Description Usage Arguments Value Author(s) References Examples
This function uses co-occurence pattern analysis to identify co-occuring features/taxa in community data under specified environmental conditions. Co-occurence is measured as positive correlation whose threshold(s) can be specified as indicated in arguments section. Amongst these features, pairwise co-occurences which are outstanding within sub communities are detected. p-values generated during pairwise correlations are adjusted for multiple comparisons by false discovery rate. The network statistics used to assign importance of taxa/features include betweenness, closeness, and eigenvector centrality.
1 2 3 | co_occurence_network(physeq, qval_threshold = 0.05, grouping_column,
rhos = c(-0.75, -0.5, 0.5, 0.75), select.condition = NULL,
method = "cor", filename = NULL, ...)
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qval_threshold |
Cut off for "fdr" adjusted p-values. |
grouping_column |
(Required). Character string specifying name of a categorical variable that is preffered for grouping the information. information. |
rhos |
(required). A vector specifying thresholds for correlation between co-occuring pairs of features. |
select.condition |
(optional). A character string speifying name of a desired condition/group. If not supplied, co-occuence analysis is performed amongst all conditions present in the grouping variable. |
physeq(Required). |
A |
method. |
A character string for the correlation method to be used; options include: "cor" and "bicor" |
Files of visual representation of the network showing subcommunities (identified by colors), network statistics and file containing pairwise corrrelations of taxa in under different conditions.
Alfred Ssekagiri assekagiri@gmail.com, Umer Zeeshan Ijaz Umer.Ijaz@glasgow.ac.uk
http://userweb.eng.gla.ac.uk/umer.ijaz/, Umer Ijaz, 2015
[Ryan J. Williams, Adina Howe and Kirsten S. Hofmockel. Demonstrating microbial co-occurrence pattern analyses within and between ecosystems, Frontiers in Microbial Ecology, 5:358, 2014].
1 2 3 | data(pitlatrine)
physeq <- taxa_level(pitlatrine, "Phylum")
co_occr <- co_occurence_network(physeq, grouping_column = "Country", rhos = 0.35, select.condition = "V", scale.vertex.size=3, scale.edge.width=15)
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