Package index • anansi

Association

weaveWeb() weaveKEGG(): Weave an AnansiWeb

anansi(): anansi: Annotation-based Analysis of Specific Interactions.

getGraph(<MultiFactor>) getGraph(<list>): Get a graph object out of an MultiFactor.

Visualisation

plotAnansi(): Bioc style plotting wrapper for anansi output

Classes

AnansiWeb() dimnames(<AnansiWeb>) dim(<AnansiWeb>) names(<AnansiWeb>) tableY(<AnansiWeb>) `tableY<-`(<AnansiWeb>) tableX(<AnansiWeb>) `tableX<-`(<AnansiWeb>) dictionary(<AnansiWeb>) `dictionary<-`(<AnansiWeb>) metadata(<AnansiWeb>) `metadata<-`(<AnansiWeb>) asMAE() as.list(<AnansiWeb>) as.data.frame(<AnansiWeb>) which(<AnansiWeb>) getFeaturePairs(<AnansiWeb>) mapply(<AnansiWeb>) show(<AnansiWeb>): AnansiWeb S4 container class

MultiFactor() asMultiFactor() dimnames(<MultiFactor>) dim(<MultiFactor>) names(<MultiFactor>) dictionary(<MultiFactor>) `dictionary<-`(<MultiFactor>) levels(<MultiFactor>) unfactor(<MultiFactor>) droplevels(<MultiFactor>) `[`(<MultiFactor>,<ANY>,<ANY>) `[<-`(<MultiFactor>,<ANY>,<ANY>,<list>) `[[`(<MultiFactor>,<ANY>) `[[<-`(<MultiFactor>,<ANY>,<ANY>) subset(<MultiFactor>) c(<MultiFactor>) as.list(<MultiFactor>) getEdgeList(<MultiFactor>) show(<MultiFactor>): MultiFactor S4 container class

anansiTale-class: An S4 class to contain all anansi stats results so that they can easily be extracted.

Datasets

randomWeb() randomMultiFactor() krebsDemoWeb(): Generate a random AnansiWeb or MultiFactor

FMT_data: Snippet of the CLR-transformed inferred functional data from the FMT Aging study.

FMT_data: Snippet of the CLR-transformed hippocampal metabolomics data from the FMT Aging study.

FMT_data: Snippet of the metadata from the FMT Aging study.

krebs: Simplified snippet of the Krebs cycle

ec2ko ec2cpd kegg_link(): Use linking data from the KEGG database.

Conversion utils

linkBiobakeryMap(): make a link data.frame for biobakery mapping files input

Internal utils

anansiCor(): Compute r-statistics for each featureY-featureX pair in the dictionary. Typically, the main anansi() function will run this for you.

anansiCorPvalue(): Compute r-statistics for each featureY-featureX pair in the dictionary. Typically, the main anansi() function will run this for you.

anansiCorTestByGroup(): Run correlations for all interacting metabolites and functions.

anansiDiffCor(): Run differential correlation analysis for all interacting metabolites and functions.

is.categorical(): Is character, factor or ordered factor

named_group_list(): Provide name and levels for categories for group terms

named_term_list(): Provide name and levels for categories