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Our novel network-based approach, CTD, “connects the dots” between metabolite perturbations observed in individual metabolomics profiles and a given disease state by calculating how connected those metabolites are in the context of a disease-specific network.
We are now a CRAN package! Install on R 4.0+ with install.packages(“CTD”).
Alternatively, particularly if you have an earlier version of R installed, you can install using devtools: require(devtools) install_github(“BRL-BCM/CTD”).
Located in /inst/doc/CTD_Lab-Exercise.Rmd. It will take you across all the stages in the analysis pipeline, including: 1. Background knowledge graph generation. 2. The encoding algorithm: including generating node permutations using a network walker, converting node permutations into bitstrings, and calculating the minimum encoding length between k codewords. 3. Calculate the probability of a node subset based on the encoding length. 4. Calculate similarity between two node subsets, using a metric based on mutual information.
Thistlethwaite L.R., Petrosyan V., Li X., Miller M.J., Elsea S.H., Milosavljevic A. (2021). CTD: an information-theoretic method to interpret multivariate perturbations in the context of graphical models with applications in metabolomics and transcriptomics. Plos Comput Biol, 17(1):e1008550. https://doi.org/10.1371/journal.pcbi.1008550.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.