human_microbiome — Human microbiome multiplex network (2014)

Description

A multiplex network of microbial taxa across 18 sites of the human body, assembled by M. De Domenico and J. Biamonte from the data of Ding and Schloss (2014), who analyzed the 16S rRNA gene sequences of the bacterial communities sampled from healthy adults by the Human Microbiome Project, and found that the communities of each body site fall into a few distinct community types. Nodes are 305 operational taxonomic units (OTUs, groups of similar sequences that stand for bacterial taxa), and each layer corresponds to a body site. An undirected edge in a layer connects two taxa that are associated (interact) in the microbial communities of that body site; how the associations were inferred from the abundance data is not documented by the authors. There are 4,433 edges, at most one per pair of taxa and layer.

The layers are: Anterior_nares (55 edges), Buccal_mucosa (114), Hard_palate (241), Keratinized_gingiva (46), L_Antecubital_fossa (524), L_Retroauricular_crease (63), Mid_vagina (81), Palatine_Tonsils (483), Posterior_fornix (33), R_Antecubital_fossa (335), R_Retroauricular_crease (49), Saliva (324), Stool (244), Subgingival_plaque (590), Supragingival_plaque (359), Throat (442), Tongue_dorsum (324) and Vaginal_introitus (126).

The taxa are identified only by the last two levels of their taxonomic classification, so several nodes share the same label (e.g. 23 nodes are labeled Clostridia(100);Clostridiales(100);).

The network has the following properties:

Nodes: name and nodeLabel, the last two levels of the taxonomic classification of the OTU, each followed by the confidence of the classification in percent in parentheses (e.g. Prevotellaceae(100);Prevotella(100);).
Edges: layer, the body site (numbered from 1 to 18 in the order listed above; the names are stored in the graph property layer_key); weight, which is always 1.

Tags

Biological Multilayer Unweighted

Citation

T. Ding and P. D. Schloss, "Dynamics and associations of microbial community types across the human body." Nature 509, 357-360 (2014) 10.1038/nature13178 Sci-Hub
M. De Domenico and J. Biamonte, "Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison." Physical Review X 6, 041062 (2016) 10.1103/PhysRevX.6.041062 Sci-Hub

Upstream URL OK

https://manliodedomenico.com/data.php

Upstream license

Open Data Commons Open Database License (ODbL), https://opendatacommons.org/licenses/odbl/

Networks

Tip: hover over a column header for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
human_microbiome	305	4,433	29.07	39.98	28.34	23.93	0.49	0.65	10	0.95	Undirected	Unipartite	name nodeLabel	layer weight	13 KiB	27 KiB	24 KiB	20 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.