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.

Using CTD in Google Colab.

Click through the Google Colab Notebook, even before installing CTD.

Open /Colab_CTD_PLOSCB.ipynb in Google Colab to reproduce some results from Thistlethwaite et al. (2020) step by step. This does not require CTD local installation. You will:

1. Visualize individual or average cohort metabolomics profiles on comprehensive or individual pathway maps from MetaboLync, curated by Metabolon.
2. Estimate the probability and significance of a metabolite set against different disease-specific network contexts using CTD.
3. Use CTD as feature selection method and a covariate in Partial Least Square (PLS) regression.

References

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.