# 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 /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.

## 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.