Sparse Cooccurnce Network Investigation for Compositional data Pronounced 'scenic'.
NOTE: SCNIC was recently updated to be python 3 only, old installations in python 2 only environments will not be functional
SCNIC is a package for the generation and analysis of cooccurence networks with compositional data. Data generated by many next gen sequencing experiments is compositional (is a subsampling of the total community) which violates assumptions of typical cooccurence network analysis techniques. 16S sequencing data is often very compositional in nature so methods such as SparCC (https://bitbucket.org/yonatanf/sparcc) have been developed for studying correlations microbiome data. SCNIC is designed with compositional data in mind and so provides multiple correlation measures including SparCC.
Running SCNIC is possible via two different methods. SCNIC is packaged with scripts to allow running it on the command line but also is avaliable as a Qiime2 plugin (https://www.github.com/shafferm/q2-SCNIC). Either method is valid but usage of the Qiime2 plugin provides easier access when working within the Qiime2 ecosystem.
The 'within' method takes as input BIOM formatted files (http://biom-format.org/) and forms cooccurence networks using a user specified correlation metric.
From the correlation network generated as part of the within step, SCNIC finds modules of cooccuring observations by finding groups of observations which all have a minimum pairwise correlation value. Modules are summarized and a new biom table with observations contained in modules collapsed into single observations are returned. This biom table along with a list of modules and their contents are output. A gml file of the network that can be opened using network visualization tools such as cytoscape (http://www.cytoscape.org/) is created which contains all observation metadata provided in the input biom file as well as module information. Please be aware that the networks output by this analysis will only include positive correlations as only positive correlations are used in module finding and summarization.
The 'between' method takes two biom tables as input and calculates all pairwise correlations between the tables using a selection of correlation metrics. A gml correlation network is output as well as a file containing statistics and p-values of all correlations.
To download the latest release from PyPI install using this command:
pip install SCNIC
To download the lastest changes to the repository use the following commands:
git clone https://github.com/shafferm/SCNIC.git
cd SCNIC/
python setup.py install
NOTE: This latest code may not be functional and should only be used if you want to play around with the code this is based on.
SCNIC_analysis.py within -i example_table.biom -o within_output/ -m sparcc
SCNIC_analysis.py modules -i within_output/correls.txt -o modules_output/ --min_r .35 --table example_table.biom
NOTE: We use a minimum R value of .3 when running SparCC with 16S data as a computationally demanding bootstrapping procedure must be run to determine p-values. We have run SparCC with 1 million bootstraps on a variety of datasets and found that a R value of between .3 and .35 will always return FDR adjusted p-values less than .05 and .1 respectively.
SCNIC_analysis.py between -1 example_table1.biom -2 example_table2.biom -o output_folder/ -m spearman --min_p .05