The package can implement the graph-based reconstruction and
analyses of the KEGG pathways.
The package can implement the graph-based reconstruction
and analysis of the KEGG pathways. (1) Our system provides many
strategies of converting pathways to graph models. Ten
functions related to conversion from pathways to graphs are
developed. Furthermore, the combinations of these functions can
get many combined conversion strategies of pathway graphs (>
20). The system can also support topology-based pathway
analysis of molecule sets. (2) The iSubpathwayMiner can support
the annotation and identification of entire pathways based on
molecule (gene and/or metabolite) sets. (3) The
iSubpathwayMiner provides the k-clique method for
identification of metabolic subpathways based on gene sets. (4)
The iSubpathwayMiner provides the Subpathway-GM method for
identification of metabolic subpathways based on gene and
metabolite sets (new!).
initialize.R visualize.R go2go.R pathway.R metabolic.R
non-metabolic.R metabolic_non-metabolic.R annotation.R
topologicalanalysis.R combindedNetwork.R sample.R k-clique.R
SubpathwayGM.R

Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, in preparation).

Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).

Some functions for performing ICA, MICA, and Multilinear ICA are implemented.
ICA, MICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively.
For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.