The provided scripts can be used to convert correlation and p-value matrices to an condensed edgelist. Following, the resulting edge list will be used to generate networks using the python package NetworkX. The edge list can be optional restricted to a given correlation treshold (-clim) and p-value threshold (-plim). Great tools for calculating correlation matrices (identifying co-occurence patterns), especially of microbiome communities are SparCC and FastSpar. Created networks are only a basic representation, advanced representations require adjustments of the code. The 'data' folder contains artificially generated correlation and p-value matrices for testing purposes.
The additional python packages below are required:
- numpy
- pandas
- matplotlib
- networkx
Following input is required to run the script:
| Argument | Type | Comment |
|---|---|---|
-cm |
float | tabular separated (.tsv) |
-pm |
float | tabular separated (.tsv) |
Optional:
| Argument | Type | Default |
|---|---|---|
-clim |
numeric | 0.7 |
-plim |
numeric | 0.05 |
-dlim |
numeric | 5 |
-out |
character | current working directory |
--prefix |
character | firstproject |
-cm correlation matrix
-pm p-value matrix
-clim Threshold of correlation matrix (Default: 0.7)
-plim Threshold of p-value matrix (Default: 0.05)
-dlim Threshold of outgoing connections (degree) of source nodes (Default: 5)
-out Path for generated output. If not provided, current working directory is used.
-pre Prefix used to name output folder and files (Default: corrnet)
python3 corrnet.py -cm ./data/random_correlation.tsv -pm ./data/random_p_value.tsv- adjancy list (columns: source, target, weight, pval, direction, color)
- network plot