README.md

CTD:: a method which interprets multivariate perturbations identified in molecular profiles by identifying highly connected nodes in disease-specific co-perturbation networks

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

In R, install the devtools package, and install CTD by 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. et al. (2019). CTD: a method which interprets multivariate perturbations observed in molecular profiles by identifying highly connected nodes in disease-specific co-perturbation networks. Manuscript submitted.



BRL-BCM/CTD documentation built on Feb. 7, 2020, 1:42 a.m.