CoNet offers a series of features that distinguish it from other network inference tools, such as its support for object groups. This feature allows a user to assign objects to different groups (
e.g. metabolites and enzymes). Relationships can then be inferred only between different object types (resulting in a bipartite network) or only within the same object type. CoNet's treatment of two input matrices is built upon this feature.
Furthermore, CoNet can handle row metadata, which allows for instance to infer links between objects at different hierarchical levels (
e.g. between order Lactobacillales and genus Ureaplasma) while preventing links between different levels of the same hierarchy (e.g. Lactobacillales and Lactobacillaceae). CoNet can also read in sample metadata such as temperature or oxygen concentration. When sample metadata are provided, associations among metadata items and between taxa and metadata items are inferred in addition to the taxon associations. Metadata are then represented as additional nodes in the resulting network. In addition, CoNet recognizes abundance tables generated from biom files (
McDonald
et al., 2012 ) and, in its Cytoscape 3.× version, reads biom files in HDF5 format directly, using the BiomIO Java library (
Ladau ). Taxonomic lineages in biom files or biom-derived tables are automatically parsed and displayed as node attributes of the resulting network. For instance, the lineage "k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactobacillus; s_Lactobacillus acidophilus" of an operating taxonomic unit with identifier 12 would create a kingdom, phylum, class, order, family, genus and species attribute in the node property table for node OTU-12, filled with the corresponding values from the lineage. CoNet also computes a node's total edge number as well as the number of positive and negative edges, the total row sum and the number of samples in which the object was observed (e.g. was different from zero or a missing value).
To ease the selection of suitable preprocessing steps, CoNet can display input matrix properties and recommendations based on them. Importantly, CoNet can also handle missing values, by omitting sample pairs with missing values from the association strength calculation. Finally, CoNet supports a few input and output network formats absent in Cytoscape, including adjacency matrices (import), dot (the format of GraphViz (
http://www.graphviz.org/ )) and VisML (VisANT's format (
Hu
et al., 2013 )) (both for export).
e.g. metabolites and enzymes). Relationships can then be inferred only between different object types (resulting in a bipartite network) or only within the same object type. CoNet's treatment of two input matrices is built upon this feature.
Furthermore, CoNet can handle row metadata, which allows for instance to infer links between objects at different hierarchical levels (
e.g. between order Lactobacillales and genus Ureaplasma) while preventing links between different levels of the same hierarchy (e.g. Lactobacillales and Lactobacillaceae). CoNet can also read in sample metadata such as temperature or oxygen concentration. When sample metadata are provided, associations among metadata items and between taxa and metadata items are inferred in addition to the taxon associations. Metadata are then represented as additional nodes in the resulting network. In addition, CoNet recognizes abundance tables generated from biom files (
et al., 2012
Ladau ). Taxonomic lineages in biom files or biom-derived tables are automatically parsed and displayed as node attributes of the resulting network. For instance, the lineage "k__Bacteria; p__Firmicutes; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactobacillus; s_Lactobacillus acidophilus" of an operating taxonomic unit with identifier 12 would create a kingdom, phylum, class, order, family, genus and species attribute in the node property table for node OTU-12, filled with the corresponding values from the lineage. CoNet also computes a node's total edge number as well as the number of positive and negative edges, the total row sum and the number of samples in which the object was observed (e.g. was different from zero or a missing value).
To ease the selection of suitable preprocessing steps, CoNet can display input matrix properties and recommendations based on them. Importantly, CoNet can also handle missing values, by omitting sample pairs with missing values from the association strength calculation. Finally, CoNet supports a few input and output network formats absent in Cytoscape, including adjacency matrices (import), dot (the format of GraphViz (
et al., 2013
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