# CTD v0.99.8

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## A Method for 'Connecting The Dots' in Weighted Graphs

A method for pattern discovery in weighted graphs. Two use cases are achieved: 1) Given a weighted graph and a subset of its nodes, do the nodes show significant connectedness? 2) Given a weighted graph and two subsets of its nodes, are the subsets close neighbors or distant?

## Readme

# 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:

- Background knowledge graph generation.
- 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.
- Calculate the probability of a node subset based on the encoding length.
- 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.

## Functions in CTD

Name | Description | |

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 | |

No Results! |

## Vignettes of CTD

Name | ||

images/probability_diffusion.png | ||

CTD_Lab-Exercise.Rmd | ||

No Results! |

## Last month downloads

## Details

Date | 2020-09-12 |

SystemRequirements | gmp (>=5.0) |

VignetteBuilder | knitr |

biocViews | BiomedicalInformatics, Metabolomics, SystemsBiology, GraphAndNetwork |

License | MIT + file LICENSE |

Encoding | UTF-8 |

LazyData | true |

RoxygenNote | 7.1.1 |

NeedsCompilation | no |

Packaged | 2020-10-08 18:52:06 UTC; lillian.rosa |

Repository | CRAN |

Date/Publication | 2020-10-16 14:00:09 UTC |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/CTD)](http://www.rdocumentation.org/packages/CTD)
```