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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

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., 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.

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Version

Install

install.packages('CTD')

Monthly Downloads

220

Version

0.99.8

License

MIT + file LICENSE

Maintainer

Lillian Thistlethwaite

Last Published

October 16th, 2020

Functions in CTD (0.99.8)

Thistlethwaite2020

Thistlethwaite et al. (2020)
data.zscoreData

Z-transform available data
Miller2015

Miller et al. (2015)
cohorts_coded

Disease cohorts with coded identifiers
graph.connectToExt

Connect a node to its unvisited "extended" neighbors
data.surrogateProfiles

Generate surrogate profiles
data.combineData

Combine datasets
data.imputeData

Impute missing values
graph.diffuseP1

Diffuse Probability P1 from a starting node
graph.diffusionSnapShot

Capture the current state of probability diffusion
Wangler2017

Wangler et al. (2017)
mle.getPtDist

CTDncd: A network-based distance metric.
stat.fishersMethod

Fisher's Combined P-value
mle.getPtBSbyK

Generate patient-specific bitstrings
mle.getMinPtDistance

Get minimum patient distances
singleNode.getNodeRanksN

Generate single-node node rankings ("fixed" walk)
stat.entropyFunction

Entropy of a bit-string
mle.getEncodingLength

Minimum encoding length
graph.netWalkSnapShot

Capture the current location of a network walker
multiNode.getNodeRanks

Generate multi-node node rankings ("adaptive" walk)
stat.getDirSim

DirSim: The Jaccard distance with directionality incorporated.
graph.naivePruning

Network pruning for disease-specific network determination