CTD: an information-theoretic method to interpret multivariate perturbations in the context of graphical models with applications in metabolomics and transcriptomics

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.

Using CTD in R.

Installation

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”).

Look at the package Rmd vignette.

Located in /vignette/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.

References

Thistlethwaite L.R., Petrosyan V., Li X., Miller M.J., Elsea S.H., Milosavljevic A. (2020). CTD: an information-theoretic method to interpret multivariate perturbations in the context of graphical models with applications in metabolomics and transcriptomics. Manuscript in review.