks from compositional datasets typically generated from 16S amplicon sequencing.

FlashWeave

FlashWeave is a Julia based package which predicts ecological interactions between microbes from large-scale compositional abundance data (e.g., ASV or OTU tables constructed from sequencing data) through statistical co-occurrence or co-abundance. It reports direct associations, with adjustment for bystander effects and other confounders, and can furthermore integrate environmental or technical factors into the analysis of microbial systems.

Plug-in Features

q2-makarsa is at the $\alpha$ stage. In addition to wrapping the SpiecEasi and FlashWeave packages it provides a visualisation for generated networks. As development continues additional features will be listed here.

• All of SpiecEasi's features are exposed to the user except batch-mode execution (thread-level parallelism is supported)
• All of FlashWeave's learn_network features are exposed
• Visualisation features
  • Relationships between features (eg. ASVs, OTUs, MVs, taxa, peptides) are represented as ecological networks
  • The network is interactive and its overall size and shape on the screen can be manipulated manually.
  • An image of the network can be saved to the local device in PNG format.
  • Network nodes can be selected and information on which feature is represented by the node, statistics, and various centrality measures for the node will be displayed.
  • Network edges are colour coded for positive (blue) and negative (orange) correlations
  • Edge thickness is scaled according to statistics appropriate to each method with thicker edges indicating stronger connections between features.
  • Node size is scaled according user-selected statistics such as centrality measures.
  • Nodes are colour coded according to user-selected categories such as taxonomic classification
  • Synchronises settings across tabs meaning that if you have multiple tabs open in the visualisation, any changes you make to the settings (such as the scaling of edges or nodes) will be applied to all tabs at once.
  • Node attributes can be added via feature metadata (eg. taxonomy, DNA sequence, differential abundance scores)
  • If node attributes are taxonomic labels, visualisation offers colourings at any taxonomic level

Installation

q2-makarsa requires a working QIIME 2 environment, installed using conda. Please follow the "Natively installing QIIME 2" instructions. (If that link is outdated, please navigate there in the latest QIIME 2 docs.)

Make sure your conda environment is activated (as described in the QIIME 2 installation instructions), then install the dependencies:

conda install -c bioconda -c conda-forge r-spieceasi julia
julia -e 'using Pkg; Pkg.add(["FlashWeave", "ArgParse", "GraphIO"])'

In the same conda environment pip install from the q2-makarsa github repo:

pip install git+https://github.com/BenKaehler/q2-makarsa.git

Usage Examples

From within the conda environment create a working folder and move into it

mkdir plugin-example
cd plugin-example/

This folder will contain the QIIME 2 artefacts produced by q2-makarsa at the completion of each example.

Basic work flow

The sequencing data for this example is derived from the Sponge Microbiome Project. In particular, we will use data for the Suberitida order of sponges.

Download the data

https://github.com/ramellose/networktutorials/raw/master/Workshop%202021/sponges/Suberitida.biom

The next step is to import the BIOM file as a frequency FeatureTable within QIIME 2.

qiime tools import \
    --input-path Suberitida.biom \
    --type 'FeatureTable[Frequency]' \
    --input-format BIOMV210Format \
    --output-path sponge-feature-table.qza

The QIIME 2 artefact spongeFeatureTable.qza should exist in the working folder if this command was successful.

Accessing SpiecEasi

Now, we are ready to use q2-makarsa to access the SpiecEasi algorithms to infer the microbial network. The most minimal command to generate the network requires the name of artefact containing the FeatureTable and the name of the intended output artefact containing the inferred network.

qiime makarsa spiec-easi \
    --i-table sponge-feature-table.qza \
    --o-network sponge-net.qza

From the sponge-net.qza network artefact a visualisation can be created and then viewed

qiime makarsa visualise-network \
    --i-network sponge-net.qza \
    --o-visualization sponge-net.qzv

qiime tools view sponge-net.qzv

The network images should open in your default browser. Alternatively, you can upload sponge-net.qva to qiime2view. The network containing the largest number of members is in the tab labelled Group 1 , next largest network in the tab Group 2, and so on down. Trivial networks of two members and singletons are listed by feature in the Pairs and Singles tab respectively.

SpiecEasi Options

Several parameter options exist for qiime makarsa spiec-easi . For a full list of parameters and the defaults execute qiime makarsa spiec-easi --help. Some examples are below.

The algorithm utilised to infer the network can be set with -p-method parameter switch and one of 3 keywords:

1. glasso Graphical LASSO (default)
2. mb Neighbourhood selection or Meinshausen and Bühlmann method
3. slr Sparse and Low-Rank method

For example to infer the network from the example data using the MB method execute the command

qiime makarsa spiec-easi \ 
   --i-table sponge-feature-table.qza \ 
   --o-network sponge-net.qza \ 
   --p-method mb 

The remaining parameters relate to selection of the optimal penalty $\lambda$ in each method's lasso like optimization problem. The network inference algorithms search for the optimal $\lambda$ penalty where the complete graph and an empty graph are at the extremes of the search range. Essentially the process is finding a balance between network sparsity and least-squares fit.

The range of $\lambda$ values tested is between --p-lambda-min-ratio $\times\lambda{max}$ and $\lambda{max}$, where $\lambda_{max}$ is the theoretical upper bound on $\lambda$. This upper bound is $\max|S|$, the maximum absolute value in the data correlation matrix.

The lambda range is sampled logarithmically --p-nlambda times.

FlashWeave

Alternatively, we can use FlashWeave to infer the network. The commands are similar. Create the network.

qiime makarsa flashweave \
    --i-table sponge-feature-table.qza \
    --o-network sponge-fw-net.qza

Then generate the visualisation.

qiime makarsa visualise-network \
    --i-network sponge-fw-net.qza \
    --o-visualization sponge-fw-net.qzv

View the visualisation as usual

qiime tools view sponge-net.qzv

Community detection

Once a network graph is generated, this can be used to identify modules of co-occurring features. This is useful for, e.g., grouping these features for downstream analyses. For module detection, q2-makarsa employs the Louvain method.

qiime makarsa louvain-communities \
   --i-network sponge-net.qza \
   --o-community node-map.qza

Now you can colour your nodes by community.

qiime makarsa visualise-network \
    --i-network sponge-net.qza \
    --m-metadata-file node-map.qza \
    --o-visualization sponge-louvain-net.qzv

Alternatively you can view the resulting node map (showing which features belong to each module).

qiime metadata tabulate \
   --m-input-file node-map.qza \
   --o-visualization node-map.qzv

The node map can be input as feature metadata to other QIIME 2 actions. For example, the following action can be used to group the features in a feature table based on their community affiliation.

qiime feature-table group \
    --i-table sponge-feature-table.qza \
    --p-axis feature \
    --m-metadata-file node-map.qza \
    --m-metadata-column Community \
    --p-mode sum \
    --o-grouped-table grouped-table.qza